[ { "id": "2021.acl-long.24", "title": "A Bidirectional Transformer Based Alignment Model for Unsupervised Word Alignment", "track": "main", "status": "Long", "award": false, "abstract": "Word alignment and machine translation are two closely related tasks. Neural translation models, such as RNN-based and Transformer models, employ a target-to-source attention mechanism which can provide rough word alignments, but with a rather low accuracy. High-quality word alignment can help neural machine translation in many different ways, such as missing word detection, annotation transfer and lexicon injection. Existing methods for learning word alignment include statistical word aligners (e.g. GIZA++) and recently neural word alignment models. This paper presents a bidirectional Transformer based alignment (BTBA) model for unsupervised learning of the word alignment task. Our BTBA model predicts the current target word by attending the source context and both left-side and right-side target context to produce accurate target-to-source attention (alignment). We further fine-tune the target-to-source attention in the BTBA model to obtain better alignments using a full context based optimization method and self-supervised training. We test our method on three word alignment tasks and show that our method outperforms both previous neural word alignment approaches and the popular statistical word aligner GIZA++.", "author": "Jingyi Zhang; Josef van Genabith", "authorids": "/j/jingyi-zhang/; /j/josef-van-genabith/", "bibtex": "@inproceedings{zhang-van-genabith-2021-bidirectional,\n title = \"A Bidirectional Transformer Based Alignment Model for Unsupervised Word Alignment\",\n author = \"Zhang, Jingyi and\n van Genabith, Josef\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.24/\",\n doi = \"10.18653/v1/2021.acl-long.24\",\n pages = \"283--292\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.24.pdf", "site": "https://aclanthology.org/2021.acl-long.24/", "pdf_size": 500701, "gs_citation": 11, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=12487721309990084059&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 4, "aff": "German Research Center for Artificial Intelligence (DFKI), Saarland Informatics Campus, Saarbr\u00fccken, Germany + Department of Language Science and Technology, Saarland University, Saarland Informatics Campus, Saarbr\u00fccken, Germany; German Research Center for Artificial Intelligence (DFKI), Saarland Informatics Campus, Saarbr\u00fccken, Germany + Department of Language Science and Technology, Saarland University, Saarland Informatics Campus, Saarbr\u00fccken, Germany", "aff_domain": "dfki.de;dfki.de", "email": "dfki.de;dfki.de", "github": "", "project": "", "author_num": 2, "aff_unique_index": "0+1;0+1", "aff_unique_norm": "German Research Center for Artificial Intelligence;Saarland University", "aff_unique_dep": ";Department of Language Science and Technology", "aff_unique_url": "https://www.dFKI.de;https://www.uni-saarland.de", "aff_unique_abbr": "DFKI;Saarland U", "aff_campus_unique_index": "0+1;0+1", "aff_campus_unique": "Saarland Informatics Campus;Saarbr\u00fccken", "aff_country_unique_index": "0+0;0+0", "aff_country_unique": "Germany" }, { "id": "2021.acl-long.447", "title": "A Closer Look at Few-Shot Crosslingual Transfer: The Choice of Shots Matters", "track": "main", "status": "Long", "award": false, "abstract": "Few-shot crosslingual transfer has been shown to outperform its zero-shot counterpart with pretrained encoders like multilingual BERT. Despite its growing popularity, little to no attention has been paid to standardizing and analyzing the design of few-shot experiments. In this work, we highlight a fundamental risk posed by this shortcoming, illustrating that the model exhibits a high degree of sensitivity to the selection of few shots. We conduct a large-scale experimental study on 40 sets of sampled few shots for six diverse NLP tasks across up to 40 languages. We provide an analysis of success and failure cases of few-shot transfer, which highlights the role of lexical features. Additionally, we show that a straightforward full model finetuning approach is quite effective for few-shot transfer, outperforming several state-of-the-art few-shot approaches. As a step towards standardizing few-shot crosslingual experimental designs, we make our sampled few shots publicly available.", "author": "Mengjie Zhao; Yi Zhu; Ehsan Shareghi; Ivan Vuli\u0107; Roi Reichart; Anna Korhonen; Hinrich Sch\u00fctze", "authorids": "/m/mengjie-zhao/; /y/yi-zhu/; /e/ehsan-shareghi/; /i/ivan-vulic/; /r/roi-reichart/; /a/anna-korhonen/; /h/hinrich-schutze/", "bibtex": "@inproceedings{zhao-etal-2021-closer,\n title = \"A Closer Look at Few-Shot Crosslingual Transfer: The Choice of Shots Matters\",\n author = {Zhao, Mengjie and\n Zhu, Yi and\n Shareghi, Ehsan and\n Vuli{\\'c}, Ivan and\n Reichart, Roi and\n Korhonen, Anna and\n Sch{\\\"u}tze, Hinrich},\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.447/\",\n doi = \"10.18653/v1/2021.acl-long.447\",\n pages = \"5751--5767\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.447.pdf", "site": "https://aclanthology.org/2021.acl-long.447/", "pdf_size": 737136, "gs_citation": 56, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=1357292261117797123&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 5, "aff": "CIS, LMU Munich; LTL, University of Cambridge; Department of Data Science & AI, Monash University; LTL, University of Cambridge; Faculty of Industrial Engineering and Management, Technion, IIT; LTL, University of Cambridge; CIS, LMU Munich", "aff_domain": "cis.lmu.de;cam.ac.uk;monash.edu;cam.ac.uk;technion.ac.il;cam.ac.uk; ", "email": "cis.lmu.de;cam.ac.uk;monash.edu;cam.ac.uk;technion.ac.il;cam.ac.uk; ", "github": "https://github.com/fsxlt", "project": "", "author_num": 7, "aff_unique_index": "0;1;2;1;3;1;0", "aff_unique_norm": "Ludwig Maximilian University of Munich;University of Cambridge;Monash University;Technion - Israel Institute of Technology", "aff_unique_dep": "Computer Science and Information Systems;LTL;Department of Data Science & AI;Faculty of Industrial Engineering and Management", "aff_unique_url": "https://www.lmu.de;https://www.cam.ac.uk;https://www.monash.edu;https://www.technion.ac.il/en/", "aff_unique_abbr": "LMU;Cambridge;Monash;Technion", "aff_campus_unique_index": "0;1;1;1;0", "aff_campus_unique": "Munich;Cambridge;", "aff_country_unique_index": "0;1;2;1;3;1;0", "aff_country_unique": "Germany;United Kingdom;Australia;Israel" }, { "id": "2021.acl-short.73", "title": "A Cluster-based Approach for Improving Isotropy in Contextual Embedding Space", "track": "main", "status": "Short", "award": false, "abstract": "The representation degeneration problem in Contextual Word Representations (CWRs) hurts the expressiveness of the embedding space by forming an anisotropic cone where even unrelated words have excessively positive correlations. Existing techniques for tackling this issue require a learning process to re-train models with additional objectives and mostly employ a global assessment to study isotropy. Our quantitative analysis over isotropy shows that a local assessment could be more accurate due to the clustered structure of CWRs. Based on this observation, we propose a local cluster-based method to address the degeneration issue in contextual embedding spaces. We show that in clusters including punctuations and stop words, local dominant directions encode structural information, removing which can improve CWRs performance on semantic tasks. Moreover, we find that tense information in verb representations dominates sense semantics. We show that removing dominant directions of verb representations can transform the space to better suit semantic applications. Our experiments demonstrate that the proposed cluster-based method can mitigate the degeneration problem on multiple tasks.", "author": "Sara Rajaee; Mohammad Taher Pilehvar", "authorids": "/s/sara-rajaee/; /m/mohammad-taher-pilehvar/", "bibtex": "@inproceedings{rajaee-pilehvar-2021-cluster,\n title = \"A Cluster-based Approach for Improving Isotropy in Contextual Embedding Space\",\n author = \"Rajaee, Sara and\n Pilehvar, Mohammad Taher\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-short.73/\",\n doi = \"10.18653/v1/2021.acl-short.73\",\n pages = \"575--584\"\n}", "pdf": "https://aclanthology.org/2021.acl-short.73.pdf", "site": "https://aclanthology.org/2021.acl-short.73/", "pdf_size": 937371, "gs_citation": 43, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=7442790866157211388&as_sdt=5,33&sciodt=0,33&hl=en", "gs_version_total": 5, "aff": "Iran University of Science and Technology, Tehran, Iran+Tehran Institute for Advanced Studies, Tehran, Iran; Iran University of Science and Technology, Tehran, Iran", "aff_domain": "comp.iust.ac.ir;cam.ac.uk", "email": "comp.iust.ac.ir;cam.ac.uk", "github": "https://github.com/Sara-Rajaee/clusterbased_isotropy_enhancement/", "project": "", "author_num": 2, "aff_unique_index": "0+1;0", "aff_unique_norm": "Iran University of Science and Technology;Tehran Institute for Advanced Studies", "aff_unique_dep": ";", "aff_unique_url": "https://www.iust.ac.ir;", "aff_unique_abbr": "IUST;", "aff_campus_unique_index": "0+0;0", "aff_campus_unique": "Tehran", "aff_country_unique_index": "0+0;0", "aff_country_unique": "Iran" }, { "id": "2021.acl-long.404", "title": "A Cognitive Regularizer for Language Modeling", "track": "main", "status": "Long", "award": false, "abstract": "The uniform information density (UID) hypothesis, which posits that speakers behaving optimally tend to distribute information uniformly across a linguistic signal, has gained traction in psycholinguistics as an explanation for certain syntactic, morphological, and prosodic choices. In this work, we explore whether the UID hypothesis can be operationalized as an inductive bias for statistical language modeling. Specifically, we augment the canonical MLE objective for training language models with a regularizer that encodes UID. In experiments on ten languages spanning five language families, we find that using UID regularization consistently improves perplexity in language models, having a larger effect when training data is limited. Moreover, via an analysis of generated sequences, we find that UID-regularized language models have other desirable properties, e.g., they generate text that is more lexically diverse. Our results not only suggest that UID is a reasonable inductive bias for language modeling, but also provide an alternative validation of the UID hypothesis using modern-day NLP tools.", "author": "Jason Wei; Clara Meister; Ryan Cotterell", "authorids": "/j/jason-wei/; /c/clara-meister/; /r/ryan-cotterell/", "bibtex": "@inproceedings{wei-etal-2021-cognitive,\n title = \"A Cognitive Regularizer for Language Modeling\",\n author = \"Wei, Jason and\n Meister, Clara and\n Cotterell, Ryan\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.404/\",\n doi = \"10.18653/v1/2021.acl-long.404\",\n pages = \"5191--5202\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.404.pdf", "site": "https://aclanthology.org/2021.acl-long.404/", "pdf_size": 664000, "gs_citation": 24, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=8557840880227545507&as_sdt=4005&sciodt=0,6&hl=en", "gs_version_total": 7, "aff": "Google AI Language; ETH Z\u00fcrich; University of Cambridge", "aff_domain": "google.com;inf.ethz.ch;inf.ethz.ch", "email": "google.com;inf.ethz.ch;inf.ethz.ch", "github": "", "project": "", "author_num": 3, "aff_unique_index": "0;1;2", "aff_unique_norm": "Google;ETH Z\u00fcrich;University of Cambridge", "aff_unique_dep": "Google AI Language;;", "aff_unique_url": "https://ai.google;https://www.ethz.ch;https://www.cam.ac.uk", "aff_unique_abbr": "Google AI;ETHZ;Cambridge", "aff_campus_unique_index": "0;2", "aff_campus_unique": "Mountain View;;Cambridge", "aff_country_unique_index": "0;1;2", "aff_country_unique": "United States;Switzerland;United Kingdom" }, { "id": "2021.acl-long.450", "title": "A Conditional Splitting Framework for Efficient Constituency Parsing", "track": "main", "status": "Long", "award": false, "abstract": "We introduce a generic seq2seq parsing framework that casts constituency parsing problems (syntactic and discourse parsing) into a series of conditional splitting decisions. Our parsing model estimates the conditional probability distribution of possible splitting points in a given text span and supports efficient top-down decoding, which is linear in number of nodes. The conditional splitting formulation together with efficient beam search inference facilitate structural consistency without relying on expensive structured inference. Crucially, for discourse analysis we show that in our formulation, discourse segmentation can be framed as a special case of parsing which allows us to perform discourse parsing without requiring segmentation as a pre-requisite. Experiments show that our model achieves good results on the standard syntactic parsing tasks under settings with/without pre-trained representations and rivals state-of-the-art (SoTA) methods that are more computationally expensive than ours. In discourse parsing, our method outperforms SoTA by a good margin.", "author": "Thanh-Tung Nguyen; Xuan-Phi Nguyen; Shafiq Joty; Xiaoli Li", "authorids": "/t/thanh-tung-nguyen/; /x/xuan-phi-nguyen/; /s/shafiq-joty/; /x/xiaoli-li/", "bibtex": "@inproceedings{nguyen-etal-2021-conditional,\n title = \"A Conditional Splitting Framework for Efficient Constituency Parsing\",\n author = \"Nguyen, Thanh-Tung and\n Nguyen, Xuan-Phi and\n Joty, Shafiq and\n Li, Xiaoli\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.450/\",\n doi = \"10.18653/v1/2021.acl-long.450\",\n pages = \"5795--5807\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.450.pdf", "site": "https://aclanthology.org/2021.acl-long.450/", "pdf_size": 768452, "gs_citation": 10, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=3012811980080261868&as_sdt=5,44&sciodt=0,44&hl=en", "gs_version_total": 5, "aff": "Nanyang Technological University+Institute for Infocomm Research, A-STAR; Nanyang Technological University+Institute for Infocomm Research, A-STAR; Salesforce Research Asia+Nanyang Technological University+Institute for Infocomm Research, A-STAR; Nanyang Technological University+Institute for Infocomm Research, A-STAR", "aff_domain": "e.ntu.edu.sg;e.ntu.edu.sg;ntu.edu.sg;i2r.a-star.edu.sg", "email": "e.ntu.edu.sg;e.ntu.edu.sg;ntu.edu.sg;i2r.a-star.edu.sg", "github": "", "project": "", "author_num": 4, "aff_unique_index": "0+1;0+1;2+0+1;0+1", "aff_unique_norm": "Nanyang Technological University;Institute for Infocomm Research;Salesforce Research", "aff_unique_dep": ";;Research", "aff_unique_url": "https://www.ntu.edu.sg;https://www.i2r.a-star.edu.sg;https://research.salesforce.com", "aff_unique_abbr": "NTU;I2R;Salesforce Research Asia", "aff_campus_unique_index": ";;1;", "aff_campus_unique": ";Asia", "aff_country_unique_index": "0+0;0+0;0+0+0;0+0", "aff_country_unique": "Singapore" }, { "id": "2021.acl-long.83", "title": "A DQN-based Approach to Finding Precise Evidences for Fact Verification", "track": "main", "status": "Long", "award": false, "abstract": "Computing precise evidences, namely minimal sets of sentences that support or refute a given claim, rather than larger evidences is crucial in fact verification (FV), since larger evidences may contain conflicting pieces some of which support the claim while the other refute, thereby misleading FV. Despite being important, precise evidences are rarely studied by existing methods for FV. It is challenging to find precise evidences due to a large search space with lots of local optimums. Inspired by the strong exploration ability of the deep Q-learning network (DQN), we propose a DQN-based approach to retrieval of precise evidences. In addition, to tackle the label bias on Q-values computed by DQN, we design a post-processing strategy which seeks best thresholds for determining the true labels of computed evidences. Experimental results confirm the effectiveness of DQN in computing precise evidences and demonstrate improvements in achieving accurate claim verification.", "author": "Hai Wan; Haicheng Chen; Jianfeng Du; Weilin Luo; Rongzhen Ye", "authorids": "/h/hai-wan/; /h/haicheng-chen/; /j/jianfeng-du/; /w/weilin-luo/; /r/rongzhen-ye/", "bibtex": "@inproceedings{wan-etal-2021-dqn,\n title = \"A {DQN}-based Approach to Finding Precise Evidences for Fact Verification\",\n author = \"Wan, Hai and\n Chen, Haicheng and\n Du, Jianfeng and\n Luo, Weilin and\n Ye, Rongzhen\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.83/\",\n doi = \"10.18653/v1/2021.acl-long.83\",\n pages = \"1030--1039\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.83.pdf", "site": "https://aclanthology.org/2021.acl-long.83/", "pdf_size": 1048481, "gs_citation": 20, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=3127789047674142644&as_sdt=5,31&sciodt=0,31&hl=en", "gs_version_total": 2, "aff": "School of Computer Science and Engineering, Sun Yat-sen University; School of Computer Science and Engineering, Sun Yat-sen University; Guangzhou Key Laboratory of Multilingual Intelligent Processing, Guangdong University of Foreign Studies + Pazhou Lab; School of Computer Science and Engineering, Sun Yat-sen University; School of Computer Science and Engineering, Sun Yat-sen University", "aff_domain": "mail.sysu.edu.cn;mail2.sysu.edu.cn;gdufs.edu.cn;mail2.sysu.edu.cn;mail2.sysu.edu.cn", "email": "mail.sysu.edu.cn;mail2.sysu.edu.cn;gdufs.edu.cn;mail2.sysu.edu.cn;mail2.sysu.edu.cn", "github": "https://github.com/sysulic/DQN-FV", "project": "", "author_num": 5, "aff_unique_index": "0;0;1+2;0;0", "aff_unique_norm": "Sun Yat-sen University;Guangdong University of Foreign Studies;Pazhou Lab", "aff_unique_dep": "School of Computer Science and Engineering;Guangzhou Key Laboratory of Multilingual Intelligent Processing;", "aff_unique_url": "http://www.sysu.edu.cn;http://www.gdufs.edu.cn;", "aff_unique_abbr": "SYSU;GDUFS;", "aff_campus_unique_index": "1", "aff_campus_unique": ";Guangzhou", "aff_country_unique_index": "0;0;0;0;0", "aff_country_unique": "China;" }, { "id": "2021.acl-long.97", "title": "A Dataset and Baselines for Multilingual Reply Suggestion", "track": "main", "status": "Long", "award": false, "abstract": "Reply suggestion models help users process emails and chats faster. Previous work only studies English reply suggestion. Instead, we present MRS, a multilingual reply suggestion dataset with ten languages. MRS can be used to compare two families of models: 1) retrieval models that select the reply from a fixed set and 2) generation models that produce the reply from scratch. Therefore, MRS complements existing cross-lingual generalization benchmarks that focus on classification and sequence labeling tasks. We build a generation model and a retrieval model as baselines for MRS. The two models have different strengths in the monolingual setting, and they require different strategies to generalize across languages. MRS is publicly available at https://github.com/zhangmozhi/mrs.", "author": "Mozhi Zhang; Wei Wang; Budhaditya Deb; Guoqing Zheng; Milad Shokouhi; Ahmed Hassan Awadallah", "authorids": "/m/mozhi-zhang/; /w/wei-wang/; /b/budhaditya-deb/; /g/guoqing-zheng/; /m/milad-shokouhi/; /a/ahmed-hassan/", "bibtex": "@inproceedings{zhang-etal-2021-dataset,\n title = \"A Dataset and Baselines for Multilingual Reply Suggestion\",\n author = \"Zhang, Mozhi and\n Wang, Wei and\n Deb, Budhaditya and\n Zheng, Guoqing and\n Shokouhi, Milad and\n Awadallah, Ahmed Hassan\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.97/\",\n doi = \"10.18653/v1/2021.acl-long.97\",\n pages = \"1207--1220\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.97.pdf", "site": "https://aclanthology.org/2021.acl-long.97/", "pdf_size": 505625, "gs_citation": 8, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=16581033277470050682&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 5, "aff": "University of Maryland; Qualtrics + Microsoft Research; Microsoft AI; Microsoft Research; Microsoft AI; Microsoft Research", "aff_domain": "cs.umd.edu;qualtrics.com;microsoft.com;microsoft.com;microsoft.com;microsoft.com", "email": "cs.umd.edu;qualtrics.com;microsoft.com;microsoft.com;microsoft.com;microsoft.com", "github": "https://github.com/zhangmozhi/mrs", "project": "", "author_num": 6, "aff_unique_index": "0;1+2;2;2;2;2", "aff_unique_norm": "University of Maryland;Qualtrics;Microsoft Corporation", "aff_unique_dep": ";;Microsoft Research", "aff_unique_url": "https://www/umd.edu;https://www.qualtrics.com;https://www.microsoft.com/en-us/research", "aff_unique_abbr": "UMD;Qualtrics;MSR", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0;0+0;0;0;0;0", "aff_country_unique": "United States" }, { "id": "2021.acl-long.119", "title": "A Gradually Soft Multi-Task and Data-Augmented Approach to Medical Question Understanding", "track": "main", "status": "Long", "award": false, "abstract": "Users of medical question answering systems often submit long and detailed questions, making it hard to achieve high recall in answer retrieval. To alleviate this problem, we propose a novel Multi-Task Learning (MTL) method with data augmentation for medical question understanding. We first establish an equivalence between the tasks of question summarization and Recognizing Question Entailment (RQE) using their definitions in the medical domain. Based on this equivalence, we propose a data augmentation algorithm to use just one dataset to optimize for both tasks, with a weighted MTL loss. We introduce gradually soft parameter-sharing: a constraint for decoder parameters to be close, that is gradually loosened as we move to the highest layer. We show through ablation studies that our proposed novelties improve performance. Our method outperforms existing MTL methods across 4 datasets of medical question pairs, in ROUGE scores, RQE accuracy and human evaluation. Finally, we show that our method fares better than single-task learning under 4 low-resource settings.", "author": "Khalil Mrini; Franck Dernoncourt; Seunghyun Yoon; Trung Bui; Walter Chang; Emilia Farcas; Ndapa Nakashole", "authorids": "/k/khalil-mrini/; /f/franck-dernoncourt/; /s/seunghyun-yoon/; /t/trung-bui/; /w/walter-chang/; /e/emilia-farcas/; /n/ndapandula-nakashole/", "bibtex": "@inproceedings{mrini-etal-2021-gradually,\n title = \"A Gradually Soft Multi-Task and Data-Augmented Approach to Medical Question Understanding\",\n author = \"Mrini, Khalil and\n Dernoncourt, Franck and\n Yoon, Seunghyun and\n Bui, Trung and\n Chang, Walter and\n Farcas, Emilia and\n Nakashole, Ndapa\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.119/\",\n doi = \"10.18653/v1/2021.acl-long.119\",\n pages = \"1505--1515\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.119.pdf", "site": "https://aclanthology.org/2021.acl-long.119/", "pdf_size": 792294, "gs_citation": 34, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=3890212953366768823&as_sdt=5,33&sciodt=0,33&hl=en", "gs_version_total": 4, "aff": "University of California, San Diego, La Jolla, CA 92093; Adobe Research, San Jose, CA 95110; Adobe Research, San Jose, CA 95110; Adobe Research, San Jose, CA 95110; Adobe Research, San Jose, CA 95110; University of California, San Diego, La Jolla, CA 92093; University of California, San Diego, La Jolla, CA 92093", "aff_domain": "ucsd.edu;adobe.com;adobe.com;adobe.com;adobe.com;ucsd.edu;ucsd.edu", "email": "ucsd.edu;adobe.com;adobe.com;adobe.com;adobe.com;ucsd.edu;ucsd.edu", "github": "", "project": "", "author_num": 7, "aff_unique_index": "0;1;1;1;1;0;0", "aff_unique_norm": "University of California, San Diego;Adobe Research", "aff_unique_dep": ";", "aff_unique_url": "https://www.ucsd.edu;https://research.adobe.com", "aff_unique_abbr": "UCSD;Adobe", "aff_campus_unique_index": "0;1;1;1;1;0;0", "aff_campus_unique": "La Jolla;San Jose", "aff_country_unique_index": "0;0;0;0;0;0;0", "aff_country_unique": "United States" }, { "id": "2021.acl-long.187", "title": "A Hierarchical VAE for Calibrating Attributes while Generating Text using Normalizing Flow", "track": "main", "status": "Long", "award": false, "abstract": "In this digital age, online users expect personalized content. To cater to diverse group of audiences across online platforms it is necessary to generate multiple variants of same content with differing degree of characteristics (sentiment, style, formality, etc.). Though text-style transfer is a well explored related area, it focuses on flipping the style attribute polarity instead of regulating a fine-grained attribute transfer. In this paper we propose a hierarchical architecture for finer control over the at- tribute, preserving content using attribute dis- entanglement. We demonstrate the effective- ness of the generative process for two different attributes with varied complexity, namely sentiment and formality. With extensive experiments and human evaluation on five real-world datasets, we show that the framework can generate natural looking sentences with finer degree of control of intensity of a given attribute.", "author": "Bidisha Samanta; Mohit Agrawal; NIloy Ganguly", "authorids": "/b/bidisha-samanta/; /m/mohit-agrawal/; /n/niloy-ganguly/", "bibtex": "@inproceedings{samanta-etal-2021-hierarchical,\n title = \"A Hierarchical {VAE} for Calibrating Attributes while Generating Text using Normalizing Flow\",\n author = \"Samanta, Bidisha and\n Agrawal, Mohit and\n Ganguly, NIloy\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.187/\",\n doi = \"10.18653/v1/2021.acl-long.187\",\n pages = \"2405--2415\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.187.pdf", "site": "https://aclanthology.org/2021.acl-long.187/", "pdf_size": 779920, "gs_citation": 7, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=15812655361595957556&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 2, "aff": "IIT Kharagpur; IIT Kharagpur; IIT Kharagpur", "aff_domain": "iitkgp.ac.in;iitkgp.ac.in;cse.iitkgp.ac.in", "email": "iitkgp.ac.in;iitkgp.ac.in;cse.iitkgp.ac.in", "github": "", "project": "", "author_num": 3, "aff_unique_index": "0;0;0", "aff_unique_norm": "Indian Institute of Technology Kharagpur", "aff_unique_dep": "", "aff_unique_url": "https://www.iitkgp.ac.in", "aff_unique_abbr": "IIT KGP", "aff_campus_unique_index": "0;0;0", "aff_campus_unique": "Kharagpur", "aff_country_unique_index": "0;0;0", "aff_country_unique": "India" }, { "id": "2021.acl-long.436", "title": "A Human-machine Collaborative Framework for Evaluating Malevolence in Dialogues", "track": "main", "status": "Long", "award": false, "abstract": "Conversational dialogue systems (CDSs) are hard to evaluate due to the complexity of natural language. Automatic evaluation of dialogues often shows insufficient correlation with human judgements. Human evaluation is reliable but labor-intensive. We introduce a human-machine collaborative framework, HMCEval, that can guarantee reliability of the evaluation outcomes with reduced human effort. HMCEval casts dialogue evaluation as a sample assignment problem, where we need to decide to assign a sample to a human or a machine for evaluation. HMCEval includes a model confidence estimation module to estimate the confidence of the predicted sample assignment, and a human effort estimation module to estimate the human effort should the sample be assigned to human evaluation, as well as a sample assignment execution module that finds the optimum assignment solution based on the estimated confidence and effort. We assess the performance of HMCEval on the task of evaluating malevolence in dialogues. The experimental results show that HMCEval achieves around 99% evaluation accuracy with half of the human effort spared, showing that HMCEval provides reliable evaluation outcomes while reducing human effort by a large amount.", "author": "Yangjun Zhang; Pengjie Ren; Maarten de Rijke", "authorids": "/y/yangjun-zhang/; /p/pengjie-ren/; /m/maarten-de-rijke/", "bibtex": "@inproceedings{zhang-etal-2021-human,\n title = \"A Human-machine Collaborative Framework for Evaluating Malevolence in Dialogues\",\n author = \"Zhang, Yangjun and\n Ren, Pengjie and\n de Rijke, Maarten\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.436/\",\n doi = \"10.18653/v1/2021.acl-long.436\",\n pages = \"5612--5623\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.436.pdf", "site": "https://aclanthology.org/2021.acl-long.436/", "pdf_size": 781353, "gs_citation": 17, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=15017598690402664410&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 7, "aff": "University of Amsterdam; Shandong University; University of Amsterdam + Ahold Delhaize", "aff_domain": "uva.nl;sdu.edu.cn;uva.nl", "email": "uva.nl;sdu.edu.cn;uva.nl", "github": "", "project": "", "author_num": 3, "aff_unique_index": "0;1;0+2", "aff_unique_norm": "University of Amsterdam;Shandong University;Ahold Delhaize", "aff_unique_dep": ";;", "aff_unique_url": "https://www.uva.nl;http://www.sdu.edu.cn;https://www.aholddelhaize.com", "aff_unique_abbr": "UvA;SDU;", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0;1;0+0", "aff_country_unique": "Netherlands;China" }, { "id": "2021.acl-long.138", "title": "A Joint Model for Dropped Pronoun Recovery and Conversational Discourse Parsing in Chinese Conversational Speech", "track": "main", "status": "Long", "award": false, "abstract": "In this paper, we present a neural model for joint dropped pronoun recovery (DPR) and conversational discourse parsing (CDP) in Chinese conversational speech. We show that DPR and CDP are closely related, and a joint model benefits both tasks. We refer to our model as DiscProReco, and it first encodes the tokens in each utterance in a conversation with a directed Graph Convolutional Network (GCN). The token states for an utterance are then aggregated to produce a single state for each utterance. The utterance states are then fed into a biaffine classifier to construct a conversational discourse graph. A second (multi-relational) GCN is then applied to the utterance states to produce a discourse relation-augmented representation for the utterances, which are then fused together with token states in each utterance as input to a dropped pronoun recovery layer. The joint model is trained and evaluated on a new Structure Parsing-enhanced Dropped Pronoun Recovery (SPDPR) data set that we annotated with both two types of information. Experimental results on the SPDPR dataset and other benchmarks show that DiscProReco significantly outperforms the state-of-the-art baselines of both tasks.", "author": "Jingxuan Yang; Kerui Xu; Jun Xu; Si Li; Sheng Gao; Jun Guo; Nianwen Xue; Ji-Rong Wen", "authorids": "/j/jingxuan-yang/; /k/kerui-xu/; /j/jun-xu/; /s/si-li/; /s/sheng-gao/; /j/jun-guo/; /n/nianwen-xue/; /j/ji-rong-wen/", "bibtex": "@inproceedings{yang-etal-2021-joint,\n title = \"A Joint Model for Dropped Pronoun Recovery and Conversational Discourse Parsing in {C}hinese Conversational Speech\",\n author = \"Yang, Jingxuan and\n Xu, Kerui and\n Xu, Jun and\n Li, Si and\n Gao, Sheng and\n Guo, Jun and\n Xue, Nianwen and\n Wen, Ji-Rong\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.138/\",\n doi = \"10.18653/v1/2021.acl-long.138\",\n pages = \"1752--1763\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.138.pdf", "site": "https://aclanthology.org/2021.acl-long.138/", "pdf_size": 854294, "gs_citation": 18, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=14911446657056922686&as_sdt=5,33&sciodt=0,33&hl=en", "gs_version_total": 7, "aff": "School of Artificial Intelligence, Beijing University of Posts and Telecommunications; School of Artificial Intelligence, Beijing University of Posts and Telecommunications; Gaoling School of Artificial Intelligence, Renmin University of China+Beijing Key Laboratory of Big Data Management and Analysis Methods; School of Artificial Intelligence, Beijing University of Posts and Telecommunications; School of Artificial Intelligence, Beijing University of Posts and Telecommunications; School of Artificial Intelligence, Beijing University of Posts and Telecommunications; Department of Computer Science, Brandeis University; Gaoling School of Artificial Intelligence, Renmin University of China+Beijing Key Laboratory of Big Data Management and Analysis Methods", "aff_domain": "bupt.edu.cn;bupt.edu.cn;ruc.edu.cn;bupt.edu.cn;bupt.edu.cn;bupt.edu.cn;brandeis.edu;gmail.com", "email": "bupt.edu.cn;bupt.edu.cn;ruc.edu.cn;bupt.edu.cn;bupt.edu.cn;bupt.edu.cn;brandeis.edu;gmail.com", "github": "", "project": "", "author_num": 8, "aff_unique_index": "0;0;1+2;0;0;0;3;1+2", "aff_unique_norm": "Beijing University of Posts and Telecommunications;Renmin University of China;Beijing Key Laboratory of Big Data Management and Analysis Methods;Brandeis University", "aff_unique_dep": "School of Artificial Intelligence;Gaoling School of Artificial Intelligence;Big Data Management and Analysis;Department of Computer Science", "aff_unique_url": "http://www.bupt.edu.cn/;http://www.ruc.edu.cn;;https://www.brandeis.edu", "aff_unique_abbr": "BUPT;RUC;;Brandeis", "aff_campus_unique_index": "0;0;0;0;0;0;0", "aff_campus_unique": "Beijing;", "aff_country_unique_index": "0;0;0+0;0;0;0;1;0+0", "aff_country_unique": "China;United States" }, { "id": "2021.acl-long.407", "title": "A Knowledge-Guided Framework for Frame Identification", "track": "main", "status": "Long", "award": false, "abstract": "Frame Identification (FI) is a fundamental and challenging task in frame semantic parsing. The task aims to find the exact frame evoked by a target word in a given sentence. It is generally regarded as a classification task in existing work, where frames are treated as discrete labels or represented using onehot embeddings. However, the valuable knowledge about frames is neglected. In this paper, we propose a Knowledge-Guided Frame Identification framework (KGFI) that integrates three types frame knowledge, including frame definitions, frame elements and frame-to-frame relations, to learn better frame representation, which guides the KGFI to jointly map target words and frames into the same embedding space and subsequently identify the best frame by calculating the dot-product similarity scores between the target word embedding and all of the frame embeddings. The extensive experimental results demonstrate KGFI significantly outperforms the state-of-the-art methods on two benchmark datasets.", "author": "Xuefeng Su; Ru Li; Xiaoli Li; Jeff Z. Pan; Hu Zhang; Qinghua Chai; Xiaoqi Han", "authorids": "/x/xuefeng-su/; /r/ru-li/; /x/xiaoli-li/; /j/jeff-z-pan/; /h/hu-zhang/; /q/qinghua-chai/; /x/xiaoqi-han/", "bibtex": "@inproceedings{su-etal-2021-knowledge,\n title = \"A Knowledge-Guided Framework for Frame Identification\",\n author = \"Su, Xuefeng and\n Li, Ru and\n Li, Xiaoli and\n Pan, Jeff Z. and\n Zhang, Hu and\n Chai, Qinghua and\n Han, Xiaoqi\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.407/\",\n doi = \"10.18653/v1/2021.acl-long.407\",\n pages = \"5230--5240\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.407.pdf", "site": "https://aclanthology.org/2021.acl-long.407/", "pdf_size": 631506, "gs_citation": 22, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=16017796822708843624&as_sdt=5,31&sciodt=0,31&hl=en", "gs_version_total": 4, "aff": "School of Computer and Information Technology, Shanxi University, Taiyuan, China + School of E-commerce and Logistics, Shanxi Vocational University of Engineering Technology, Taiyuan, China; Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education, Shanxi University, Taiyuan, China; Institute for Infocomm Research, A*Star, Singapore; ILCC, School of Informatics, University of Edinburgh, UK; School of Computer and Information Technology, Shanxi University, Taiyuan, China; School of Computer and Information Technology, Shanxi University, Taiyuan, China; School of Computer and Information Technology, Shanxi University, Taiyuan, China", "aff_domain": "163.com;sxu.edu.cn;ntu.edu.sg;ed.ac.uk;sxu.edu.cn;sxu.edu.cn;163.com", "email": "163.com;sxu.edu.cn;ntu.edu.sg;ed.ac.uk;sxu.edu.cn;sxu.edu.cn;163.com", "github": "", "project": "", "author_num": 7, "aff_unique_index": "0+1;0;2;3;0;0;0", "aff_unique_norm": "Shanxi University;Shanxi Vocational University of Engineering Technology;Institute for Infocomm Research;University of Edinburgh", "aff_unique_dep": "School of Computer and Information Technology;School of E-commerce and Logistics;;School of Informatics", "aff_unique_url": ";;https://www.i2r.a-star.edu.sg;https://www.ed.ac.uk", "aff_unique_abbr": ";;I2R;Edinburgh", "aff_campus_unique_index": "0+0;0;2;0;0;0", "aff_campus_unique": "Taiyuan;;Edinburgh", "aff_country_unique_index": "0+0;0;1;2;0;0;0", "aff_country_unique": "China;Singapore;United Kingdom" }, { "id": "2021.acl-long.218", "title": "A Large-Scale Chinese Multimodal NER Dataset with Speech Clues", "track": "main", "status": "Long", "award": false, "abstract": "In this paper, we aim to explore an uncharted territory, which is Chinese multimodal named entity recognition (NER) with both textual and acoustic contents. To achieve this, we construct a large-scale human-annotated Chinese multimodal NER dataset, named CNERTA. Our corpus totally contains 42,987 annotated sentences accompanying by 71 hours of speech data. Based on this dataset, we propose a family of strong and representative baseline models, which can leverage textual features or multimodal features. Upon these baselines, to capture the natural monotonic alignment between the textual modality and the acoustic modality, we further propose a simple multimodal multitask model by introducing a speech-to-text alignment auxiliary task. Through extensive experiments, we observe that: (1) Progressive performance boosts as we move from unimodal to multimodal, verifying the necessity of integrating speech clues into Chinese NER. (2) Our proposed model yields state-of-the-art (SoTA) results on CNERTA, demonstrating its effectiveness. For further research, the annotated dataset is publicly available at http://github.com/DianboWork/CNERTA.", "author": "Dianbo Sui; Zhengkun Tian; Yubo Chen; Kang Liu; Jun Zhao", "authorids": "/d/dianbo-sui/; /z/zhengkun-tian/; /y/yubo-chen/; /k/kang-liu/; /j/jun-zhao/", "bibtex": "@inproceedings{sui-etal-2021-large,\n title = \"A Large-Scale {C}hinese Multimodal {NER} Dataset with Speech Clues\",\n author = \"Sui, Dianbo and\n Tian, Zhengkun and\n Chen, Yubo and\n Liu, Kang and\n Zhao, Jun\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.218/\",\n doi = \"10.18653/v1/2021.acl-long.218\",\n pages = \"2807--2818\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.218.pdf", "site": "https://aclanthology.org/2021.acl-long.218/", "pdf_size": 767610, "gs_citation": 40, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=10934043647539743405&as_sdt=5,44&sciodt=0,44&hl=en", "gs_version_total": 2, "aff": "National Laboratory of Pattern Recognition, Institute of Automation, CAS; National Laboratory of Pattern Recognition, Institute of Automation, CAS; National Laboratory of Pattern Recognition, Institute of Automation, CAS; National Laboratory of Pattern Recognition, Institute of Automation, CAS; National Laboratory of Pattern Recognition, Institute of Automation, CAS", "aff_domain": "nlpr.ia.ac.cn;nlpr.ia.ac.cn;nlpr.ia.ac.cn;nlpr.ia.ac.cn;nlpr.ia.ac.cn", "email": "nlpr.ia.ac.cn;nlpr.ia.ac.cn;nlpr.ia.ac.cn;nlpr.ia.ac.cn;nlpr.ia.ac.cn", "github": "http://github.com/DianboWork/CNERTA", "project": "", "author_num": 5, "aff_unique_index": "0;0;0;0;0", "aff_unique_norm": "Chinese Academy of Sciences", "aff_unique_dep": "Institute of Automation", "aff_unique_url": "http://www.ia.cas.cn", "aff_unique_abbr": "CAS", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0;0;0;0;0", "aff_country_unique": "China" }, { "id": "2021.acl-short.71", "title": "A Mixture-of-Experts Model for Antonym-Synonym Discrimination", "track": "main", "status": "Short", "award": false, "abstract": "Discrimination between antonyms and synonyms is an important and challenging NLP task. Antonyms and synonyms often share the same or similar contexts and thus are hard to make a distinction. This paper proposes two underlying hypotheses and employs the mixture-of-experts framework as a solution. It works on the basis of a divide-and-conquer strategy, where a number of localized experts focus on their own domains (or subspaces) to learn their specialties, and a gating mechanism determines the space partitioning and the expert mixture. Experimental results have shown that our method achieves the state-of-the-art performance on the task.", "author": "Zhipeng Xie; Nan Zeng", "authorids": "/z/zhipeng-xie/; /n/nan-zeng/", "bibtex": "@inproceedings{xie-zeng-2021-mixture,\n title = \"A Mixture-of-Experts Model for Antonym-Synonym Discrimination\",\n author = \"Xie, Zhipeng and\n Zeng, Nan\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-short.71/\",\n doi = \"10.18653/v1/2021.acl-short.71\",\n pages = \"558--564\"\n}", "pdf": "https://aclanthology.org/2021.acl-short.71.pdf", "site": "https://aclanthology.org/2021.acl-short.71/", "pdf_size": 425157, "gs_citation": 11, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=18417023474410901814&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 2, "aff": "School of Computer Science, Fudan University, Shanghai, China; School of Computer Science, Fudan University, Shanghai, China", "aff_domain": "fudan.edu.cn;fudan.edu.cn", "email": "fudan.edu.cn;fudan.edu.cn", "github": "", "project": "", "author_num": 2, "aff_unique_index": "0;0", "aff_unique_norm": "Fudan University", "aff_unique_dep": "School of Computer Science", "aff_unique_url": "https://www.fudan.edu.cn", "aff_unique_abbr": "Fudan", "aff_campus_unique_index": "0;0", "aff_campus_unique": "Shanghai", "aff_country_unique_index": "0;0", "aff_country_unique": "China" }, { "id": "2021.acl-long.318", "title": "A Mutual Information Maximization Approach for the Spurious Solution Problem in Weakly Supervised Question Answering", "track": "main", "status": "Long", "award": false, "abstract": "Weakly supervised question answering usually has only the final answers as supervision signals while the correct solutions to derive the answers are not provided. This setting gives rise to the spurious solution problem: there may exist many spurious solutions that coincidentally derive the correct answer, but training on such solutions can hurt model performance (e.g., producing wrong solutions or answers). For example, for discrete reasoning tasks as on DROP, there may exist many equations to derive a numeric answer, and typically only one of them is correct. Previous learning methods mostly filter out spurious solutions with heuristics or using model confidence, but do not explicitly exploit the semantic correlations between a question and its solution. In this paper, to alleviate the spurious solution problem, we propose to explicitly exploit such semantic correlations by maximizing the mutual information between question-answer pairs and predicted solutions. Extensive experiments on four question answering datasets show that our method significantly outperforms previous learning methods in terms of task performance and is more effective in training models to produce correct solutions.", "author": "Zhihong Shao; Lifeng Shang; Qun Liu; Minlie Huang", "authorids": "/z/zhihong-shao/; /l/lifeng-shang/; /q/qun-liu/; /m/minlie-huang/", "bibtex": "@inproceedings{shao-etal-2021-mutual,\n title = \"A Mutual Information Maximization Approach for the Spurious Solution Problem in Weakly Supervised Question Answering\",\n author = \"Shao, Zhihong and\n Shang, Lifeng and\n Liu, Qun and\n Huang, Minlie\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.318/\",\n doi = \"10.18653/v1/2021.acl-long.318\",\n pages = \"4111--4124\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.318.pdf", "site": "https://aclanthology.org/2021.acl-long.318/", "pdf_size": 1091996, "gs_citation": 6, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=16269194523745050120&as_sdt=5,44&sciodt=0,44&hl=en", "gs_version_total": 4, "aff": "The CoAI group, DCST, Tsinghua University+State Key Lab of Intelligent Technology and Systems+Beijing National Research Center for Information Science and Technology+Tsinghua University; Huawei Noah\u2019s Ark Lab; Huawei Noah\u2019s Ark Lab; The CoAI group, DCST, Tsinghua University+State Key Lab of Intelligent Technology and Systems+Beijing National Research Center for Information Science and Technology+Tsinghua University", "aff_domain": "mails.tsinghua.edu.cn;huawei.com;huawei.com;tsinghua.edu.cn", "email": "mails.tsinghua.edu.cn;huawei.com;huawei.com;tsinghua.edu.cn", "github": "", "project": "", "author_num": 4, "aff_unique_index": "0+1+2+0;3;3;0+1+2+0", "aff_unique_norm": "Tsinghua University;State Key Lab of Intelligent Technology and Systems;Beijing National Research Center for Information Science and Technology;Huawei", "aff_unique_dep": "Department of Computer Science and Technology;;;Noah\u2019s Ark Lab", "aff_unique_url": "https://www.tsinghua.edu.cn;;;https://www.huawei.com", "aff_unique_abbr": "THU;;;Huawei", "aff_campus_unique_index": ";", "aff_campus_unique": "", "aff_country_unique_index": "0+0+0+0;0;0;0+0+0+0", "aff_country_unique": "China" }, { "id": "2021.acl-long.301", "title": "A Neural Model for Joint Document and Snippet Ranking in Question Answering for Large Document Collections", "track": "main", "status": "Long", "award": false, "abstract": "Question answering (QA) systems for large document collections typically use pipelines that (i) retrieve possibly relevant documents, (ii) re-rank them, (iii) rank paragraphs or other snippets of the top-ranked documents, and (iv) select spans of the top-ranked snippets as exact answers. Pipelines are conceptually simple, but errors propagate from one component to the next, without later components being able to revise earlier decisions. We present an architecture for joint document and snippet ranking, the two middle stages, which leverages the intuition that relevant documents have good snippets and good snippets come from relevant documents. The architecture is general and can be used with any neural text relevance ranker. We experiment with two main instantiations of the architecture, based on POSIT-DRMM (PDRMM) and a BERT-based ranker. Experiments on biomedical data from BIOASQ show that our joint models vastly outperform the pipelines in snippet retrieval, the main goal for QA, with fewer trainable parameters, also remaining competitive in document retrieval. Furthermore, our joint PDRMM-based model is competitive with BERT-based models, despite using orders of magnitude fewer parameters. These claims are also supported by human evaluation on two test batches of BIOASQ. To test our key findings on another dataset, we modified the Natural Questions dataset so that it can also be used for document and snippet retrieval. Our joint PDRMM-based model again outperforms the corresponding pipeline in snippet retrieval on the modified Natural Questions dataset, even though it performs worse than the pipeline in document retrieval. We make our code and the modified Natural Questions dataset publicly available.", "author": "Dimitris Pappas; Ion Androutsopoulos", "authorids": "/d/dimitris-pappas/; /i/ion-androutsopoulos/", "bibtex": "@inproceedings{pappas-androutsopoulos-2021-neural,\n title = \"A Neural Model for Joint Document and Snippet Ranking in Question Answering for Large Document Collections\",\n author = \"Pappas, Dimitris and\n Androutsopoulos, Ion\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.301/\",\n doi = \"10.18653/v1/2021.acl-long.301\",\n pages = \"3896--3907\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.301.pdf", "site": "https://aclanthology.org/2021.acl-long.301/", "pdf_size": 410552, "gs_citation": 5, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=8619580390913803823&as_sdt=5,31&sciodt=0,31&hl=en", "gs_version_total": 12, "aff": "Department of Informatics, Athens University of Economics and Business, Greece + Department of Informatics, Athens University of Economics and Business, Greece; Department of Informatics, Athens University of Economics and Business, Greece", "aff_domain": "aueb.gr;aueb.gr", "email": "aueb.gr;aueb.gr", "github": "", "project": "", "author_num": 2, "aff_unique_index": "0+0;0", "aff_unique_norm": "Athens University of Economics and Business", "aff_unique_dep": "Department of Informatics", "aff_unique_url": "https://www.aueb.gr", "aff_unique_abbr": "AUEB", "aff_campus_unique_index": "0+0;0", "aff_campus_unique": "Athens", "aff_country_unique_index": "0+0;0", "aff_country_unique": "Greece" }, { "id": "2021.acl-long.219", "title": "A Neural Transition-based Joint Model for Disease Named Entity Recognition and Normalization", "track": "main", "status": "Long", "award": false, "abstract": "Disease is one of the fundamental entities in biomedical research. Recognizing such entities from biomedical text and then normalizing them to a standardized disease vocabulary offer a tremendous opportunity for many downstream applications. Previous studies have demonstrated that joint modeling of the two sub-tasks has superior performance than the pipelined counterpart. Although the neural joint model based on multi-task learning framework has achieved state-of-the-art performance, it suffers from the boundary inconsistency problem due to the separate decoding procedures. Moreover, it ignores the rich information (e.g., the text surface form) of each candidate concept in the vocabulary, which is quite essential for entity normalization. In this work, we propose a neural transition-based joint model to alleviate these two issues. We transform the end-to-end disease recognition and normalization task as an action sequence prediction task, which not only jointly learns the model with shared representations of the input, but also jointly searches the output by state transitions in one search space. Moreover, we introduce attention mechanisms to take advantage of the text surface form of each candidate concept for better normalization performance. Experimental results conducted on two publicly available datasets show the effectiveness of the proposed method.", "author": "Zongcheng Ji; Tian Xia; Mei Han; Jing Xiao", "authorids": "/z/zongcheng-ji/; /t/tian-xia/; /m/mei-han/; /j/jing-xiao/", "bibtex": "@inproceedings{ji-etal-2021-neural,\n title = \"A Neural Transition-based Joint Model for Disease Named Entity Recognition and Normalization\",\n author = \"Ji, Zongcheng and\n Xia, Tian and\n Han, Mei and\n Xiao, Jing\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.219/\",\n doi = \"10.18653/v1/2021.acl-long.219\",\n pages = \"2819--2827\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.219.pdf", "site": "https://aclanthology.org/2021.acl-long.219/", "pdf_size": 400977, "gs_citation": 19, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=12362526596409614570&as_sdt=5,33&sciodt=0,33&hl=en", "gs_version_total": 2, "aff": "PAII Inc., Palo Alto, CA, USA; PAII Inc., Palo Alto, CA, USA; PAII Inc., Palo Alto, CA, USA; Ping An Technology, Shenzhen, China", "aff_domain": "gmail.com;gmail.com;gmail.com;pingan.com.cn", "email": "gmail.com;gmail.com;gmail.com;pingan.com.cn", "github": "", "project": "", "author_num": 4, "aff_unique_index": "0;0;0;1", "aff_unique_norm": "PAII Inc.;Ping An Technology", "aff_unique_dep": ";", "aff_unique_url": ";https://www.pingan.com", "aff_unique_abbr": ";", "aff_campus_unique_index": "0;0;0", "aff_campus_unique": "Palo Alto;", "aff_country_unique_index": "0;0;0;1", "aff_country_unique": "United States;China" }, { "id": "2021.acl-long.497", "title": "A Neural Transition-based Model for Argumentation Mining", "track": "main", "status": "Long", "award": false, "abstract": "The goal of argumentation mining is to automatically extract argumentation structures from argumentative texts. Most existing methods determine argumentative relations by exhaustively enumerating all possible pairs of argument components, which suffer from low efficiency and class imbalance. Moreover, due to the complex nature of argumentation, there is, so far, no universal method that can address both tree and non-tree structured argumentation. Towards these issues, we propose a neural transition-based model for argumentation mining, which incrementally builds an argumentation graph by generating a sequence of actions, avoiding inefficient enumeration operations. Furthermore, our model can handle both tree and non-tree structured argumentation without introducing any structural constraints. Experimental results show that our model achieves the best performance on two public datasets of different structures.", "author": "Jianzhu Bao; Chuang Fan; Jipeng Wu; Yixue Dang; Jiachen Du; Ruifeng Xu", "authorids": "/j/jianzhu-bao/; /c/chuang-fan/; /j/jipeng-wu/; /y/yixue-dang/; /j/jiachen-du/; /r/ruifeng-xu/", "bibtex": "@inproceedings{bao-etal-2021-neural,\n title = \"A Neural Transition-based Model for Argumentation Mining\",\n author = \"Bao, Jianzhu and\n Fan, Chuang and\n Wu, Jipeng and\n Dang, Yixue and\n Du, Jiachen and\n Xu, Ruifeng\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.497/\",\n doi = \"10.18653/v1/2021.acl-long.497\",\n pages = \"6354--6364\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.497.pdf", "site": "https://aclanthology.org/2021.acl-long.497/", "pdf_size": 795358, "gs_citation": 42, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=9711466587410623294&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 2, "aff": "Harbin Institute of Technology (Shenzhen), China+Joint Lab of China Merchants Securities and HITSZ; Harbin Institute of Technology (Shenzhen), China+Joint Lab of China Merchants Securities and HITSZ; Harbin Institute of Technology (Shenzhen), China+Joint Lab of China Merchants Securities and HITSZ; China Merchants Securities Co., Ltd.; Harbin Institute of Technology (Shenzhen), China+Joint Lab of China Merchants Securities and HITSZ; Harbin Institute of Technology (Shenzhen), China+Peng Cheng Laboratory, Shenzhen, China", "aff_domain": "gmail.com;gmail.com;stu.hit.edu.cn;cmschina.com.cn;gmail.com;hit.edu.cn", "email": "gmail.com;gmail.com;stu.hit.edu.cn;cmschina.com.cn;gmail.com;hit.edu.cn", "github": "", "project": "", "author_num": 6, "aff_unique_index": "0+1;0+1;0+1;1;0+1;0+2", "aff_unique_norm": "Harbin Institute of Technology;China Merchants Securities;Peng Cheng Laboratory", "aff_unique_dep": ";Joint Lab;", "aff_unique_url": "http://en.hhit.edu.cn/;http://www.cms.com.cn;", "aff_unique_abbr": "HIT;CMS;", "aff_campus_unique_index": "0;0;0;0;0+0", "aff_campus_unique": "Shenzhen;", "aff_country_unique_index": "0+0;0+0;0+0;0;0+0;0+0", "aff_country_unique": "China" }, { "id": "2021.acl-long.511", "title": "A Novel Estimator of Mutual Information for Learning to Disentangle Textual Representations", "track": "main", "status": "Long", "award": false, "abstract": "Learning disentangled representations of textual data is essential for many natural language tasks such as fair classification, style transfer and sentence generation, among others. The existent dominant approaches in the context of text data either rely on training an adversary (discriminator) that aims at making attribute values difficult to be inferred from the latent code or rely on minimising variational bounds of the mutual information between latent code and the value attribute. However, the available methods suffer of the impossibility to provide a fine-grained control of the degree (or force) of disentanglement. In contrast to adversarial methods, which are remarkably simple, although the adversary seems to be performing perfectly well during the training phase, after it is completed a fair amount of information about the undesired attribute still remains. This paper introduces a novel variational upper bound to the mutual information between an attribute and the latent code of an encoder. Our bound aims at controlling the approximation error via the Renyi\u2019s divergence, leading to both better disentangled representations and in particular, a precise control of the desirable degree of disentanglement than state-of-the-art methods proposed for textual data. Furthermore, it does not suffer from the degeneracy of other losses in multi-class scenarios. We show the superiority of this method on fair classification and on textual style transfer tasks. Additionally, we provide new insights illustrating various trade-offs in style transfer when attempting to learn disentangled representations and quality of the generated sentence.", "author": "Pierre Colombo; Pablo Piantanida; Chlo\u00e9 Clavel", "authorids": "/p/pierre-colombo/; /p/pablo-piantanida/; /c/chloe-clavel/", "bibtex": "@inproceedings{colombo-etal-2021-novel,\n title = \"A Novel Estimator of Mutual Information for Learning to Disentangle Textual Representations\",\n author = \"Colombo, Pierre and\n Piantanida, Pablo and\n Clavel, Chlo{\\'e}\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.511/\",\n doi = \"10.18653/v1/2021.acl-long.511\",\n pages = \"6539--6550\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.511.pdf", "site": "https://aclanthology.org/2021.acl-long.511/", "pdf_size": 648555, "gs_citation": 80, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=2943634378789919217&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 7, "aff": "IBM GBS France+T\u00e9l\u00e9com ParisTech, Universit\u00e9 Paris Saclay; Laboratoire des Signaux et Syst\u00e8mes (L2S), CentraleSupelec CNRS Universite Paris-Saclay+T\u00e9l\u00e9com ParisTech, Universit\u00e9 Paris Saclay; T\u00e9l\u00e9com ParisTech, Universit\u00e9 Paris Saclay", "aff_domain": "ibm.com;centralesupelec.fr;telecom-paris.fr", "email": "ibm.com;centralesupelec.fr;telecom-paris.fr", "github": "", "project": "", "author_num": 3, "aff_unique_index": "0+1;2+1;1", "aff_unique_norm": "IBM Global Business Services;T\u00e9l\u00e9com ParisTech;CentraleSupelec", "aff_unique_dep": ";;Laboratoire des Signaux et Syst\u00e8mes (L2S)", "aff_unique_url": "https://www.ibm.com/services;https://www.telecom-paristech.fr;https://www.centralesupelec.fr", "aff_unique_abbr": "IBM GBS;T\u00e9l\u00e9com ParisTech;CentraleSupelec", "aff_campus_unique_index": "1;1;1", "aff_campus_unique": ";Paris", "aff_country_unique_index": "0+0;0+0;0", "aff_country_unique": "France" }, { "id": "2021.acl-long.343", "title": "A Pre-training Strategy for Zero-Resource Response Selection in Knowledge-Grounded Conversations", "track": "main", "status": "Long", "award": false, "abstract": "Recently, many studies are emerging towards building a retrieval-based dialogue system that is able to effectively leverage background knowledge (e.g., documents) when conversing with humans. However, it is non-trivial to collect large-scale dialogues that are naturally grounded on the background documents, which hinders the effective and adequate training of knowledge selection and response matching. To overcome the challenge, we consider decomposing the training of the knowledge-grounded response selection into three tasks including: 1) query-passage matching task; 2) query-dialogue history matching task; 3) multi-turn response matching task, and joint learning all these tasks in a unified pre-trained language model. The former two tasks could help the model in knowledge selection and comprehension, while the last task is designed for matching the proper response with the given query and background knowledge (dialogue history). By this means, the model can be learned to select relevant knowledge and distinguish proper response, with the help of ad-hoc retrieval corpora and a large number of ungrounded multi-turn dialogues. Experimental results on two benchmarks of knowledge-grounded response selection indicate that our model can achieve comparable performance with several existing methods that rely on crowd-sourced data for training.", "author": "Chongyang Tao; Changyu Chen; Jiazhan Feng; Ji-Rong Wen; Rui Yan", "authorids": "/c/chongyang-tao/; /c/changyu-chen/; /j/jiazhan-feng/; /j/ji-rong-wen/; /r/rui-yan/", "bibtex": "@inproceedings{tao-etal-2021-pre,\n title = \"A Pre-training Strategy for Zero-Resource Response Selection in Knowledge-Grounded Conversations\",\n author = \"Tao, Chongyang and\n Chen, Changyu and\n Feng, Jiazhan and\n Wen, Ji-Rong and\n Yan, Rui\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.343/\",\n doi = \"10.18653/v1/2021.acl-long.343\",\n pages = \"4446--4457\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.343.pdf", "site": "https://aclanthology.org/2021.acl-long.343/", "pdf_size": 463181, "gs_citation": 9, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=220747048970325170&as_sdt=5,33&sciodt=0,33&hl=en", "gs_version_total": 2, "aff": "Peking University; Gaoling School of Arti\ufb01cial Intelligence, Renmin University of China + Beijing Academy of Arti\ufb01cial Intelligence; Peking University; Gaoling School of Arti\ufb01cial Intelligence, Renmin University of China + Beijing Academy of Arti\ufb01cial Intelligence; Gaoling School of Arti\ufb01cial Intelligence, Renmin University of China + Beijing Academy of Arti\ufb01cial Intelligence", "aff_domain": "pku.edu.cn;ruc.edu.cn;pku.edu.cn;ruc.edu.cn;ruc.edu.cn", "email": "pku.edu.cn;ruc.edu.cn;pku.edu.cn;ruc.edu.cn;ruc.edu.cn", "github": "", "project": "", "author_num": 5, "aff_unique_index": "0;1+2;0;1+2;1+2", "aff_unique_norm": "Peking University;Renmin University of China;Beijing Academy of Artificial Intelligence", "aff_unique_dep": ";Gaoling School of Arti\ufb01cial Intelligence;", "aff_unique_url": "http://www.pku.edu.cn;http://www.ruc.edu.cn;https://www.baaic.cn", "aff_unique_abbr": "Peking U;RUC;BAAI", "aff_campus_unique_index": "1;1;1", "aff_campus_unique": ";Beijing", "aff_country_unique_index": "0;0+0;0;0+0;0+0", "aff_country_unique": "China" }, { "id": "2021.acl-long.237", "title": "A Semantic-based Method for Unsupervised Commonsense Question Answering", "track": "main", "status": "Long", "award": false, "abstract": "Unsupervised commonsense question answering is appealing since it does not rely on any labeled task data. Among existing work, a popular solution is to use pre-trained language models to score candidate choices directly conditioned on the question or context. However, such scores from language models can be easily affected by irrelevant factors, such as word frequencies, sentence structures, etc. These distracting factors may not only mislead the model to choose a wrong answer but also make it oversensitive to lexical perturbations in candidate answers. In this paper, we present a novel SEmantic-based Question Answering method (SEQA) for unsupervised commonsense question answering. Instead of directly scoring each answer choice, our method first generates a set of plausible answers with generative models (e.g., GPT-2), and then uses these plausible answers to select the correct choice by considering the semantic similarity between each plausible answer and each choice. We devise a simple, yet sound formalism for this idea and verify its effectiveness and robustness with extensive experiments. We evaluate the proposed method on four benchmark datasets, and our method achieves the best results in unsupervised settings. Moreover, when attacked by TextFooler with synonym replacement, SEQA demonstrates much less performance drops than baselines, thereby indicating stronger robustness.", "author": "Yilin Niu; Fei Huang; Jiaming Liang; Wenkai Chen; Xiaoyan Zhu; Minlie Huang", "authorids": "/y/yilin-niu/; /f/fei-huang/; /j/jiaming-liang/; /w/wenkai-chen/; /x/xiaoyan-zhu/; /m/minlie-huang/", "bibtex": "@inproceedings{niu-etal-2021-semantic,\n title = \"A Semantic-based Method for Unsupervised Commonsense Question Answering\",\n author = \"Niu, Yilin and\n Huang, Fei and\n Liang, Jiaming and\n Chen, Wenkai and\n Zhu, Xiaoyan and\n Huang, Minlie\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.237/\",\n doi = \"10.18653/v1/2021.acl-long.237\",\n pages = \"3037--3049\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.237.pdf", "site": "https://aclanthology.org/2021.acl-long.237/", "pdf_size": 1218396, "gs_citation": 16, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=13960577097608329373&as_sdt=80000005&sciodt=0,23&hl=en", "gs_version_total": 8, "aff": "The CoAI group, DCST+Institute for Arti\ufb01cial Intelligence+State Key Lab of Intelligent Technology and Systems+Beijing National Research Center for Information Science and Technology+Tsinghua University, Beijing 100084, China; The CoAI group, DCST+Institute for Arti\ufb01cial Intelligence+State Key Lab of Intelligent Technology and Systems+Beijing National Research Center for Information Science and Technology+Tsinghua University, Beijing 100084, China; The CoAI group, DCST+Institute for Arti\ufb01cial Intelligence+State Key Lab of Intelligent Technology and Systems+Beijing National Research Center for Information Science and Technology+Tsinghua University, Beijing 100084, China; School of Computer Science and Technology, Beijing University of Posts and Telecommunications; The CoAI group, DCST+Institute for Arti\ufb01cial Intelligence+State Key Lab of Intelligent Technology and Systems+Beijing National Research Center for Information Science and Technology+Tsinghua University, Beijing 100084, China; The CoAI group, DCST+Institute for Arti\ufb01cial Intelligence+State Key Lab of Intelligent Technology and Systems+Beijing National Research Center for Information Science and Technology+Tsinghua University, Beijing 100084, China", "aff_domain": "tsinghua.org.cn;mails.tsinghua.edu.cn;mails.tsinghua.edu.cn;gmail.com;tsinghua.edu.cn;tsinghua.edu.cn", "email": "tsinghua.org.cn;mails.tsinghua.edu.cn;mails.tsinghua.edu.cn;gmail.com;tsinghua.edu.cn;tsinghua.edu.cn", "github": "", "project": "", "author_num": 6, "aff_unique_index": "0+1+2+3+4;0+1+2+3+4;0+1+2+3+4;5;0+1+2+3+4;0+1+2+3+4", "aff_unique_norm": "DCST;Institute for Artificial Intelligence;State Key Lab of Intelligent Technology and Systems;Beijing National Research Center for Information Science and Technology;Tsinghua University;Beijing University of Posts and Telecommunications", "aff_unique_dep": "The CoAI group;Artificial Intelligence;;;;School of Computer Science and Technology", "aff_unique_url": ";https://www.iai.uni-bonn.de/;;;https://www.tsinghua.edu.cn;http://www.bupt.edu.cn", "aff_unique_abbr": ";IAI;;;THU;BUPT", "aff_campus_unique_index": "1;1;1;1;1;1", "aff_campus_unique": ";Beijing", "aff_country_unique_index": "1+2+2+2;1+2+2+2;1+2+2+2;2;1+2+2+2;1+2+2+2", "aff_country_unique": ";Germany;China" }, { "id": "2021.acl-short.34", "title": "A Semantics-aware Transformer Model of Relation Linking for Knowledge Base Question Answering", "track": "main", "status": "Short", "award": false, "abstract": "Relation linking is a crucial component of Knowledge Base Question Answering systems. Existing systems use a wide variety of heuristics, or ensembles of multiple systems, heavily relying on the surface question text. However, the explicit semantic parse of the question is a rich source of relation information that is not taken advantage of. We propose a simple transformer-based neural model for relation linking that leverages the AMR semantic parse of a sentence. Our system significantly outperforms the state-of-the-art on 4 popular benchmark datasets. These are based on either DBpedia or Wikidata, demonstrating that our approach is effective across KGs.", "author": "Tahira Naseem; Srinivas Ravishankar; Nandana Mihindukulasooriya; Ibrahim Abdelaziz; Young-Suk Lee; Pavan Kapanipathi; Salim Roukos; Alfio Gliozzo; Alexander Gray", "authorids": "/t/tahira-naseem/; /s/srinivas-ravishankar/; /n/nandana-mihindukulasooriya/; /i/ibrahim-abdelaziz/; /y/young-suk-lee/; /p/pavan-kapanipathi/; /s/salim-roukos/; /a/alfio-gliozzo/; /a/alexander-gray/", "bibtex": "@inproceedings{naseem-etal-2021-semantics,\n title = \"A Semantics-aware Transformer Model of Relation Linking for Knowledge Base Question Answering\",\n author = \"Naseem, Tahira and\n Ravishankar, Srinivas and\n Mihindukulasooriya, Nandana and\n Abdelaziz, Ibrahim and\n Lee, Young-Suk and\n Kapanipathi, Pavan and\n Roukos, Salim and\n Gliozzo, Alfio and\n Gray, Alexander\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-short.34/\",\n doi = \"10.18653/v1/2021.acl-short.34\",\n pages = \"256--262\"\n}", "pdf": "https://aclanthology.org/2021.acl-short.34.pdf", "site": "https://aclanthology.org/2021.acl-short.34/", "pdf_size": 640194, "gs_citation": 34, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=10665914917608316139&as_sdt=5,34&sciodt=0,34&hl=en", "gs_version_total": 6, "aff": "IBM Research; IBM Research; IBM Research; IBM Research; IBM Research; IBM Research; IBM Research; IBM Research; IBM Research", "aff_domain": "us.ibm.com;ibm.com;ibm.com;ibm.com;us.ibm.com;us.ibm.com;us.ibm.com;us.ibm.com;ibm.com", "email": "us.ibm.com;ibm.com;ibm.com;ibm.com;us.ibm.com;us.ibm.com;us.ibm.com;us.ibm.com;ibm.com", "github": "", "project": "", "author_num": 9, "aff_unique_index": "0;0;0;0;0;0;0;0;0", "aff_unique_norm": "IBM", "aff_unique_dep": "IBM Research", "aff_unique_url": "https://www.ibm.com/research", "aff_unique_abbr": "IBM", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0;0;0;0;0;0;0;0;0", "aff_country_unique": "United States" }, { "id": "2021.acl-long.135", "title": "A Sequence-to-Sequence Approach to Dialogue State Tracking", "track": "main", "status": "Long", "award": false, "abstract": "This paper is concerned with dialogue state tracking (DST) in a task-oriented dialogue system. Building a DST module that is highly effective is still a challenging issue, although significant progresses have been made recently. This paper proposes a new approach to dialogue state tracking, referred to as Seq2Seq-DU, which formalizes DST as a sequence-to-sequence problem. Seq2Seq-DU employs two BERT-based encoders to respectively encode the utterances in the dialogue and the descriptions of schemas, an attender to calculate attentions between the utterance embeddings and the schema embeddings, and a decoder to generate pointers to represent the current state of dialogue. Seq2Seq-DU has the following advantages. It can jointly model intents, slots, and slot values; it can leverage the rich representations of utterances and schemas based on BERT; it can effectively deal with categorical and non-categorical slots, and unseen schemas. In addition, Seq2Seq-DU can also be used in the NLU (natural language understanding) module of a dialogue system. Experimental results on benchmark datasets in different settings (SGD, MultiWOZ2.2, MultiWOZ2.1, WOZ2.0, DSTC2, M2M, SNIPS, and ATIS) show that Seq2Seq-DU outperforms the existing methods.", "author": "Yue Feng; Yang Wang; Hang Li", "authorids": "/y/yue-feng/; /y/yang-wang/; /h/hang-li/", "bibtex": "@inproceedings{feng-etal-2021-sequence,\n title = \"A Sequence-to-Sequence Approach to Dialogue State Tracking\",\n author = \"Feng, Yue and\n Wang, Yang and\n Li, Hang\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.135/\",\n doi = \"10.18653/v1/2021.acl-long.135\",\n pages = \"1714--1725\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.135.pdf", "site": "https://aclanthology.org/2021.acl-long.135/", "pdf_size": 643345, "gs_citation": 78, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=1077751747568956428&as_sdt=80005&sciodt=0,11&hl=en", "gs_version_total": 4, "aff": "University College London, London, UK; ByteDance AI Lab, Beijing, China; ByteDance AI Lab, Beijing, China", "aff_domain": "ucl.ac.uk;bytedance.com;bytedance.com", "email": "ucl.ac.uk;bytedance.com;bytedance.com", "github": "", "project": "", "author_num": 3, "aff_unique_index": "0;1;1", "aff_unique_norm": "University College London;ByteDance", "aff_unique_dep": ";AI Lab", "aff_unique_url": "https://www.ucl.ac.uk;https://www.bytedance.com", "aff_unique_abbr": "UCL;ByteDance", "aff_campus_unique_index": "0;1;1", "aff_campus_unique": "London;Beijing", "aff_country_unique_index": "0;1;1", "aff_country_unique": "United Kingdom;China" }, { "id": "2021.acl-short.89", "title": "A Simple Recipe for Multilingual Grammatical Error Correction", "track": "main", "status": "Short", "award": false, "abstract": "This paper presents a simple recipe to trainstate-of-the-art multilingual Grammatical Error Correction (GEC) models. We achieve this by first proposing a language-agnostic method to generate a large number of synthetic examples. The second ingredient is to use large-scale multilingual language models (up to 11B parameters). Once fine-tuned on language-specific supervised sets we surpass the previous state-of-the-art results on GEC benchmarks in four languages: English, Czech, German and Russian. Having established a new set of baselines for GEC, we make our results easily reproducible and accessible by releasing a CLANG-8 dataset. It is produced by using our best model, which we call gT5, to clean the targets of a widely used yet noisy Lang-8 dataset. cLang-8 greatly simplifies typical GEC training pipelines composed of multiple fine-tuning stages \u2013 we demonstrate that performing a single fine-tuning stepon cLang-8 with the off-the-shelf language models yields further accuracy improvements over an already top-performing gT5 model for English.", "author": "Sascha Rothe; Jonathan Mallinson; Eric Malmi; Sebastian Krause; Aliaksei Severyn", "authorids": "/s/sascha-rothe/; /j/jonathan-mallinson/; /e/eric-malmi/; /s/sebastian-krause/; /a/aliaksei-severyn/", "bibtex": "@inproceedings{rothe-etal-2021-simple,\n title = \"A Simple Recipe for Multilingual Grammatical Error Correction\",\n author = \"Rothe, Sascha and\n Mallinson, Jonathan and\n Malmi, Eric and\n Krause, Sebastian and\n Severyn, Aliaksei\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-short.89/\",\n doi = \"10.18653/v1/2021.acl-short.89\",\n pages = \"702--707\"\n}", "pdf": "https://aclanthology.org/2021.acl-short.89.pdf", "site": "https://aclanthology.org/2021.acl-short.89/", "pdf_size": 215849, "gs_citation": 190, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=14325355215789466137&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 5, "aff": "Google; Google; Google; Google; Google", "aff_domain": "google.com;google.com;google.com;google.com;google.com", "email": "google.com;google.com;google.com;google.com;google.com", "github": "https://github.com/google-research-datasets/clang8", "project": "", "author_num": 5, "aff_unique_index": "0;0;0;0;0", "aff_unique_norm": "Google", "aff_unique_dep": "", "aff_unique_url": "https://www.google.com", "aff_unique_abbr": "Google", "aff_campus_unique_index": "0;0;0;0;0", "aff_campus_unique": "Mountain View", "aff_country_unique_index": "0;0;0;0;0", "aff_country_unique": "United States" }, { "id": "2021.acl-long.372", "title": "A Span-Based Model for Joint Overlapped and Discontinuous Named Entity Recognition", "track": "main", "status": "Long", "award": false, "abstract": "Research on overlapped and discontinuous named entity recognition (NER) has received increasing attention. The majority of previous work focuses on either overlapped or discontinuous entities. In this paper, we propose a novel span-based model that can recognize both overlapped and discontinuous entities jointly. The model includes two major steps. First, entity fragments are recognized by traversing over all possible text spans, thus, overlapped entities can be recognized. Second, we perform relation classification to judge whether a given pair of entity fragments to be overlapping or succession. In this way, we can recognize not only discontinuous entities, and meanwhile doubly check the overlapped entities. As a whole, our model can be regarded as a relation extraction paradigm essentially. Experimental results on multiple benchmark datasets (i.e., CLEF, GENIA and ACE05) show that our model is highly competitive for overlapped and discontinuous NER.", "author": "Fei Li; ZhiChao Lin; Meishan Zhang; Donghong Ji", "authorids": "/f/fei-li/; /z/zhichao-lin/; /m/meishan-zhang/; /d/donghong-ji/", "bibtex": "@inproceedings{li-etal-2021-span,\n title = \"A Span-Based Model for Joint Overlapped and Discontinuous Named Entity Recognition\",\n author = \"Li, Fei and\n Lin, ZhiChao and\n Zhang, Meishan and\n Ji, Donghong\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.372/\",\n doi = \"10.18653/v1/2021.acl-long.372\",\n pages = \"4814--4828\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.372.pdf", "site": "https://aclanthology.org/2021.acl-long.372/", "pdf_size": 1200708, "gs_citation": 136, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=3260476018699377855&as_sdt=5,33&sciodt=0,33&hl=en", "gs_version_total": 5, "aff": "Department of Key Laboratory of Aerospace Information Security and Trusted Computing, Ministry of Education, School of Cyber Science and Engineering, Wuhan University, China+School of New Media and Communication, Tianjin University, China; School of New Media and Communication, Tianjin University, China; School of New Media and Communication, Tianjin University, China; Department of Key Laboratory of Aerospace Information Security and Trusted Computing, Ministry of Education, School of Cyber Science and Engineering, Wuhan University, China", "aff_domain": "whu.edu.cn;whu.edu.cn;gmail.com;whu.edu.cn", "email": "whu.edu.cn;whu.edu.cn;gmail.com;whu.edu.cn", "github": "", "project": "", "author_num": 4, "aff_unique_index": "0+1;1;1;0", "aff_unique_norm": "Wuhan University;Tianjin University", "aff_unique_dep": "School of Cyber Science and Engineering;School of New Media and Communication", "aff_unique_url": "http://www.whu.edu.cn/;http://www.tju.edu.cn", "aff_unique_abbr": "WHU;Tianjin University", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0+0;0;0;0", "aff_country_unique": "China" }, { "id": "2021.acl-short.26", "title": "A Span-based Dynamic Local Attention Model for Sequential Sentence Classification", "track": "main", "status": "Short", "award": false, "abstract": "Sequential sentence classification aims to classify each sentence in the document based on the context in which sentences appear. Most existing work addresses this problem using a hierarchical sequence labeling network. However, they ignore considering the latent segment structure of the document, in which contiguous sentences often have coherent semantics. In this paper, we proposed a span-based dynamic local attention model that could explicitly capture the structural information by the proposed supervised dynamic local attention. We further introduce an auxiliary task called span-based classification to explore the span-level representations. Extensive experiments show that our model achieves better or competitive performance against state-of-the-art baselines on two benchmark datasets.", "author": "Xichen Shang; Qianli Ma; Zhenxi Lin; Jiangyue Yan; Zipeng Chen", "authorids": "/x/xichen-shang/; /q/qianli-ma/; /z/zhenxi-lin/; /j/jiangyue-yan/; /z/zipeng-chen/", "bibtex": "@inproceedings{shang-etal-2021-span,\n title = \"A Span-based Dynamic Local Attention Model for Sequential Sentence Classification\",\n author = \"Shang, Xichen and\n Ma, Qianli and\n Lin, Zhenxi and\n Yan, Jiangyue and\n Chen, Zipeng\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-short.26/\",\n doi = \"10.18653/v1/2021.acl-short.26\",\n pages = \"198--203\"\n}", "pdf": "https://aclanthology.org/2021.acl-short.26.pdf", "site": "https://aclanthology.org/2021.acl-short.26/", "pdf_size": 617057, "gs_citation": 18, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=13549390511183432943&as_sdt=5,44&sciodt=0,44&hl=en", "gs_version_total": 2, "aff": "School of Computer Science and Engineering, South China University of Technology; School of Computer Science and Engineering, South China University of Technology; School of Computer Science and Engineering, South China University of Technology; School of Computer Science and Engineering, South China University of Technology; School of Computer Science and Engineering, South China University of Technology", "aff_domain": "foxmail.com;scut.edu.cn; ; ; ", "email": "foxmail.com;scut.edu.cn; ; ; ", "github": "", "project": "", "author_num": 5, "aff_unique_index": "0;0;0;0;0", "aff_unique_norm": "South China University of Technology", "aff_unique_dep": "School of Computer Science and Engineering", "aff_unique_url": "https://www.scut.edu.cn", "aff_unique_abbr": "SCUT", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0;0;0;0;0", "aff_country_unique": "China" }, { "id": "2021.acl-long.131", "title": "A Survey of Code-switching: Linguistic and Social Perspectives for Language Technologies", "track": "main", "status": "Long", "award": false, "abstract": "The analysis of data in which multiple languages are represented has gained popularity among computational linguists in recent years. So far, much of this research focuses mainly on the improvement of computational methods and largely ignores linguistic and social aspects of C-S discussed across a wide range of languages within the long-established literature in linguistics. To fill this gap, we offer a survey of code-switching (C-S) covering the literature in linguistics with a reflection on the key issues in language technologies. From the linguistic perspective, we provide an overview of structural and functional patterns of C-S focusing on the literature from European and Indian contexts as highly multilingual areas. From the language technologies perspective, we discuss how massive language models fail to represent diverse C-S types due to lack of appropriate training data, lack of robust evaluation benchmarks for C-S (across multilingual situations and types of C-S) and lack of end-to- end systems that cover sociolinguistic aspects of C-S as well. Our survey will be a step to- wards an outcome of mutual benefit for computational scientists and linguists with a shared interest in multilingualism and C-S.", "author": "A. Seza Do\u011fru\u00f6z; Sunayana Sitaram; Barbara E. Bullock; Almeida Jacqueline Toribio", "authorids": "/a/a-seza-dogruoz/; /s/sunayana-sitaram/; /b/barbara-bullock/; /a/almeida-jacqueline-toribio/", "bibtex": "@inproceedings{dogruoz-etal-2021-survey,\n title = \"A Survey of Code-switching: Linguistic and Social Perspectives for Language Technologies\",\n author = {Do{\\u{g}}ru{\\\"o}z, A. Seza and\n Sitaram, Sunayana and\n Bullock, Barbara E. and\n Toribio, Almeida Jacqueline},\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.131/\",\n doi = \"10.18653/v1/2021.acl-long.131\",\n pages = \"1654--1666\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.131.pdf", "site": "https://aclanthology.org/2021.acl-long.131/", "pdf_size": 358721, "gs_citation": 78, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=11919702034120819444&as_sdt=400005&sciodt=0,14&hl=en", "gs_version_total": 7, "aff": "Ghent University, Gent, Belgium; Microsoft Research India, Bangalore, India; UT at Austin, Austin, USA; UT at Austin, Austin, USA", "aff_domain": "ugent.be;microsoft.com;austin.utexas.edu;austin.utexas.edu", "email": "ugent.be;microsoft.com;austin.utexas.edu;austin.utexas.edu", "github": "", "project": "", "author_num": 4, "aff_unique_index": "0;1;2;2", "aff_unique_norm": "Ghent University;Microsoft Research India;University of Texas at Austin", "aff_unique_dep": ";;", "aff_unique_url": "https://www.ugent.be/en;https://www.microsoft.com/en-us/research/group/microsoft-research-india;https://www.utexas.edu", "aff_unique_abbr": "UGent;MSRI;UT Austin", "aff_campus_unique_index": "0;1;2;2", "aff_campus_unique": "Gent;Bangalore;Austin", "aff_country_unique_index": "0;1;2;2", "aff_country_unique": "Belgium;India;United States" }, { "id": "2021.acl-long.149", "title": "A Survey of Race, Racism, and Anti-Racism in NLP", "track": "main", "status": "Long", "award": false, "abstract": "Despite inextricable ties between race and language, little work has considered race in NLP research and development. In this work, we survey 79 papers from the ACL anthology that mention race. These papers reveal various types of race-related bias in all stages of NLP model development, highlighting the need for proactive consideration of how NLP systems can uphold racial hierarchies. However, persistent gaps in research on race and NLP remain: race has been siloed as a niche topic and remains ignored in many NLP tasks; most work operationalizes race as a fixed single-dimensional variable with a ground-truth label, which risks reinforcing differences produced by historical racism; and the voices of historically marginalized people are nearly absent in NLP literature. By identifying where and how NLP literature has and has not considered race, especially in comparison to related fields, our work calls for inclusion and racial justice in NLP research practices.", "author": "Anjalie Field; Su Lin Blodgett; Zeerak Waseem; Yulia Tsvetkov", "authorids": "/a/anjalie-field/; /s/su-lin-blodgett/; /z/zeerak-talat/; /y/yulia-tsvetkov/", "bibtex": "@inproceedings{field-etal-2021-survey,\n title = \"A Survey of Race, Racism, and Anti-Racism in {NLP}\",\n author = \"Field, Anjalie and\n Blodgett, Su Lin and\n Waseem, Zeerak and\n Tsvetkov, Yulia\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.149/\",\n doi = \"10.18653/v1/2021.acl-long.149\",\n pages = \"1905--1925\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.149.pdf", "site": "https://aclanthology.org/2021.acl-long.149/", "pdf_size": 517228, "gs_citation": 138, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=4738573342998994081&as_sdt=40005&sciodt=0,10&hl=en", "gs_version_total": 5, "aff": "Carnegie Mellon University; Microsoft Research; University of Shef\ufb01eld; University of Washington", "aff_domain": "cs.cmu.edu;microsoft.com;sheffield.ac.uk;cs.washington.edu", "email": "cs.cmu.edu;microsoft.com;sheffield.ac.uk;cs.washington.edu", "github": "", "project": "", "author_num": 4, "aff_unique_index": "0;1;2;3", "aff_unique_norm": "Carnegie Mellon University;Microsoft Corporation;University of Sheffield;University of Washington", "aff_unique_dep": ";Microsoft Research;;", "aff_unique_url": "https://www.cmu.edu;https://www.microsoft.com/en-us/research;https://www.sheffield.ac.uk;https://www.washington.edu", "aff_unique_abbr": "CMU;MSR;Sheffield;UW", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0;0;1;0", "aff_country_unique": "United States;United Kingdom" }, { "id": "2021.acl-long.296", "title": "A Sweet Rabbit Hole by DARCY: Using Honeypots to Detect Universal Trigger\u2019s Adversarial Attacks", "track": "main", "status": "Long", "award": false, "abstract": "The Universal Trigger (UniTrigger) is a recently-proposed powerful adversarial textual attack method. Utilizing a learning-based mechanism, UniTrigger generates a fixed phrase that, when added to any benign inputs, can drop the prediction accuracy of a textual neural network (NN) model to near zero on a target class. To defend against this attack that can cause significant harm, in this paper, we borrow the \u201choneypot\u201d concept from the cybersecurity community and propose DARCY, a honeypot-based defense framework against UniTrigger. DARCY greedily searches and injects multiple trapdoors into an NN model to \u201cbait and catch\u201d potential attacks. Through comprehensive experiments across four public datasets, we show that DARCY detects UniTrigger\u2019s adversarial attacks with up to 99% TPR and less than 2% FPR in most cases, while maintaining the prediction accuracy (in F1) for clean inputs within a 1% margin. We also demonstrate that DARCY with multiple trapdoors is also robust to a diverse set of attack scenarios with attackers\u2019 varying levels of knowledge and skills. We release the source code of DARCY at: https://github.com/lethaiq/ACL2021-DARCY-HoneypotDefenseNLP.", "author": "Thai Le; Noseong Park; Dongwon Lee", "authorids": "/t/thai-le/; /n/noseong-park/; /d/dongwon-lee/", "bibtex": "@inproceedings{le-etal-2021-sweet,\n title = \"A Sweet Rabbit Hole by {DARCY}: Using Honeypots to Detect Universal Trigger`s Adversarial Attacks\",\n author = \"Le, Thai and\n Park, Noseong and\n Lee, Dongwon\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.296/\",\n doi = \"10.18653/v1/2021.acl-long.296\",\n pages = \"3831--3844\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.296.pdf", "site": "https://aclanthology.org/2021.acl-long.296/", "pdf_size": 4900739, "gs_citation": 28, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=12500114742063591394&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 9, "aff": "Penn State University; Yonsei University; Penn State University", "aff_domain": "psu.edu;yonsei.ac.kr;psu.edu", "email": "psu.edu;yonsei.ac.kr;psu.edu", "github": "https://github.com/lethaiq/ACL2021-DARCY-HoneypotDefenseNLP", "project": "", "author_num": 3, "aff_unique_index": "0;1;0", "aff_unique_norm": "Penn State University;Yonsei University", "aff_unique_dep": ";", "aff_unique_url": "https://www.psu.edu;https://www.yonsei.ac.kr", "aff_unique_abbr": "PSU;Yonsei", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0;1;0", "aff_country_unique": "United States;South Korea" }, { "id": "2021.acl-long.139", "title": "A Systematic Investigation of KB-Text Embedding Alignment at Scale", "track": "main", "status": "Long", "award": false, "abstract": "Knowledge bases (KBs) and text often contain complementary knowledge: KBs store structured knowledge that can support long range reasoning, while text stores more comprehensive and timely knowledge in an unstructured way. Separately embedding the individual knowledge sources into vector spaces has demonstrated tremendous successes in encoding the respective knowledge, but how to jointly embed and reason with both knowledge sources to fully leverage the complementary information is still largely an open problem. We conduct a large-scale, systematic investigation of aligning KB and text embeddings for joint reasoning. We set up a novel evaluation framework with two evaluation tasks, few-shot link prediction and analogical reasoning, and evaluate an array of KB-text embedding alignment methods. We also demonstrate how such alignment can infuse textual information into KB embeddings for more accurate link prediction on emerging entities and events, using COVID-19 as a case study.", "author": "Vardaan Pahuja; Yu Gu; Wenhu Chen; Mehdi Bahrami; Lei Liu; Wei-Peng Chen; Yu Su", "authorids": "/v/vardaan-pahuja/; /y/yu-gu/; /w/wenhu-chen/; /m/mehdi-bahrami/; /l/lei-liu/; /w/wei-peng-chen/; /y/yu-su/", "bibtex": "@inproceedings{pahuja-etal-2021-systematic,\n title = \"A Systematic Investigation of {KB}-Text Embedding Alignment at Scale\",\n author = \"Pahuja, Vardaan and\n Gu, Yu and\n Chen, Wenhu and\n Bahrami, Mehdi and\n Liu, Lei and\n Chen, Wei-Peng and\n Su, Yu\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.139/\",\n doi = \"10.18653/v1/2021.acl-long.139\",\n pages = \"1764--1774\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.139.pdf", "site": "https://aclanthology.org/2021.acl-long.139/", "pdf_size": 444083, "gs_citation": 3, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=13760672129131832421&as_sdt=5,44&sciodt=0,44&hl=en", "gs_version_total": 8, "aff": "The Ohio State University; The Ohio State University; University of California, Santa Barbara; Fujitsu Laboratories of America; Fujitsu Laboratories of America; Fujitsu Laboratories of America; The Ohio State University", "aff_domain": "osu.edu;osu.edu;cs.ucsb.edu;fujitsu.com;fujitsu.com;fujitsu.com;osu.edu", "email": "osu.edu;osu.edu;cs.ucsb.edu;fujitsu.com;fujitsu.com;fujitsu.com;osu.edu", "github": "https://github.com/dki-lab/joint-kb-text-embedding", "project": "", "author_num": 7, "aff_unique_index": "0;0;1;2;2;2;0", "aff_unique_norm": "The Ohio State University;University of California, Santa Barbara;Fujitsu Laboratories of America", "aff_unique_dep": ";;", "aff_unique_url": "https://www.osu.edu;https://www.ucsb.edu;https://www.fujitsu.com/us/group/labs/", "aff_unique_abbr": "OSU;UCSB;FLA", "aff_campus_unique_index": "1", "aff_campus_unique": ";Santa Barbara", "aff_country_unique_index": "0;0;0;0;0;0;0", "aff_country_unique": "United States" }, { "id": "2021.acl-long.76", "title": "A Targeted Assessment of Incremental Processing in Neural Language Models and Humans", "track": "main", "status": "Long", "award": false, "abstract": "We present a targeted, scaled-up comparison of incremental processing in humans and neural language models by collecting by-word reaction time data for sixteen different syntactic test suites across a range of structural phenomena. Human reaction time data comes from a novel online experimental paradigm called the Interpolated Maze task. We compare human reaction times to by-word probabilities for four contemporary language models, with different architectures and trained on a range of data set sizes. We find that across many phenomena, both humans and language models show increased processing difficulty in ungrammatical sentence regions with human and model \u2018accuracy\u2019 scores a la Marvin and Linzen (2018) about equal. However, although language model outputs match humans in direction, we show that models systematically under-predict the difference in magnitude of incremental processing difficulty between grammatical and ungrammatical sentences. Specifically, when models encounter syntactic violations they fail to accurately predict the longer reading times observed in the human data. These results call into question whether contemporary language models are approaching human-like performance for sensitivity to syntactic violations.", "author": "Ethan Wilcox; Pranali Vani; Roger Levy", "authorids": "/e/ethan-wilcox/; /p/pranali-vani/; /r/roger-levy/", "bibtex": "@inproceedings{wilcox-etal-2021-targeted,\n title = \"A Targeted Assessment of Incremental Processing in Neural Language Models and Humans\",\n author = \"Wilcox, Ethan and\n Vani, Pranali and\n Levy, Roger\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.76/\",\n doi = \"10.18653/v1/2021.acl-long.76\",\n pages = \"939--952\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.76.pdf", "site": "https://aclanthology.org/2021.acl-long.76/", "pdf_size": 614535, "gs_citation": 49, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=11627493597884207317&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 4, "aff": "Harvard University, Department of Linguistics; MIT, Brain and Cognitive Science; MIT, Brain and Cognitive Science", "aff_domain": "g.harvard.edu;mit.edu;mit.edu", "email": "g.harvard.edu;mit.edu;mit.edu", "github": "https://github.com/wilcoxeg/targeted-assessment-imaze", "project": "", "author_num": 3, "aff_unique_index": "0;1;1", "aff_unique_norm": "Harvard University;Massachusetts Institute of Technology", "aff_unique_dep": "Department of Linguistics;Brain and Cognitive Science", "aff_unique_url": "https://www.harvard.edu;https://www.mit.edu", "aff_unique_abbr": "Harvard;MIT", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0;0;0", "aff_country_unique": "United States" }, { "id": "2021.acl-long.34", "title": "A Training-free and Reference-free Summarization Evaluation Metric via Centrality-weighted Relevance and Self-referenced Redundancy", "track": "main", "status": "Long", "award": false, "abstract": "In recent years, reference-based and supervised summarization evaluation metrics have been widely explored. However, collecting human-annotated references and ratings are costly and time-consuming. To avoid these limitations, we propose a training-free and reference-free summarization evaluation metric. Our metric consists of a centrality-weighted relevance score and a self-referenced redundancy score. The relevance score is computed between the pseudo reference built from the source document and the given summary, where the pseudo reference content is weighted by the sentence centrality to provide importance guidance. Besides an F1-based relevance score, we also design an F\ud835\udefd-based variant that pays more attention to the recall score. As for the redundancy score of the summary, we compute a self-masked similarity score with the summary itself to evaluate the redundant information in the summary. Finally, we combine the relevance and redundancy scores to produce the final evaluation score of the given summary. Extensive experiments show that our methods can significantly outperform existing methods on both multi-document and single-document summarization evaluation. The source code is released at https://github.com/Chen-Wang-CUHK/Training-Free-and-Ref-Free-Summ-Evaluation.", "author": "Wang Chen; Piji Li; Irwin King", "authorids": "/w/wang-chen/; /p/piji-li/; /i/irwin-king/", "bibtex": "@inproceedings{chen-etal-2021-training,\n title = \"A Training-free and Reference-free Summarization Evaluation Metric via Centrality-weighted Relevance and Self-referenced Redundancy\",\n author = \"Chen, Wang and\n Li, Piji and\n King, Irwin\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.34/\",\n doi = \"10.18653/v1/2021.acl-long.34\",\n pages = \"404--414\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.34.pdf", "site": "https://aclanthology.org/2021.acl-long.34/", "pdf_size": 5071549, "gs_citation": 23, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=13009196286682259887&as_sdt=5,33&sciodt=0,33&hl=en", "gs_version_total": 7, "aff": "Department of Computer Science and Engineering, The Chinese University of Hong Kong, Shatin, N.T., Hong Kong + Tencent AI Lab; Tencent AI Lab; Department of Computer Science and Engineering, The Chinese University of Hong Kong, Shatin, N.T., Hong Kong", "aff_domain": "cse.cuhk.edu.hk;cse.cuhk.edu.hk;tencent.com", "email": "cse.cuhk.edu.hk;cse.cuhk.edu.hk;tencent.com", "github": "https://github.com/Chen-Wang-CUHK/Training-Free-and-Ref-Free-Summ-Evaluation", "project": "", "author_num": 3, "aff_unique_index": "0+1;1;0", "aff_unique_norm": "The Chinese University of Hong Kong;Tencent", "aff_unique_dep": "Department of Computer Science and Engineering;Tencent AI Lab", "aff_unique_url": "https://www.cuhk.edu.hk;https://ai.tencent.com", "aff_unique_abbr": "CUHK;Tencent AI Lab", "aff_campus_unique_index": "0;0", "aff_campus_unique": "Shatin;", "aff_country_unique_index": "0+0;0;0", "aff_country_unique": "China" }, { "id": "2021.acl-long.188", "title": "A Unified Generative Framework for Aspect-based Sentiment Analysis", "track": "main", "status": "Long", "award": false, "abstract": "Aspect-based Sentiment Analysis (ABSA) aims to identify the aspect terms, their corresponding sentiment polarities, and the opinion terms. There exist seven subtasks in ABSA. Most studies only focus on the subsets of these subtasks, which leads to various complicated ABSA models while hard to solve these subtasks in a unified framework. In this paper, we redefine every subtask target as a sequence mixed by pointer indexes and sentiment class indexes, which converts all ABSA subtasks into a unified generative formulation. Based on the unified formulation, we exploit the pre-training sequence-to-sequence model BART to solve all ABSA subtasks in an end-to-end framework. Extensive experiments on four ABSA datasets for seven subtasks demonstrate that our framework achieves substantial performance gain and provides a real unified end-to-end solution for the whole ABSA subtasks, which could benefit multiple tasks.", "author": "Hang Yan; Junqi Dai; Tuo Ji; Xipeng Qiu; Zheng Zhang", "authorids": "/h/hang-yan/; /j/junqi-dai/; /t/tuo-ji/; /x/xipeng-qiu/; /z/zheng-zhang/", "bibtex": "@inproceedings{yan-etal-2021-unified,\n title = \"A Unified Generative Framework for Aspect-based Sentiment Analysis\",\n author = \"Yan, Hang and\n Dai, Junqi and\n Ji, Tuo and\n Qiu, Xipeng and\n Zhang, Zheng\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.188/\",\n doi = \"10.18653/v1/2021.acl-long.188\",\n pages = \"2416--2429\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.188.pdf", "site": "https://aclanthology.org/2021.acl-long.188/", "pdf_size": 1469988, "gs_citation": 341, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=12478434582045516909&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 5, "aff": "Shanghai Key Laboratory of Intelligent Information Processing, Fudan University + School of Computer Science, Fudan University; Shanghai Key Laboratory of Intelligent Information Processing, Fudan University + School of Computer Science, Fudan University; Shanghai Key Laboratory of Intelligent Information Processing, Fudan University; Shanghai Key Laboratory of Intelligent Information Processing, Fudan University + Pazhou Lab, Guangzhou, China; New York University", "aff_domain": "fudan.edu.cn;fudan.edu.cn;fudan.edu.cn;fudan.edu.cn;nyu.edu", "email": "fudan.edu.cn;fudan.edu.cn;fudan.edu.cn;fudan.edu.cn;nyu.edu", "github": "https://github.com/yhcc/BARTABSA", "project": "", "author_num": 5, "aff_unique_index": "0+0;0+0;0;0+1;2", "aff_unique_norm": "Fudan University;Pazhou Lab;New York University", "aff_unique_dep": "Shanghai Key Laboratory of Intelligent Information Processing;;", "aff_unique_url": "https://www.fudan.edu.cn;;https://www.nyu.edu", "aff_unique_abbr": "Fudan;;NYU", "aff_campus_unique_index": "0;0;0;0+2", "aff_campus_unique": "Shanghai;;Guangzhou", "aff_country_unique_index": "0+0;0+0;0;0+0;1", "aff_country_unique": "China;United States" }, { "id": "2021.acl-long.451", "title": "A Unified Generative Framework for Various NER Subtasks", "track": "main", "status": "Long", "award": false, "abstract": "Named Entity Recognition (NER) is the task of identifying spans that represent entities in sentences. Whether the entity spans are nested or discontinuous, the NER task can be categorized into the flat NER, nested NER, and discontinuous NER subtasks. These subtasks have been mainly solved by the token-level sequence labelling or span-level classification. However, these solutions can hardly tackle the three kinds of NER subtasks concurrently. To that end, we propose to formulate the NER subtasks as an entity span sequence generation task, which can be solved by a unified sequence-to-sequence (Seq2Seq) framework. Based on our unified framework, we can leverage the pre-trained Seq2Seq model to solve all three kinds of NER subtasks without the special design of the tagging schema or ways to enumerate spans. We exploit three types of entity representations to linearize entities into a sequence. Our proposed framework is easy-to-implement and achieves state-of-the-art (SoTA) or near SoTA performance on eight English NER datasets, including two flat NER datasets, three nested NER datasets, and three discontinuous NER datasets.", "author": "Hang Yan; Tao Gui; Junqi Dai; Qipeng Guo; Zheng Zhang; Xipeng Qiu", "authorids": "/h/hang-yan/; /t/tao-gui/; /j/junqi-dai/; /q/qipeng-guo/; /z/zheng-zhang/; /x/xipeng-qiu/", "bibtex": "@inproceedings{yan-etal-2021-unified-generative,\n title = \"A Unified Generative Framework for Various {NER} Subtasks\",\n author = \"Yan, Hang and\n Gui, Tao and\n Dai, Junqi and\n Guo, Qipeng and\n Zhang, Zheng and\n Qiu, Xipeng\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.451/\",\n doi = \"10.18653/v1/2021.acl-long.451\",\n pages = \"5808--5822\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.451.pdf", "site": "https://aclanthology.org/2021.acl-long.451/", "pdf_size": 679654, "gs_citation": 333, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=4947145306568942590&as_sdt=5,34&sciodt=0,34&hl=en", "gs_version_total": 5, "aff": "Shanghai Key Laboratory of Intelligent Information Processing, Fudan University+School of Computer Science, Fudan University; Institute of Modern Languages and Linguistics, Fudan University; Shanghai Key Laboratory of Intelligent Information Processing, Fudan University+School of Computer Science, Fudan University; Shanghai Key Laboratory of Intelligent Information Processing, Fudan University+School of Computer Science, Fudan University; New York University; Shanghai Key Laboratory of Intelligent Information Processing, Fudan University+School of Computer Science, Fudan University+Pazhou Lab, Guangzhou, China", "aff_domain": "fudan.edu.cn;fudan.edu.cn;fudan.edu.cn;fudan.edu.cn;nyu.edu;fudan.edu.cn", "email": "fudan.edu.cn;fudan.edu.cn;fudan.edu.cn;fudan.edu.cn;nyu.edu;fudan.edu.cn", "github": "https://github.com/yhcc/BARTNER", "project": "", "author_num": 6, "aff_unique_index": "0+0;0;0+0;0+0;1;0+0+2", "aff_unique_norm": "Fudan University;New York University;Pazhou Lab", "aff_unique_dep": "Shanghai Key Laboratory of Intelligent Information Processing;;", "aff_unique_url": "https://www.fudan.edu.cn;https://www.nyu.edu;", "aff_unique_abbr": "Fudan;NYU;", "aff_campus_unique_index": "0;0;0;0+2", "aff_campus_unique": "Shanghai;;Guangzhou", "aff_country_unique_index": "0+0;0;0+0;0+0;1;0+0+0", "aff_country_unique": "China;United States" }, { "id": "2021.acl-long.309", "title": "A unified approach to sentence segmentation of punctuated text in many languages", "track": "main", "status": "Long", "award": false, "abstract": "The sentence is a fundamental unit of text processing. Yet sentences in the wild are commonly encountered not in isolation, but unsegmented within larger paragraphs and documents. Therefore, the first step in many NLP pipelines is sentence segmentation. Despite its importance, this step is the subject of relatively little research. There are no standard test sets or even methods for evaluation, leaving researchers and engineers without a clear footing for evaluating and selecting models for the task. Existing tools have relatively small language coverage, and efforts to extend them to other languages are often ad hoc. We introduce a modern context-based modeling approach that provides a solution to the problem of segmenting punctuated text in many languages, and show how it can be trained on noisily-annotated data. We also establish a new 23-language multilingual evaluation set. Our approach exceeds high baselines set by existing methods on prior English corpora (WSJ and Brown corpora), and also performs well on average on our new evaluation set. We release our tool, ersatz, as open source.", "author": "Rachel Wicks; Matt Post", "authorids": "/r/rachel-wicks/; /m/matt-post/", "bibtex": "@inproceedings{wicks-post-2021-unified,\n title = \"A unified approach to sentence segmentation of punctuated text in many languages\",\n author = \"Wicks, Rachel and\n Post, Matt\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.309/\",\n doi = \"10.18653/v1/2021.acl-long.309\",\n pages = \"3995--4007\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.309.pdf", "site": "https://aclanthology.org/2021.acl-long.309/", "pdf_size": 366573, "gs_citation": 36, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=6024984806310850816&as_sdt=5,33&sciodt=0,33&hl=en", "gs_version_total": 2, "aff": "Center for Language and Speech Processing; Center for Language and Speech Processing + Human Language Technology Center of Excellence", "aff_domain": "jhu.edu;cs.jhu.edu", "email": "jhu.edu;cs.jhu.edu", "github": "", "project": "", "author_num": 2, "aff_unique_index": "0;0+1", "aff_unique_norm": "Center for Language and Speech Processing;Human Language Technology Center of Excellence", "aff_unique_dep": "Department of Language and Speech Processing;", "aff_unique_url": ";", "aff_unique_abbr": "CLSP;", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0;0+0", "aff_country_unique": "United States" }, { "id": "2021.acl-long.303", "title": "ABCD: A Graph Framework to Convert Complex Sentences to a Covering Set of Simple Sentences", "track": "main", "status": "Long", "award": false, "abstract": "Atomic clauses are fundamental text units for understanding complex sentences. Identifying the atomic sentences within complex sentences is important for applications such as summarization, argument mining, discourse analysis, discourse parsing, and question answering. Previous work mainly relies on rule-based methods dependent on parsing. We propose a new task to decompose each complex sentence into simple sentences derived from the tensed clauses in the source, and a novel problem formulation as a graph edit task. Our neural model learns to Accept, Break, Copy or Drop elements of a graph that combines word adjacency and grammatical dependencies. The full processing pipeline includes modules for graph construction, graph editing, and sentence generation from the output graph. We introduce DeSSE, a new dataset designed to train and evaluate complex sentence decomposition, and MinWiki, a subset of MinWikiSplit. ABCD achieves comparable performance as two parsing baselines on MinWiki. On DeSSE, which has a more even balance of complex sentence types, our model achieves higher accuracy on the number of atomic sentences than an encoder-decoder baseline. Results include a detailed error analysis.", "author": "Yanjun Gao; Ting-Hao Huang; Rebecca J. Passonneau", "authorids": "/y/yanjun-gao/; /t/ting-hao-huang/; /r/rebecca-j-passonneau/", "bibtex": "@inproceedings{gao-etal-2021-abcd,\n title = \"{ABCD}: A Graph Framework to Convert Complex Sentences to a Covering Set of Simple Sentences\",\n author = \"Gao, Yanjun and\n Huang, Ting-Hao and\n Passonneau, Rebecca J.\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.303/\",\n doi = \"10.18653/v1/2021.acl-long.303\",\n pages = \"3919--3931\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.303.pdf", "site": "https://aclanthology.org/2021.acl-long.303/", "pdf_size": 1677873, "gs_citation": 14, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=11134765917105451817&as_sdt=400005&sciodt=0,14&hl=en", "gs_version_total": 7, "aff": "Pennsylvania State University; Pennsylvania State University; Pennsylvania State University", "aff_domain": "psu.edu;psu.edu;psu.edu", "email": "psu.edu;psu.edu;psu.edu", "github": "", "project": "", "author_num": 3, "aff_unique_index": "0;0;0", "aff_unique_norm": "Pennsylvania State University", "aff_unique_dep": "", "aff_unique_url": "https://www.psu.edu", "aff_unique_abbr": "PSU", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0;0;0", "aff_country_unique": "United States" }, { "id": "2021.acl-long.553", "title": "ADEPT: An Adjective-Dependent Plausibility Task", "track": "main", "status": "Long", "award": false, "abstract": "A false contract is more likely to be rejected than a contract is, yet a false key is less likely than a key to open doors. While correctly interpreting and assessing the effects of such adjective-noun pairs (e.g., false key) on the plausibility of given events (e.g., opening doors) underpins many natural language understanding tasks, doing so often requires a significant degree of world knowledge and common-sense reasoning. We introduce ADEPT \u2013 a large-scale semantic plausibility task consisting of over 16 thousand sentences that are paired with slightly modified versions obtained by adding an adjective to a noun. Overall, we find that while the task appears easier for human judges (85% accuracy), it proves more difficult for transformer-based models like RoBERTa (71% accuracy). Our experiments also show that neither the adjective itself nor its taxonomic class suffice in determining the correct plausibility judgement, emphasizing the importance of endowing automatic natural language understanding systems with more context sensitivity and common-sense reasoning.", "author": "Ali Emami; Ian Porada; Alexandra Olteanu; Kaheer Suleman; Adam Trischler; Jackie Chi Kit Cheung", "authorids": "/a/ali-emami/; /i/ian-porada/; /a/alexandra-olteanu/; /k/kaheer-suleman/; /a/adam-trischler/; /j/jackie-chi-kit-cheung/", "bibtex": "@inproceedings{emami-etal-2021-adept,\n title = \"{ADEPT}: An Adjective-Dependent Plausibility Task\",\n author = \"Emami, Ali and\n Porada, Ian and\n Olteanu, Alexandra and\n Suleman, Kaheer and\n Trischler, Adam and\n Cheung, Jackie Chi Kit\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.553/\",\n doi = \"10.18653/v1/2021.acl-long.553\",\n pages = \"7117--7128\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.553.pdf", "site": "https://aclanthology.org/2021.acl-long.553/", "pdf_size": 305686, "gs_citation": 3, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=12774554402252848191&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 2, "aff": "Mila, McGill University; Mila, McGill University; Microsoft Research Montr \u00b4eal; Microsoft Research Montr \u00b4eal; Microsoft Research Montr \u00b4eal; Mila, McGill University", "aff_domain": "mail.mcgill.ca;mail.mcgill.ca;microsoft.com;microsoft.com;microsoft.com;cs.mcgill.ca", "email": "mail.mcgill.ca;mail.mcgill.ca;microsoft.com;microsoft.com;microsoft.com;cs.mcgill.ca", "github": "", "project": "", "author_num": 6, "aff_unique_index": "0;0;1;1;1;0", "aff_unique_norm": "McGill University;Microsoft Research", "aff_unique_dep": "Mila;Microsoft Research", "aff_unique_url": "https://www.mcgill.ca;https://www.microsoft.com/en-us/research/group/microsoft-research-montreal", "aff_unique_abbr": "McGill;MSR Montreal", "aff_campus_unique_index": "0;0;0;0;0;0", "aff_campus_unique": "Montreal", "aff_country_unique_index": "0;0;0;0;0;0", "aff_country_unique": "Canada" }, { "id": "2021.acl-short.21", "title": "AND does not mean OR: Using Formal Languages to Study Language Models\u2019 Representations", "track": "main", "status": "Short", "award": false, "abstract": "A current open question in natural language processing is to what extent language models, which are trained with access only to the form of language, are able to capture the meaning of language. This question is challenging to answer in general, as there is no clear line between meaning and form, but rather meaning constrains form in consistent ways. The goal of this study is to offer insights into a narrower but critical subquestion: Under what conditions should we expect that meaning and form covary sufficiently, such that a language model with access only to form might nonetheless succeed in emulating meaning? Focusing on several formal languages (propositional logic and a set of programming languages), we generate training corpora using a variety of motivated constraints, and measure a distributional language model\u2019s ability to differentiate logical symbols (AND, OR, and NOT). Our findings are largely negative: none of our simulated training corpora result in models which definitively differentiate meaningfully different symbols (e.g., AND vs. OR), suggesting a limitation to the types of semantic signals that current models are able to exploit.", "author": "Aaron Traylor; Roman Feiman; Ellie Pavlick", "authorids": "/a/aaron-traylor/; /r/roman-feiman/; /e/ellie-pavlick/", "bibtex": "@inproceedings{traylor-etal-2021-mean,\n title = \"{AND} does not mean {OR}: Using Formal Languages to Study Language Models' Representations\",\n author = \"Traylor, Aaron and\n Feiman, Roman and\n Pavlick, Ellie\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-short.21/\",\n doi = \"10.18653/v1/2021.acl-short.21\",\n pages = \"158--167\"\n}", "pdf": "https://aclanthology.org/2021.acl-short.21.pdf", "site": "https://aclanthology.org/2021.acl-short.21/", "pdf_size": 310871, "gs_citation": 25, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=17247672968925824178&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 2, "aff": "Dept. of Computer Science, Brown University; Dept. of Cognitive, Linguistic, and Psychological Sciences, Brown University; Dept. of Computer Science, Brown University", "aff_domain": "brown.edu;brown.edu;brown.edu", "email": "brown.edu;brown.edu;brown.edu", "github": "", "project": "", "author_num": 3, "aff_unique_index": "0;0;0", "aff_unique_norm": "Brown University", "aff_unique_dep": "Dept. of Computer Science", "aff_unique_url": "https://www.brown.edu", "aff_unique_abbr": "Brown", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0;0;0", "aff_country_unique": "United States" }, { "id": "2021.acl-long.551", "title": "ARBERT & MARBERT: Deep Bidirectional Transformers for Arabic", "track": "main", "status": "Long", "award": false, "abstract": "Pre-trained language models (LMs) are currently integral to many natural language processing systems. Although multilingual LMs were also introduced to serve many languages, these have limitations such as being costly at inference time and the size and diversity of non-English data involved in their pre-training. We remedy these issues for a collection of diverse Arabic varieties by introducing two powerful deep bidirectional transformer-based models, ARBERT and MARBERT. To evaluate our models, we also introduce ARLUE, a new benchmark for multi-dialectal Arabic language understanding evaluation. ARLUE is built using 42 datasets targeting six different task clusters, allowing us to offer a series of standardized experiments under rich conditions. When fine-tuned on ARLUE, our models collectively achieve new state-of-the-art results across the majority of tasks (37 out of 48 classification tasks, on the 42 datasets). Our best model acquires the highest ARLUE score (77.40) across all six task clusters, outperforming all other models including XLM-R Large ( 3.4x larger size). Our models are publicly available at https://github.com/UBC-NLP/marbert and ARLUE will be released through the same repository.", "author": "Muhammad Abdul-Mageed; AbdelRahim Elmadany; El Moatez Billah Nagoudi", "authorids": "/m/muhammad-abdul-mageed/; /a/abdelrahim-elmadany/; /e/el-moatez-billah-nagoudi/", "bibtex": "@inproceedings{abdul-mageed-etal-2021-arbert,\n title = \"{ARBERT} {\\&} {MARBERT}: Deep Bidirectional Transformers for {A}rabic\",\n author = \"Abdul-Mageed, Muhammad and\n Elmadany, AbdelRahim and\n Nagoudi, El Moatez Billah\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.551/\",\n doi = \"10.18653/v1/2021.acl-long.551\",\n pages = \"7088--7105\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.551.pdf", "site": "https://aclanthology.org/2021.acl-long.551/", "pdf_size": 546892, "gs_citation": 532, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=7679832997388768696&as_sdt=5,33&sciodt=0,33&hl=en", "gs_version_total": 4, "aff": "Natural Language Processing Lab, The University of British Columbia; Natural Language Processing Lab, The University of British Columbia; Natural Language Processing Lab, The University of British Columbia", "aff_domain": "ubc.ca;ubc.ca;ubc.ca", "email": "ubc.ca;ubc.ca;ubc.ca", "github": "https://github.com/UBC-NLP/marbert", "project": "", "author_num": 3, "aff_unique_index": "0;0;0", "aff_unique_norm": "The University of British Columbia", "aff_unique_dep": "Natural Language Processing Lab", "aff_unique_url": "https://www.ubc.ca", "aff_unique_abbr": "UBC", "aff_campus_unique_index": "0;0;0", "aff_campus_unique": "Vancouver", "aff_country_unique_index": "0;0;0", "aff_country_unique": "Canada" }, { "id": "2021.acl-long.16", "title": "Accelerating BERT Inference for Sequence Labeling via Early-Exit", "track": "main", "status": "Long", "award": false, "abstract": "Both performance and efficiency are crucial factors for sequence labeling tasks in many real-world scenarios. Although the pre-trained models (PTMs) have significantly improved the performance of various sequence labeling tasks, their computational cost is expensive. To alleviate this problem, we extend the recent successful early-exit mechanism to accelerate the inference of PTMs for sequence labeling tasks. However, existing early-exit mechanisms are specifically designed for sequence-level tasks, rather than sequence labeling. In this paper, we first propose a simple extension of sentence-level early-exit for sequence labeling tasks. To further reduce the computational cost, we also propose a token-level early-exit mechanism that allows partial tokens to exit early at different layers. Considering the local dependency inherent in sequence labeling, we employed a window-based criterion to decide for a token whether or not to exit. The token-level early-exit brings the gap between training and inference, so we introduce an extra self-sampling fine-tuning stage to alleviate it. The extensive experiments on three popular sequence labeling tasks show that our approach can save up to 66%\u223c75% inference cost with minimal performance degradation. Compared with competitive compressed models such as DistilBERT, our approach can achieve better performance under the same speed-up ratios of 2\u00d7, 3\u00d7, and 4\u00d7.", "author": "Xiaonan Li; Yunfan Shao; Tianxiang Sun; Hang Yan; Xipeng Qiu; Xuanjing Huang", "authorids": "/x/xiaonan-li/; /y/yunfan-shao/; /t/tianxiang-sun/; /h/hang-yan/; /x/xipeng-qiu/; /x/xuan-jing-huang/", "bibtex": "@inproceedings{li-etal-2021-accelerating,\n title = \"Accelerating {BERT} Inference for Sequence Labeling via Early-Exit\",\n author = \"Li, Xiaonan and\n Shao, Yunfan and\n Sun, Tianxiang and\n Yan, Hang and\n Qiu, Xipeng and\n Huang, Xuanjing\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.16/\",\n doi = \"10.18653/v1/2021.acl-long.16\",\n pages = \"189--199\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.16.pdf", "site": "https://aclanthology.org/2021.acl-long.16/", "pdf_size": 888973, "gs_citation": 42, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=12392578931523635166&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 4, "aff": "Shanghai Key Laboratory of Intelligent Information Processing, Fudan University+School of Computer Science, Fudan University; Shanghai Key Laboratory of Intelligent Information Processing, Fudan University+School of Computer Science, Fudan University; Shanghai Key Laboratory of Intelligent Information Processing, Fudan University+School of Computer Science, Fudan University; Shanghai Key Laboratory of Intelligent Information Processing, Fudan University+School of Computer Science, Fudan University; Shanghai Key Laboratory of Intelligent Information Processing, Fudan University+School of Computer Science, Fudan University; Shanghai Key Laboratory of Intelligent Information Processing, Fudan University+School of Computer Science, Fudan University", "aff_domain": "fudan.edu.cn;fudan.edu.cn;fudan.edu.cn;fudan.edu.cn;fudan.edu.cn;fudan.edu.cn", "email": "fudan.edu.cn;fudan.edu.cn;fudan.edu.cn;fudan.edu.cn;fudan.edu.cn;fudan.edu.cn", "github": "https://github.com/LeeSureman/Sequence-Labeling-Early-Exit", "project": "", "author_num": 6, "aff_unique_index": "0+0;0+0;0+0;0+0;0+0;0+0", "aff_unique_norm": "Fudan University", "aff_unique_dep": "Shanghai Key Laboratory of Intelligent Information Processing", "aff_unique_url": "https://www.fudan.edu.cn", "aff_unique_abbr": "Fudan", "aff_campus_unique_index": "0;0;0;0;0;0", "aff_campus_unique": "Shanghai;", "aff_country_unique_index": "0+0;0+0;0+0;0+0;0+0;0+0", "aff_country_unique": "China" }, { "id": "2021.acl-long.514", "title": "Accelerating Text Communication via Abbreviated Sentence Input", "track": "main", "status": "Long", "award": false, "abstract": "Typing every character in a text message may require more time or effort than strictly necessary. Skipping spaces or other characters may be able to speed input and reduce a user\u2019s physical input effort. This can be particularly important for people with motor impairments. In a large crowdsourced study, we found workers frequently abbreviated text by omitting mid-word vowels. We designed a recognizer optimized for expanding noisy abbreviated input where users often omit spaces and mid-word vowels. We show using neural language models for selecting conversational-style training text and for rescoring the recognizer\u2019s n-best sentences improved accuracy. On noisy touchscreen data collected from hundreds of users, we found accurate abbreviated input was possible even if a third of characters was omitted. Finally, in a study where users had to dwell for a second on each key, sentence abbreviated input was competitive with a conventional keyboard with word predictions. After practice, users wrote abbreviated sentences at 9.6 words-per-minute versus word input at 9.9 words-per-minute.", "author": "Jiban Adhikary; Jamie Berger; Keith Vertanen", "authorids": "/j/jiban-adhikary/; /j/jamie-berger/; /k/keith-vertanen/", "bibtex": "@inproceedings{adhikary-etal-2021-accelerating,\n title = \"Accelerating Text Communication via Abbreviated Sentence Input\",\n author = \"Adhikary, Jiban and\n Berger, Jamie and\n Vertanen, Keith\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.514/\",\n doi = \"10.18653/v1/2021.acl-long.514\",\n pages = \"6574--6588\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.514.pdf", "site": "https://aclanthology.org/2021.acl-long.514/", "pdf_size": 897528, "gs_citation": 17, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=6195186498730423125&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 7, "aff": "Michigan Technological University, Houghton, Michigan, USA; Washington Leadership Academy, Washington, DC, USA; Michigan Technological University, Houghton, Michigan, USA", "aff_domain": "mtu.edu;gmail.com;mtu.edu", "email": "mtu.edu;gmail.com;mtu.edu", "github": "", "project": "", "author_num": 3, "aff_unique_index": "0;1;0", "aff_unique_norm": "Michigan Technological University;Washington Leadership Academy", "aff_unique_dep": ";", "aff_unique_url": "https://www.mtu.edu;", "aff_unique_abbr": "MTU;", "aff_campus_unique_index": "0;1;0", "aff_campus_unique": "Houghton;Washington, DC", "aff_country_unique_index": "0;0;0", "aff_country_unique": "United States" }, { "id": "2021.acl-long.362", "title": "AdaTag: Multi-Attribute Value Extraction from Product Profiles with Adaptive Decoding", "track": "main", "status": "Long", "award": false, "abstract": "Automatic extraction of product attribute values is an important enabling technology in e-Commerce platforms. This task is usually modeled using sequence labeling architectures, with several extensions to handle multi-attribute extraction. One line of previous work constructs attribute-specific models, through separate decoders or entirely separate models. However, this approach constrains knowledge sharing across different attributes. Other contributions use a single multi-attribute model, with different techniques to embed attribute information. But sharing the entire network parameters across all attributes can limit the model\u2019s capacity to capture attribute-specific characteristics. In this paper we present AdaTag, which uses adaptive decoding to handle extraction. We parameterize the decoder with pretrained attribute embeddings, through a hypernetwork and a Mixture-of-Experts (MoE) module. This allows for separate, but semantically correlated, decoders to be generated on the fly for different attributes. This approach facilitates knowledge sharing, while maintaining the specificity of each attribute. Our experiments on a real-world e-Commerce dataset show marked improvements over previous methods.", "author": "Jun Yan; Nasser Zalmout; Yan Liang; Christan Grant; Xiang Ren; Xin Luna Dong", "authorids": "/j/jun-yan/; /n/nasser-zalmout/; /y/yan-liang/; /c/christan-grant/; /x/xiang-ren/; /x/xin-luna-dong/", "bibtex": "@inproceedings{yan-etal-2021-adatag,\n title = \"{A}da{T}ag: Multi-Attribute Value Extraction from Product Profiles with Adaptive Decoding\",\n author = \"Yan, Jun and\n Zalmout, Nasser and\n Liang, Yan and\n Grant, Christan and\n Ren, Xiang and\n Dong, Xin Luna\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.362/\",\n doi = \"10.18653/v1/2021.acl-long.362\",\n pages = \"4694--4705\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.362.pdf", "site": "https://aclanthology.org/2021.acl-long.362/", "pdf_size": 766589, "gs_citation": 56, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=17971155295440136509&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 5, "aff": "University of Southern California1; Amazon.com2; Amazon.com2; University of Oklahoma3; University of Southern California1; Amazon.com2", "aff_domain": "usc.edu;amazon.com;amazon.com;ou.edu;usc.edu;amazon.com", "email": "usc.edu;amazon.com;amazon.com;ou.edu;usc.edu;amazon.com", "github": "", "project": "", "author_num": 6, "aff_unique_index": "0;1;1;2;0;1", "aff_unique_norm": "University of Southern California;Amazon.com;University of Oklahoma", "aff_unique_dep": ";;", "aff_unique_url": "https://www.usc.edu;https://www.amazon.com;https://www.ou.edu", "aff_unique_abbr": "USC;Amazon;OU", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0;0;0;0;0;0", "aff_country_unique": "United States" }, { "id": "2021.acl-long.66", "title": "Adapting High-resource NMT Models to Translate Low-resource Related Languages without Parallel Data", "track": "main", "status": "Long", "award": false, "abstract": "The scarcity of parallel data is a major obstacle for training high-quality machine translation systems for low-resource languages. Fortunately, some low-resource languages are linguistically related or similar to high-resource languages; these related languages may share many lexical or syntactic structures. In this work, we exploit this linguistic overlap to facilitate translating to and from a low-resource language with only monolingual data, in addition to any parallel data in the related high-resource language. Our method, NMT-Adapt, combines denoising autoencoding, back-translation and adversarial objectives to utilize monolingual data for low-resource adaptation. We experiment on 7 languages from three different language families and show that our technique significantly improves translation into low-resource language compared to other translation baselines.", "author": "Wei-Jen Ko; Ahmed El-Kishky; Adithya Renduchintala; Vishrav Chaudhary; Naman Goyal; Francisco Guzm\u00e1n; Pascale Fung; Philipp Koehn; Mona Diab", "authorids": "/w/wei-jen-ko/; /a/ahmed-el-kishky/; /a/adithya-renduchintala/; /v/vishrav-chaudhary/; /n/naman-goyal/; /f/francisco-guzman/; /p/pascale-fung/; /p/philipp-koehn/; /m/mona-diab/", "bibtex": "@inproceedings{ko-etal-2021-adapting,\n title = \"Adapting High-resource {NMT} Models to Translate Low-resource Related Languages without Parallel Data\",\n author = \"Ko, Wei-Jen and\n El-Kishky, Ahmed and\n Renduchintala, Adithya and\n Chaudhary, Vishrav and\n Goyal, Naman and\n Guzm{\\'a}n, Francisco and\n Fung, Pascale and\n Koehn, Philipp and\n Diab, Mona\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.66/\",\n doi = \"10.18653/v1/2021.acl-long.66\",\n pages = \"802--812\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.66.pdf", "site": "https://aclanthology.org/2021.acl-long.66/", "pdf_size": 944349, "gs_citation": 44, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=104650595598673149&as_sdt=5,44&sciodt=0,44&hl=en", "gs_version_total": 10, "aff": "University of Texas at Austin; Twitter Cortex; Facebook AI; The Hong Kong University of Science and Technology; Johns Hopkins University; Facebook AI; Facebook AI; Facebook AI; Facebook AI", "aff_domain": "utexas.edu;twitter.com;fb.com;fb.com;fb.com;fb.com;ece.ust.hk;jhu.edu;fb.com", "email": "utexas.edu;twitter.com;fb.com;fb.com;fb.com;fb.com;ece.ust.hk;jhu.edu;fb.com", "github": "", "project": "", "author_num": 9, "aff_unique_index": "0;1;2;3;4;2;2;2;2", "aff_unique_norm": "University of Texas at Austin;Twitter;Facebook;Hong Kong University of Science and Technology;Johns Hopkins University", "aff_unique_dep": ";Cortex;Facebook AI;;", "aff_unique_url": "https://www.utexas.edu;https://twitter.com;https://www.facebook.com;https://www.ust.hk;https://www.jhu.edu", "aff_unique_abbr": "UT Austin;Twitter;Facebook AI;HKUST;JHU", "aff_campus_unique_index": "0", "aff_campus_unique": "Austin;", "aff_country_unique_index": "0;0;0;1;0;0;0;0;0", "aff_country_unique": "United States;China" }, { "id": "2021.acl-long.449", "title": "Adapting Unsupervised Syntactic Parsing Methodology for Discourse Dependency Parsing", "track": "main", "status": "Long", "award": false, "abstract": "One of the main bottlenecks in developing discourse dependency parsers is the lack of annotated training data. A potential solution is to utilize abundant unlabeled data by using unsupervised techniques, but there is so far little research in unsupervised discourse dependency parsing. Fortunately, unsupervised syntactic dependency parsing has been studied by decades, which could potentially be adapted for discourse parsing. In this paper, we propose a simple yet effective method to adapt unsupervised syntactic dependency parsing methodology for unsupervised discourse dependency parsing. We apply the method to adapt two state-of-the-art unsupervised syntactic dependency parsing methods. Experimental results demonstrate that our adaptation is effective. Moreover, we extend the adapted methods to the semi-supervised and supervised setting and surprisingly, we find that they outperform previous methods specially designed for supervised discourse parsing. Further analysis shows our adaptations result in superiority not only in parsing accuracy but also in time and space efficiency.", "author": "Liwen Zhang; Ge Wang; Wenjuan Han; Kewei Tu", "authorids": "/l/liwen-zhang/; /g/ge-wang/; /w/wenjuan-han/; /k/kewei-tu/", "bibtex": "@inproceedings{zhang-etal-2021-adapting,\n title = \"Adapting Unsupervised Syntactic Parsing Methodology for Discourse Dependency Parsing\",\n author = \"Zhang, Liwen and\n Wang, Ge and\n Han, Wenjuan and\n Tu, Kewei\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.449/\",\n doi = \"10.18653/v1/2021.acl-long.449\",\n pages = \"5782--5794\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.449.pdf", "site": "https://aclanthology.org/2021.acl-long.449/", "pdf_size": 2054269, "gs_citation": 12, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=9506261262262193534&as_sdt=5,48&sciodt=0,48&hl=en", "gs_version_total": 4, "aff": "School of Information Science and Technology, ShanghaiTech University+Shanghai Engineering Research Center of Intelligent Vision and Imaging+Shanghai Institute of Microsystem and Information Technology+University of Chinese Academy of Sciences; School of Information Science and Technology, ShanghaiTech University+Shanghai Engineering Research Center of Intelligent Vision and Imaging+Shanghai Institute of Microsystem and Information Technology+University of Chinese Academy of Sciences; Beijing Institute for General Artificial Intelligence, Beijing, China; School of Information Science and Technology, ShanghaiTech University", "aff_domain": "shanghaitech.edu.cn;shanghaitech.edu.cn;bigai.ai;shanghaitech.edu.cn", "email": "shanghaitech.edu.cn;shanghaitech.edu.cn;bigai.ai;shanghaitech.edu.cn", "github": "", "project": "", "author_num": 4, "aff_unique_index": "0+1+2+3;0+1+2+3;4;0", "aff_unique_norm": "ShanghaiTech University;Shanghai Engineering Research Center of Intelligent Vision and Imaging;Shanghai Institute of Microsystem and Information Technology;University of Chinese Academy of Sciences;Beijing Institute for General Artificial Intelligence", "aff_unique_dep": "School of Information Science and Technology;;;;", "aff_unique_url": "https://www.shanghaitech.edu.cn;;http://www.simt.ac.cn;http://www.ucas.ac.cn;", "aff_unique_abbr": "ShanghaiTech;;;UCAS;", "aff_campus_unique_index": "0;0;2;0", "aff_campus_unique": "Shanghai;;Beijing", "aff_country_unique_index": "0+0+0+0;0+0+0+0;0;0", "aff_country_unique": "China" }, { "id": "2021.acl-short.47", "title": "Adaptive Nearest Neighbor Machine Translation", "track": "main", "status": "Short", "award": false, "abstract": "kNN-MT, recently proposed by Khandelwal et al. (2020a), successfully combines pre-trained neural machine translation (NMT) model with token-level k-nearest-neighbor (kNN) retrieval to improve the translation accuracy. However, the traditional kNN algorithm used in kNN-MT simply retrieves a same number of nearest neighbors for each target token, which may cause prediction errors when the retrieved neighbors include noises. In this paper, we propose Adaptive kNN-MT to dynamically determine the number of k for each target token. We achieve this by introducing a light-weight Meta-k Network, which can be efficiently trained with only a few training samples. On four benchmark machine translation datasets, we demonstrate that the proposed method is able to effectively filter out the noises in retrieval results and significantly outperforms the vanilla kNN-MT model. Even more noteworthy is that the Meta-k Network learned on one domain could be directly applied to other domains and obtain consistent improvements, illustrating the generality of our method. Our implementation is open-sourced at https://github.com/zhengxxn/adaptive-knn-mt.", "author": "Xin Zheng; Zhirui Zhang; Junliang Guo; Shujian Huang; Boxing Chen; Weihua Luo; Jiajun Chen", "authorids": "/x/xin-zheng/; /z/zhirui-zhang/; /j/junliang-guo/; /s/shujian-huang/; /b/boxing-chen/; /w/weihua-luo/; /j/jiajun-chen/", "bibtex": "@inproceedings{zheng-etal-2021-adaptive,\n title = \"Adaptive Nearest Neighbor Machine Translation\",\n author = \"Zheng, Xin and\n Zhang, Zhirui and\n Guo, Junliang and\n Huang, Shujian and\n Chen, Boxing and\n Luo, Weihua and\n Chen, Jiajun\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-short.47/\",\n doi = \"10.18653/v1/2021.acl-short.47\",\n pages = \"368--374\"\n}", "pdf": "https://aclanthology.org/2021.acl-short.47.pdf", "site": "https://aclanthology.org/2021.acl-short.47/", "pdf_size": 483810, "gs_citation": 110, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=14417766748360523881&as_sdt=5,47&sciodt=0,47&hl=en", "gs_version_total": 5, "aff": "National Key Laboratory for Novel Software Technology, Nanjing University, China; Machine Intelligence Technology Lab, Alibaba DAMO Academy; University of Science and Technology of China; National Key Laboratory for Novel Software Technology, Nanjing University, China; Machine Intelligence Technology Lab, Alibaba DAMO Academy; Machine Intelligence Technology Lab, Alibaba DAMO Academy; National Key Laboratory for Novel Software Technology, Nanjing University, China", "aff_domain": "smail.nju.edu.cn;alibaba-inc.com;mail.ustc.edu.cn;nju.edu.cn;alibaba-inc.com;alibaba-inc.com;nju.edu.cn", "email": "smail.nju.edu.cn;alibaba-inc.com;mail.ustc.edu.cn;nju.edu.cn;alibaba-inc.com;alibaba-inc.com;nju.edu.cn", "github": "https://github.com/zhengxxn/adaptive-knn-mt", "project": "", "author_num": 7, "aff_unique_index": "0;1;2;0;1;1;0", "aff_unique_norm": "Nanjing University;Alibaba DAMO Academy;University of Science and Technology of China", "aff_unique_dep": "National Key Laboratory for Novel Software Technology;Machine Intelligence Technology Lab;", "aff_unique_url": "http://www.nju.edu.cn;https://damo.alibaba.com;http://www.ustc.edu.cn", "aff_unique_abbr": "Nanjing U;Alibaba DAMO;USTC", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0;0;0;0;0;0;0", "aff_country_unique": "China" }, { "id": "2021.acl-short.118", "title": "Addressing Semantic Drift in Generative Question Answering with Auxiliary Extraction", "track": "main", "status": "Short", "award": false, "abstract": "Recently, question answering (QA) based on machine reading comprehension has become popular. This work focuses on generative QA which aims to generate an abstractive answer to a given question instead of extracting an answer span from a provided passage. Generative QA often suffers from two critical problems: (1) summarizing content irrelevant to a given question, (2) drifting away from a correct answer during generation. In this paper, we address these problems by a novel Rationale-Enriched Answer Generator (REAG), which incorporates an extractive mechanism into a generative model. Specifically, we add an extraction task on the encoder to obtain the rationale for an answer, which is the most relevant piece of text in an input document to a given question. Based on the extracted rationale and original input, the decoder is expected to generate an answer with high confidence. We jointly train REAG on the MS MARCO QA+NLG task and the experimental results show that REAG improves the quality and semantic accuracy of answers over baseline models.", "author": "Chenliang Li; Bin Bi; Ming Yan; Wei Wang; Songfang Huang", "authorids": "/c/chenliang-li/; /b/bin-bi/; /m/ming-yan/; /w/wei-wang/; /s/songfang-huang/", "bibtex": "@inproceedings{li-etal-2021-addressing-semantic,\n title = \"Addressing Semantic Drift in Generative Question Answering with Auxiliary Extraction\",\n author = \"Li, Chenliang and\n Bi, Bin and\n Yan, Ming and\n Wang, Wei and\n Huang, Songfang\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-short.118/\",\n doi = \"10.18653/v1/2021.acl-short.118\",\n pages = \"942--947\"\n}", "pdf": "https://aclanthology.org/2021.acl-short.118.pdf", "site": "https://aclanthology.org/2021.acl-short.118/", "pdf_size": 237908, "gs_citation": 25, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=9333567137342848369&as_sdt=5,33&sciodt=0,33&hl=en", "gs_version_total": 2, "aff": "Alibaba Group; Alibaba Group; Alibaba Group; Alibaba Group; Alibaba Group", "aff_domain": "alibaba-inc.com;alibaba-inc.com;alibaba-inc.com;alibaba-inc.com;alibaba-inc.com", "email": "alibaba-inc.com;alibaba-inc.com;alibaba-inc.com;alibaba-inc.com;alibaba-inc.com", "github": "", "project": "", "author_num": 5, "aff_unique_index": "0;0;0;0;0", "aff_unique_norm": "Alibaba Group", "aff_unique_dep": "", "aff_unique_url": "https://www.alibaba.com", "aff_unique_abbr": "Alibaba", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0;0;0;0;0", "aff_country_unique": "China" }, { "id": "2021.acl-long.61", "title": "AdvPicker: Effectively Leveraging Unlabeled Data via Adversarial Discriminator for Cross-Lingual NER", "track": "main", "status": "Long", "award": false, "abstract": "Neural methods have been shown to achieve high performance in Named Entity Recognition (NER), but rely on costly high-quality labeled data for training, which is not always available across languages. While previous works have shown that unlabeled data in a target language can be used to improve cross-lingual model performance, we propose a novel adversarial approach (AdvPicker) to better leverage such data and further improve results. We design an adversarial learning framework in which an encoder learns entity domain knowledge from labeled source-language data and better shared features are captured via adversarial training - where a discriminator selects less language-dependent target-language data via similarity to the source language. Experimental results on standard benchmark datasets well demonstrate that the proposed method benefits strongly from this data selection process and outperforms existing state-of-the-art methods; without requiring any additional external resources (e.g., gazetteers or via machine translation).", "author": "Weile Chen; Huiqiang Jiang; Qianhui Wu; B\u00f6rje Karlsson; Yi Guan", "authorids": "/w/weile-chen/; /h/huiqiang-jiang/; /q/qianhui-wu/; /b/borje-karlsson/; /y/yi-guan/", "bibtex": "@inproceedings{chen-etal-2021-advpicker,\n title = \"{A}dv{P}icker: {E}ffectively {L}everaging {U}nlabeled {D}ata via {A}dversarial {D}iscriminator for {C}ross-{L}ingual {NER}\",\n author = {Chen, Weile and\n Jiang, Huiqiang and\n Wu, Qianhui and\n Karlsson, B{\\\"o}rje and\n Guan, Yi},\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.61/\",\n doi = \"10.18653/v1/2021.acl-long.61\",\n pages = \"743--753\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.61.pdf", "site": "https://aclanthology.org/2021.acl-long.61/", "pdf_size": 477109, "gs_citation": 41, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=13263251179574173593&as_sdt=400005&sciodt=0,14&hl=en", "gs_version_total": 8, "aff": "Harbin Institute of Technology; Peking University; Department of Automation, Tsinghua University; Microsoft Research; Harbin Institute of Technology", "aff_domain": "gmail.com;pku.edu.cn;hit.edu.cn;tsinghua.org.cn;microsoft.com", "email": "gmail.com;pku.edu.cn;hit.edu.cn;tsinghua.org.cn;microsoft.com", "github": "", "project": "https://aka.ms/AdvPicker", "author_num": 5, "aff_unique_index": "0;1;2;3;0", "aff_unique_norm": "Harbin Institute of Technology;Peking University;Tsinghua University;Microsoft Corporation", "aff_unique_dep": ";;Department of Automation;Microsoft Research", "aff_unique_url": "http://www.hit.edu.cn/;http://www.pku.edu.cn;https://www.tsinghua.edu.cn;https://www.microsoft.com/en-us/research", "aff_unique_abbr": "HIT;Peking U;THU;MSR", "aff_campus_unique_index": "0;0", "aff_campus_unique": "Harbin;", "aff_country_unique_index": "0;0;0;1;0", "aff_country_unique": "China;United States" }, { "id": "2021.acl-long.305", "title": "Adversarial Learning for Discourse Rhetorical Structure Parsing", "track": "main", "status": "Long", "award": false, "abstract": "Text-level discourse rhetorical structure (DRS) parsing is known to be challenging due to the notorious lack of training data. Although recent top-down DRS parsers can better leverage global document context and have achieved certain success, the performance is still far from perfect. To our knowledge, all previous DRS parsers make local decisions for either bottom-up node composition or top-down split point ranking at each time step, and largely ignore DRS parsing from the global view point. Obviously, it is not sufficient to build an entire DRS tree only through these local decisions. In this work, we present our insight on evaluating the pros and cons of the entire DRS tree for global optimization. Specifically, based on recent well-performing top-down frameworks, we introduce a novel method to transform both gold standard and predicted constituency trees into tree diagrams with two color channels. After that, we learn an adversarial bot between gold and fake tree diagrams to estimate the generated DRS trees from a global perspective. We perform experiments on both RST-DT and CDTB corpora and use the original Parseval for performance evaluation. The experimental results show that our parser can substantially improve the performance when compared with previous state-of-the-art parsers.", "author": "Longyin Zhang; Fang Kong; Guodong Zhou", "authorids": "/l/longyin-zhang/; /f/fang-kong/; /g/guodong-zhou/", "bibtex": "@inproceedings{zhang-etal-2021-adversarial,\n title = \"Adversarial Learning for Discourse Rhetorical Structure Parsing\",\n author = \"Zhang, Longyin and\n Kong, Fang and\n Zhou, Guodong\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.305/\",\n doi = \"10.18653/v1/2021.acl-long.305\",\n pages = \"3946--3957\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.305.pdf", "site": "https://aclanthology.org/2021.acl-long.305/", "pdf_size": 1555799, "gs_citation": 40, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=3059163597850457805&as_sdt=5,33&sciodt=0,33&hl=en", "gs_version_total": 3, "aff": "Institute of Artificial Intelligence, Soochow University, China+School of Computer Science and Technology, Soochow University, China; Institute of Artificial Intelligence, Soochow University, China+School of Computer Science and Technology, Soochow University, China; Institute of Artificial Intelligence, Soochow University, China+School of Computer Science and Technology, Soochow University, China", "aff_domain": "stu.suda.edu.cn;suda.edu.cn;suda.edu.cn", "email": "stu.suda.edu.cn;suda.edu.cn;suda.edu.cn", "github": "", "project": "", "author_num": 3, "aff_unique_index": "0+0;0+0;0+0", "aff_unique_norm": "Soochow University", "aff_unique_dep": "Institute of Artificial Intelligence", "aff_unique_url": "https://www.soochow.edu.cn", "aff_unique_abbr": "", "aff_campus_unique_index": ";;", "aff_campus_unique": "", "aff_country_unique_index": "0+0;0+0;0+0", "aff_country_unique": "China" }, { "id": "2021.acl-long.113", "title": "AggGen: Ordering and Aggregating while Generating", "track": "main", "status": "Long", "award": false, "abstract": "We present AggGen (pronounced \u2018again\u2019) a data-to-text model which re-introduces two explicit sentence planning stages into neural data-to-text systems: input ordering and input aggregation. In contrast to previous work using sentence planning, our model is still end-to-end: AggGen performs sentence planning at the same time as generating text by learning latent alignments (via semantic facts) between input representation and target text. Experiments on the WebNLG and E2E challenge data show that by using fact-based alignments our approach is more interpretable, expressive, robust to noise, and easier to control, while retaining the advantages of end-to-end systems in terms of fluency. Our code is available at https://github.com/XinnuoXu/AggGen.", "author": "Xinnuo Xu; Ond\u0159ej Du\u0161ek; Verena Rieser; Ioannis Konstas", "authorids": "/x/xinnuo-xu/; /o/ondrej-dusek/; /v/verena-rieser/; /i/ioannis-konstas/", "bibtex": "@inproceedings{xu-etal-2021-agggen,\n title = \"{A}gg{G}en: Ordering and Aggregating while Generating\",\n author = \"Xu, Xinnuo and\n Du{\\v{s}}ek, Ond{\\v{r}}ej and\n Rieser, Verena and\n Konstas, Ioannis\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.113/\",\n doi = \"10.18653/v1/2021.acl-long.113\",\n pages = \"1419--1434\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.113.pdf", "site": "https://aclanthology.org/2021.acl-long.113/", "pdf_size": 1237240, "gs_citation": 16, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=8353983394016562878&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 8, "aff": "The Interaction Lab, MACS, Heriot-Watt University, Edinburgh, UK; Charles University, Faculty of Mathematics and Physics, Prague, Czechia; The Interaction Lab, MACS, Heriot-Watt University, Edinburgh, UK; The Interaction Lab, MACS, Heriot-Watt University, Edinburgh, UK", "aff_domain": "hw.ac.uk;ufal.mff.cuni.cz;hw.ac.uk;hw.ac.uk", "email": "hw.ac.uk;ufal.mff.cuni.cz;hw.ac.uk;hw.ac.uk", "github": "https://github.com/XinnuoXu/AggGen", "project": "", "author_num": 4, "aff_unique_index": "0;1;0;0", "aff_unique_norm": "Heriot-Watt University;Charles University", "aff_unique_dep": "MACS;Faculty of Mathematics and Physics", "aff_unique_url": "https://www.hw.ac.uk;https://www.cuni.cz", "aff_unique_abbr": "HWU;Charles University", "aff_campus_unique_index": "0;1;0;0", "aff_campus_unique": "Edinburgh;Prague", "aff_country_unique_index": "0;1;0;0", "aff_country_unique": "United Kingdom;Czechia" }, { "id": "2021.acl-short.54", "title": "AligNarr: Aligning Narratives on Movies", "track": "main", "status": "Short", "award": false, "abstract": "High-quality alignment between movie scripts and plot summaries is an asset for learning to summarize stories and to generate dialogues. The alignment task is challenging as scripts and summaries substantially differ in details and abstraction levels as well as in linguistic register. This paper addresses the alignment problem by devising a fully unsupervised approach based on a global optimization model. Experimental results on ten movies show the viability of our method with 76% F1-score and its superiority over a previous baseline. We publish alignments for 914 movies to foster research in this new topic.", "author": "Paramita Mirza; Mostafa Abouhamra; Gerhard Weikum", "authorids": "/p/paramita-mirza/; /m/mostafa-abouhamra/; /g/gerhard-weikum/", "bibtex": "@inproceedings{mirza-etal-2021-alignarr,\n title = \"{A}lig{N}arr: Aligning Narratives on Movies\",\n author = \"Mirza, Paramita and\n Abouhamra, Mostafa and\n Weikum, Gerhard\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-short.54/\",\n doi = \"10.18653/v1/2021.acl-short.54\",\n pages = \"427--433\"\n}", "pdf": "https://aclanthology.org/2021.acl-short.54.pdf", "site": "https://aclanthology.org/2021.acl-short.54/", "pdf_size": 500544, "gs_citation": 6, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=11935133643752620630&as_sdt=80005&sciodt=0,11&hl=en", "gs_version_total": 4, "aff": "Max Planck Institute for Informatics; Max Planck Institute for Informatics; Max Planck Institute for Informatics", "aff_domain": "mpi-inf.mpg.de;mpi-inf.mpg.de;mpi-inf.mpg.de", "email": "mpi-inf.mpg.de;mpi-inf.mpg.de;mpi-inf.mpg.de", "github": "", "project": "", "author_num": 3, "aff_unique_index": "0;0;0", "aff_unique_norm": "Max Planck Institute for Informatics", "aff_unique_dep": "", "aff_unique_url": "https://mpi-inf.mpg.de", "aff_unique_abbr": "MPII", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0;0;0", "aff_country_unique": "Germany" }, { "id": "2021.acl-long.99", "title": "Align Voting Behavior with Public Statements for Legislator Representation Learning", "track": "main", "status": "Long", "award": false, "abstract": "Ideology of legislators is typically estimated by ideal point models from historical records of votes. It represents legislators and legislation as points in a latent space and shows promising results for modeling voting behavior. However, it fails to capture more specific attitudes of legislators toward emerging issues and is unable to model newly-elected legislators without voting histories. In order to mitigate these two problems, we explore to incorporate both voting behavior and public statements on Twitter to jointly model legislators. In addition, we propose a novel task, namely hashtag usage prediction to model the ideology of legislators on Twitter. In practice, we construct a heterogeneous graph for the legislative context and use relational graph neural networks to learn the representation of legislators with the guidance of historical records of their voting and hashtag usage. Experiment results indicate that our model yields significant improvements for the task of roll call vote prediction. Further analysis further demonstrates that legislator representation we learned captures nuances in statements.", "author": "Xinyi Mou; Zhongyu Wei; Lei Chen; Shangyi Ning; Yancheng He; Changjian Jiang; Xuanjing Huang", "authorids": "/x/xinyi-mou/; /z/zhongyu-wei/; /l/lei-chen/; /s/shangyi-ning/; /y/yancheng-he/; /c/changjian-jiang/; /x/xuan-jing-huang/", "bibtex": "@inproceedings{mou-etal-2021-align,\n title = \"Align Voting Behavior with Public Statements for Legislator Representation Learning\",\n author = \"Mou, Xinyi and\n Wei, Zhongyu and\n Chen, Lei and\n Ning, Shangyi and\n He, Yancheng and\n Jiang, Changjian and\n Huang, Xuanjing\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.99/\",\n doi = \"10.18653/v1/2021.acl-long.99\",\n pages = \"1236--1246\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.99.pdf", "site": "https://aclanthology.org/2021.acl-long.99/", "pdf_size": 1554344, "gs_citation": 15, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=3743683455442848876&as_sdt=4005&sciodt=0,6&hl=en", "gs_version_total": 3, "aff": "School of Data Science, Fudan University, China; School of Data Science, Fudan University, China + Research Institute of Intelligent and Complex Systems, Fudan University, China; School of Data Science, Fudan University, China; School of Data Science, Fudan University, China; Platform and Content Group, Tencent, China; School of International Relations & Public Affairs, Fudan University, China; School of Computer Science, Fudan University, China", "aff_domain": "fudan.edu.cn;fudan.edu.cn;fudan.edu.cn;fudan.edu.cn;tencent.com;fudan.edu.cn;fudan.edu.cn", "email": "fudan.edu.cn;fudan.edu.cn;fudan.edu.cn;fudan.edu.cn;tencent.com;fudan.edu.cn;fudan.edu.cn", "github": "", "project": "", "author_num": 7, "aff_unique_index": "0;0+0;0;0;1;0;0", "aff_unique_norm": "Fudan University;Tencent", "aff_unique_dep": "School of Data Science;Platform and Content Group", "aff_unique_url": "https://www.fudan.edu.cn;https://www.tencent.com", "aff_unique_abbr": "Fudan;Tencent", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0;0+0;0;0;0;0;0", "aff_country_unique": "China" }, { "id": "2021.acl-long.417", "title": "Alignment Rationale for Natural Language Inference", "track": "main", "status": "Long", "award": false, "abstract": "Deep learning models have achieved great success on the task of Natural Language Inference (NLI), though only a few attempts try to explain their behaviors. Existing explanation methods usually pick prominent features such as words or phrases from the input text. However, for NLI, alignments among words or phrases are more enlightening clues to explain the model. To this end, this paper presents AREC, a post-hoc approach to generate alignment rationale explanations for co-attention based models in NLI. The explanation is based on feature selection, which keeps few but sufficient alignments while maintaining the same prediction of the target model. Experimental results show that our method is more faithful and human-readable compared with many existing approaches. We further study and re-evaluate three typical models through our explanation beyond accuracy, and propose a simple method that greatly improves the model robustness.", "author": "Zhongtao Jiang; Yuanzhe Zhang; Zhao Yang; Jun Zhao; Kang Liu", "authorids": "/z/zhongtao-jiang/; /y/yuanzhe-zhang/; /z/zhao-yang/; /j/jun-zhao/; /k/kang-liu/", "bibtex": "@inproceedings{jiang-etal-2021-alignment,\n title = \"Alignment Rationale for Natural Language Inference\",\n author = \"Jiang, Zhongtao and\n Zhang, Yuanzhe and\n Yang, Zhao and\n Zhao, Jun and\n Liu, Kang\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.417/\",\n doi = \"10.18653/v1/2021.acl-long.417\",\n pages = \"5372--5387\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.417.pdf", "site": "https://aclanthology.org/2021.acl-long.417/", "pdf_size": 1310796, "gs_citation": 29, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=10647572892032277477&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 2, "aff": "National Laboratory of Pattern Recognition, Institute of Automation, CAS, Beijing, China+School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China; National Laboratory of Pattern Recognition, Institute of Automation, CAS, Beijing, China+School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China; National Laboratory of Pattern Recognition, Institute of Automation, CAS, Beijing, China+School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China; National Laboratory of Pattern Recognition, Institute of Automation, CAS, Beijing, China+School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China; National Laboratory of Pattern Recognition, Institute of Automation, CAS, Beijing, China+School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China", "aff_domain": "nlpr.ia.ac.cn;nlpr.ia.ac.cn;nlpr.ia.ac.cn;nlpr.ia.ac.cn;nlpr.ia.ac.cn", "email": "nlpr.ia.ac.cn;nlpr.ia.ac.cn;nlpr.ia.ac.cn;nlpr.ia.ac.cn;nlpr.ia.ac.cn", "github": "https://github.com/changmenseng/arec", "project": "", "author_num": 5, "aff_unique_index": "0+1;0+1;0+1;0+1;0+1", "aff_unique_norm": "National Laboratory of Pattern Recognition;University of Chinese Academy of Sciences", "aff_unique_dep": "Institute of Automation;School of Artificial Intelligence", "aff_unique_url": ";http://www.ucas.ac.cn", "aff_unique_abbr": ";UCAS", "aff_campus_unique_index": "0+0;0+0;0+0;0+0;0+0", "aff_campus_unique": "Beijing", "aff_country_unique_index": "0+0;0+0;0+0;0+0;0+0", "aff_country_unique": "China" }, { "id": "2021.acl-long.565", "title": "All That\u2019s \u2018Human\u2019 Is Not Gold: Evaluating Human Evaluation of Generated Text", "track": "main", "status": "Long", "award": true, "abstract": "Human evaluations are typically considered the gold standard in natural language generation, but as models\u2019 fluency improves, how well can evaluators detect and judge machine-generated text? We run a study assessing non-experts\u2019 ability to distinguish between human- and machine-authored text (GPT2 and GPT3) in three domains (stories, news articles, and recipes). We find that, without training, evaluators distinguished between GPT3- and human-authored text at random chance level. We explore three approaches for quickly training evaluators to better identify GPT3-authored text (detailed instructions, annotated examples, and paired examples) and find that while evaluators\u2019 accuracy improved up to 55%, it did not significantly improve across the three domains. Given the inconsistent results across text domains and the often contradictory reasons evaluators gave for their judgments, we examine the role untrained human evaluations play in NLG evaluation and provide recommendations to NLG researchers for improving human evaluations of text generated from state-of-the-art models.", "author": "Elizabeth Clark; Tal August; Sofia Serrano; Nikita Haduong; Suchin Gururangan; Noah A. Smith", "authorids": "/e/elizabeth-clark/; /t/tal-august/; /s/sofia-serrano/; /n/nikita-haduong/; /s/suchin-gururangan/; /n/noah-a-smith/", "bibtex": "@inproceedings{clark-etal-2021-thats,\n title = \"All That`s {\\textquoteleft}Human' Is Not Gold: Evaluating Human Evaluation of Generated Text\",\n author = \"Clark, Elizabeth and\n August, Tal and\n Serrano, Sofia and\n Haduong, Nikita and\n Gururangan, Suchin and\n Smith, Noah A.\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.565/\",\n doi = \"10.18653/v1/2021.acl-long.565\",\n pages = \"7282--7296\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.565.pdf", "site": "https://aclanthology.org/2021.acl-long.565/", "pdf_size": 2393136, "gs_citation": 460, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=10752753854094987600&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 8, "aff": "Paul G. Allen School of Computer Science & Engineering, University of Washington; Paul G. Allen School of Computer Science & Engineering, University of Washington; Paul G. Allen School of Computer Science & Engineering, University of Washington; Paul G. Allen School of Computer Science & Engineering, University of Washington; Paul G. Allen School of Computer Science & Engineering, University of Washington; Paul G. Allen School of Computer Science & Engineering, University of Washington + Allen Institute for Artificial Intelligence", "aff_domain": "cs.washington.edu;cs.washington.edu;cs.washington.edu;cs.washington.edu;cs.washington.edu;cs.washington.edu", "email": "cs.washington.edu;cs.washington.edu;cs.washington.edu;cs.washington.edu;cs.washington.edu;cs.washington.edu", "github": "", "project": "", "author_num": 6, "aff_unique_index": "0;0;0;0;0;0+1", "aff_unique_norm": "University of Washington;Allen Institute for Artificial Intelligence", "aff_unique_dep": "Paul G. Allen School of Computer Science & Engineering;", "aff_unique_url": "https://www.washington.edu;https://allenai.org", "aff_unique_abbr": "UW;AI2", "aff_campus_unique_index": "0;0;0;0;0;0", "aff_campus_unique": "Seattle;", "aff_country_unique_index": "0;0;0;0;0;0+0", "aff_country_unique": "United States" }, { "id": "2021.acl-short.58", "title": "An Empirical Study on Adversarial Attack on NMT: Languages and Positions Matter", "track": "main", "status": "Short", "award": false, "abstract": "In this paper, we empirically investigate adversarial attack on NMT from two aspects: languages (the source vs. the target language) and positions (front vs. rear). For autoregressive NMT models that generate target words from left to right, we observe that adversarial attack on the source language is more effective than on the target language, and that attacking front positions of target sentences or positions of source sentences aligned to the front positions of corresponding target sentences is more effective than attacking other positions. We further exploit the attention distribution of the victim model to attack source sentences at positions that have a strong association with front target words. Experiment results demonstrate that our attention-based adversarial attack is more effective than adversarial attacks by sampling positions randomly or according to gradients.", "author": "Zhiyuan Zeng; Deyi Xiong", "authorids": "/z/zhiyuan-zeng/; /d/deyi-xiong/", "bibtex": "@inproceedings{zeng-xiong-2021-empirical,\n title = \"An Empirical Study on Adversarial Attack on {NMT}: Languages and Positions Matter\",\n author = \"Zeng, Zhiyuan and\n Xiong, Deyi\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-short.58/\",\n doi = \"10.18653/v1/2021.acl-short.58\",\n pages = \"454--460\"\n}", "pdf": "https://aclanthology.org/2021.acl-short.58.pdf", "site": "https://aclanthology.org/2021.acl-short.58/", "pdf_size": 306679, "gs_citation": 7, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=11365075182738662100&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 2, "aff": "School of New Media and Communication, Tianjin University, Tianjin, China; College of Intelligence and Computing, Tianjin University, Tianjin, China", "aff_domain": "tju.edu.cn;tju.edu.cn", "email": "tju.edu.cn;tju.edu.cn", "github": "", "project": "", "author_num": 2, "aff_unique_index": "0;0", "aff_unique_norm": "Tianjin University", "aff_unique_dep": "School of New Media and Communication", "aff_unique_url": "http://www.tju.edu.cn", "aff_unique_abbr": "Tianjin University", "aff_campus_unique_index": "0;0", "aff_campus_unique": "Tianjin", "aff_country_unique_index": "0;0", "aff_country_unique": "China" }, { "id": "2021.acl-long.178", "title": "An Empirical Study on Hyperparameter Optimization for Fine-Tuning Pre-trained Language Models", "track": "main", "status": "Long", "award": false, "abstract": "The performance of fine-tuning pre-trained language models largely depends on the hyperparameter configuration. In this paper, we investigate the performance of modern hyperparameter optimization methods (HPO) on fine-tuning pre-trained language models. First, we study and report three HPO algorithms\u2019 performances on fine-tuning two state-of-the-art language models on the GLUE dataset. We find that using the same time budget, HPO often fails to outperform grid search due to two reasons: insufficient time budget and overfitting. We propose two general strategies and an experimental procedure to systematically troubleshoot HPO\u2019s failure cases. By applying the procedure, we observe that HPO can succeed with more appropriate settings in the search space and time budget; however, in certain cases overfitting remains. Finally, we make suggestions for future work. Our implementation can be found in https://github.com/microsoft/FLAML/tree/main/flaml/nlp/", "author": "Xueqing Liu; Chi Wang", "authorids": "/x/xueqing-liu/; /c/chi-wang/", "bibtex": "@inproceedings{liu-wang-2021-empirical,\n title = \"An Empirical Study on Hyperparameter Optimization for Fine-Tuning Pre-trained Language Models\",\n author = \"Liu, Xueqing and\n Wang, Chi\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.178/\",\n doi = \"10.18653/v1/2021.acl-long.178\",\n pages = \"2286--2300\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.178.pdf", "site": "https://aclanthology.org/2021.acl-long.178/", "pdf_size": 383110, "gs_citation": 36, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=3480352702775921552&as_sdt=5,44&sciodt=0,44&hl=en", "gs_version_total": 4, "aff": "Stevens Institute of Technology; Microsoft Research", "aff_domain": "stevens.edu;microsoft.com", "email": "stevens.edu;microsoft.com", "github": "https://github.com/microsoft/FLAML/tree/main/flaml/nlp/", "project": "", "author_num": 2, "aff_unique_index": "0;1", "aff_unique_norm": "Stevens Institute of Technology;Microsoft Corporation", "aff_unique_dep": ";Microsoft Research", "aff_unique_url": "https://www.stevens.edu;https://www.microsoft.com/en-us/research", "aff_unique_abbr": "SIT;MSR", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0;0", "aff_country_unique": "United States" }, { "id": "2021.acl-long.485", "title": "An End-to-End Progressive Multi-Task Learning Framework for Medical Named Entity Recognition and Normalization", "track": "main", "status": "Long", "award": false, "abstract": "Medical named entity recognition (NER) and normalization (NEN) are fundamental for constructing knowledge graphs and building QA systems. Existing implementations for medical NER and NEN are suffered from the error propagation between the two tasks. The mispredicted mentions from NER will directly influence the results of NEN. Therefore, the NER module is the bottleneck of the whole system. Besides, the learnable features for both tasks are beneficial to improving the model performance. To avoid the disadvantages of existing models and exploit the generalized representation across the two tasks, we design an end-to-end progressive multi-task learning model for jointly modeling medical NER and NEN in an effective way. There are three level tasks with progressive difficulty in the framework. The progressive tasks can reduce the error propagation with the incremental task settings which implies the lower level tasks gain the supervised signals other than errors from the higher level tasks to improve their performances. Besides, the context features are exploited to enrich the semantic information of entity mentions extracted by NER. The performance of NEN profits from the enhanced entity mention features. The standard entities from knowledge bases are introduced into the NER module for extracting corresponding entity mentions correctly. The empirical results on two publicly available medical literature datasets demonstrate the superiority of our method over nine typical methods.", "author": "Baohang Zhou; Xiangrui Cai; Ying Zhang; Xiaojie Yuan", "authorids": "/b/baohang-zhou/; /x/xiangrui-cai/; /y/ying-zhang/; /x/xiaojie-yuan/", "bibtex": "@inproceedings{zhou-etal-2021-end,\n title = \"An End-to-End Progressive Multi-Task Learning Framework for Medical Named Entity Recognition and Normalization\",\n author = \"Zhou, Baohang and\n Cai, Xiangrui and\n Zhang, Ying and\n Yuan, Xiaojie\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.485/\",\n doi = \"10.18653/v1/2021.acl-long.485\",\n pages = \"6214--6224\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.485.pdf", "site": "https://aclanthology.org/2021.acl-long.485/", "pdf_size": 1285299, "gs_citation": 27, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=15199653942513443108&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 2, "aff": "College of Computer Science, Nankai University, Tianjin 300350, China + Tianjin Key Laboratory of Network and Data Security Technology, Tianjin 300350, China; College of Cyber Science, Nankai University, Tianjin 300350, China + Tianjin Key Laboratory of Network and Data Security Technology, Tianjin 300350, China; College of Computer Science, Nankai University, Tianjin 300350, China + Tianjin Key Laboratory of Network and Data Security Technology, Tianjin 300350, China; College of Computer Science, Nankai University, Tianjin 300350, China + Tianjin Key Laboratory of Network and Data Security Technology, Tianjin 300350, China", "aff_domain": "mail.nankai.edu.cn;nankai.edu.cn;nankai.edu.cn;nankai.edu.cn", "email": "mail.nankai.edu.cn;nankai.edu.cn;nankai.edu.cn;nankai.edu.cn", "github": "", "project": "", "author_num": 4, "aff_unique_index": "0+1;0+1;0+1;0+1", "aff_unique_norm": "Nankai University;Tianjin Key Laboratory of Network and Data Security Technology", "aff_unique_dep": "College of Computer Science;Network and Data Security Technology", "aff_unique_url": "http://www.nankai.edu.cn;", "aff_unique_abbr": "Nankai;", "aff_campus_unique_index": "0;0;0;0", "aff_campus_unique": "Tianjin;", "aff_country_unique_index": "0+0;0+0;0+0;0+0", "aff_country_unique": "China" }, { "id": "2021.acl-short.55", "title": "An Exploratory Analysis of Multilingual Word-Level Quality Estimation with Cross-Lingual Transformers", "track": "main", "status": "Short", "award": false, "abstract": "Most studies on word-level Quality Estimation (QE) of machine translation focus on language-specific models. The obvious disadvantages of these approaches are the need for labelled data for each language pair and the high cost required to maintain several language-specific models. To overcome these problems, we explore different approaches to multilingual, word-level QE. We show that multilingual QE models perform on par with the current language-specific models. In the cases of zero-shot and few-shot QE, we demonstrate that it is possible to accurately predict word-level quality for any given new language pair from models trained on other language pairs. Our findings suggest that the word-level QE models based on powerful pre-trained transformers that we propose in this paper generalise well across languages, making them more useful in real-world scenarios.", "author": "Tharindu Ranasinghe; Constantin Orasan; Ruslan Mitkov", "authorids": "/t/tharindu-ranasinghe/; /c/constantin-orasan/; /r/ruslan-mitkov/", "bibtex": "@inproceedings{ranasinghe-etal-2021-exploratory,\n title = \"An Exploratory Analysis of Multilingual Word-Level Quality Estimation with Cross-Lingual Transformers\",\n author = \"Ranasinghe, Tharindu and\n Orasan, Constantin and\n Mitkov, Ruslan\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-short.55/\",\n doi = \"10.18653/v1/2021.acl-short.55\",\n pages = \"434--440\"\n}", "pdf": "https://aclanthology.org/2021.acl-short.55.pdf", "site": "https://aclanthology.org/2021.acl-short.55/", "pdf_size": 565907, "gs_citation": 32, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=18259252760053640607&as_sdt=5,33&sciodt=0,33&hl=en", "gs_version_total": 11, "aff": "Research Group in Computational Linguistics, University of Wolverhampton, UK; Centre for Translation Studies, University of Surrey, UK; Research Group in Computational Linguistics, University of Wolverhampton, UK", "aff_domain": "wlv.ac.uk;surrey.ac.uk;wlv.ac.uk", "email": "wlv.ac.uk;surrey.ac.uk;wlv.ac.uk", "github": "", "project": "http://tharindu.co.uk/TransQuest/", "author_num": 3, "aff_unique_index": "0;1;0", "aff_unique_norm": "University of Wolverhampton;University of Surrey", "aff_unique_dep": "Research Group in Computational Linguistics;Centre for Translation Studies", "aff_unique_url": "https://www.wlv.ac.uk;https://www.surrey.ac.uk", "aff_unique_abbr": ";", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0;0;0", "aff_country_unique": "United Kingdom" }, { "id": "2021.acl-short.23", "title": "An Improved Model for Voicing Silent Speech", "track": "main", "status": "Short", "award": false, "abstract": "In this paper, we present an improved model for voicing silent speech, where audio is synthesized from facial electromyography (EMG) signals. To give our model greater flexibility to learn its own input features, we directly use EMG signals as input in the place of hand-designed features used by prior work. Our model uses convolutional layers to extract features from the signals and Transformer layers to propagate information across longer distances. To provide better signal for learning, we also introduce an auxiliary task of predicting phoneme labels in addition to predicting speech audio features. On an open vocabulary intelligibility evaluation, our model improves the state of the art for this task by an absolute 25.8%.", "author": "David Gaddy; Dan Klein", "authorids": "/d/david-gaddy/; /d/dan-klein/", "bibtex": "@inproceedings{gaddy-klein-2021-improved,\n title = \"An Improved Model for Voicing Silent Speech\",\n author = \"Gaddy, David and\n Klein, Dan\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-short.23/\",\n doi = \"10.18653/v1/2021.acl-short.23\",\n pages = \"175--181\"\n}", "pdf": "https://aclanthology.org/2021.acl-short.23.pdf", "site": "https://aclanthology.org/2021.acl-short.23/", "pdf_size": 351073, "gs_citation": 34, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=10658340596292768586&as_sdt=5,33&sciodt=0,33&hl=en", "gs_version_total": 5, "aff": "University of California, Berkeley; University of California, Berkeley", "aff_domain": "berkeley.edu;berkeley.edu", "email": "berkeley.edu;berkeley.edu", "github": "", "project": "", "author_num": 2, "aff_unique_index": "0;0", "aff_unique_norm": "University of California, Berkeley", "aff_unique_dep": "", "aff_unique_url": "https://www.berkeley.edu", "aff_unique_abbr": "UC Berkeley", "aff_campus_unique_index": "0;0", "aff_campus_unique": "Berkeley", "aff_country_unique_index": "0;0", "aff_country_unique": "United States" }, { "id": "2021.acl-long.452", "title": "An In-depth Study on Internal Structure of Chinese Words", "track": "main", "status": "Long", "award": false, "abstract": "Unlike English letters, Chinese characters have rich and specific meanings. Usually, the meaning of a word can be derived from its constituent characters in some way. Several previous works on syntactic parsing propose to annotate shallow word-internal structures for better utilizing character-level information. This work proposes to model the deep internal structures of Chinese words as dependency trees with 11 labels for distinguishing syntactic relationships. First, based on newly compiled annotation guidelines, we manually annotate a word-internal structure treebank (WIST) consisting of over 30K multi-char words from Chinese Penn Treebank. To guarantee quality, each word is independently annotated by two annotators and inconsistencies are handled by a third senior annotator. Second, we present detailed and interesting analysis on WIST to reveal insights on Chinese word formation. Third, we propose word-internal structure parsing as a new task, and conduct benchmark experiments using a competitive dependency parser. Finally, we present two simple ways to encode word-internal structures, leading to promising gains on the sentence-level syntactic parsing task.", "author": "Chen Gong; Saihao Huang; Houquan Zhou; Zhenghua Li; Min Zhang; Zhefeng Wang; Baoxing Huai; Nicholas Jing Yuan", "authorids": "/c/chen-gong/; /s/saihao-huang/; /h/houquan-zhou/; /z/zhenghua-li/; /m/min-zhang/; /z/zhefeng-wang/; /b/baoxing-huai/; /n/nicholas-jing-yuan/", "bibtex": "@inproceedings{gong-etal-2021-depth,\n title = \"An In-depth Study on Internal Structure of {C}hinese Words\",\n author = \"Gong, Chen and\n Huang, Saihao and\n Zhou, Houquan and\n Li, Zhenghua and\n Zhang, Min and\n Wang, Zhefeng and\n Huai, Baoxing and\n Yuan, Nicholas Jing\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.452/\",\n doi = \"10.18653/v1/2021.acl-long.452\",\n pages = \"5823--5833\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.452.pdf", "site": "https://aclanthology.org/2021.acl-long.452/", "pdf_size": 417823, "gs_citation": 2, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=12311775778318725109&as_sdt=5,33&sciodt=0,33&hl=en", "gs_version_total": 8, "aff": "Institute of Artificial Intelligence, School of Computer Science and Technology, Soochow University, China+Huawei Cloud, China; Institute of Artificial Intelligence, School of Computer Science and Technology, Soochow University, China+Huawei Cloud, China; Institute of Artificial Intelligence, School of Computer Science and Technology, Soochow University, China; Institute of Artificial Intelligence, School of Computer Science and Technology, Soochow University, China; Institute of Artificial Intelligence, School of Computer Science and Technology, Soochow University, China; Huawei Cloud, China; Huawei Cloud, China; Huawei Cloud, China", "aff_domain": "stu.suda.edu.cn;stu.suda.edu.cn;stu.suda.edu.cn;suda.edu.cn;suda.edu.cn;huawei.com;huawei.com;huawei.com", "email": "stu.suda.edu.cn;stu.suda.edu.cn;stu.suda.edu.cn;suda.edu.cn;suda.edu.cn;huawei.com;huawei.com;huawei.com", "github": "", "project": "", "author_num": 8, "aff_unique_index": "0+1;0+1;0;0;0;1;1;1", "aff_unique_norm": "Soochow University;Huawei Cloud", "aff_unique_dep": "Institute of Artificial Intelligence, School of Computer Science and Technology;", "aff_unique_url": "http://www.soochow.edu.cn;https://www.huaweicloud.com", "aff_unique_abbr": "Soochow U;Huawei Cloud", "aff_campus_unique_index": ";", "aff_campus_unique": "", "aff_country_unique_index": "0+0;0+0;0;0;0;0;0;0", "aff_country_unique": "China" }, { "id": "2021.acl-long.91", "title": "Analyzing the Source and Target Contributions to Predictions in Neural Machine Translation", "track": "main", "status": "Long", "award": false, "abstract": "In Neural Machine Translation (and, more generally, conditional language modeling), the generation of a target token is influenced by two types of context: the source and the prefix of the target sequence. While many attempts to understand the internal workings of NMT models have been made, none of them explicitly evaluates relative source and target contributions to a generation decision. We argue that this relative contribution can be evaluated by adopting a variant of Layerwise Relevance Propagation (LRP). Its underlying \u2018conservation principle\u2019 makes relevance propagation unique: differently from other methods, it evaluates not an abstract quantity reflecting token importance, but the proportion of each token\u2019s influence. We extend LRP to the Transformer and conduct an analysis of NMT models which explicitly evaluates the source and target relative contributions to the generation process. We analyze changes in these contributions when conditioning on different types of prefixes, when varying the training objective or the amount of training data, and during the training process. We find that models trained with more data tend to rely on source information more and to have more sharp token contributions; the training process is non-monotonic with several stages of different nature.", "author": "Elena Voita; Rico Sennrich; Ivan Titov", "authorids": "/e/elena-voita/; /r/rico-sennrich/; /i/ivan-titov/", "bibtex": "@inproceedings{voita-etal-2021-analyzing,\n title = \"Analyzing the Source and Target Contributions to Predictions in Neural Machine Translation\",\n author = \"Voita, Elena and\n Sennrich, Rico and\n Titov, Ivan\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.91/\",\n doi = \"10.18653/v1/2021.acl-long.91\",\n pages = \"1126--1140\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.91.pdf", "site": "https://aclanthology.org/2021.acl-long.91/", "pdf_size": 4145759, "gs_citation": 87, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=11887150553903063028&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 8, "aff": "University of Edinburgh, Scotland+University of Amsterdam, Netherlands; University of Zurich, Switzerland+University of Edinburgh, Scotland; University of Edinburgh, Scotland+University of Amsterdam, Netherlands", "aff_domain": "hotmail.com;cl.uzh.ch;inf.ed.ac.uk", "email": "hotmail.com;cl.uzh.ch;inf.ed.ac.uk", "github": "https://github.com/lena-voita/the-story-of-heads", "project": "", "author_num": 3, "aff_unique_index": "0+1;2+0;0+1", "aff_unique_norm": "University of Edinburgh;University of Amsterdam;University of Zurich", "aff_unique_dep": ";;", "aff_unique_url": "https://www.ed.ac.uk;https://www.uva.nl;https://www.unizh.ch", "aff_unique_abbr": "Edinburgh;UvA;UZH", "aff_campus_unique_index": ";;", "aff_campus_unique": "", "aff_country_unique_index": "0+1;2+0;0+1", "aff_country_unique": "United Kingdom;Netherlands;Switzerland" }, { "id": "2021.acl-short.30", "title": "Anchor-based Bilingual Word Embeddings for Low-Resource Languages", "track": "main", "status": "Short", "award": false, "abstract": "Good quality monolingual word embeddings (MWEs) can be built for languages which have large amounts of unlabeled text. MWEs can be aligned to bilingual spaces using only a few thousand word translation pairs. For low resource languages training MWEs monolingually results in MWEs of poor quality, and thus poor bilingual word embeddings (BWEs) as well. This paper proposes a new approach for building BWEs in which the vector space of the high resource source language is used as a starting point for training an embedding space for the low resource target language. By using the source vectors as anchors the vector spaces are automatically aligned during training. We experiment on English-German, English-Hiligaynon and English-Macedonian. We show that our approach results not only in improved BWEs and bilingual lexicon induction performance, but also in improved target language MWE quality as measured using monolingual word similarity.", "author": "Tobias Eder; Viktor Hangya; Alexander Fraser", "authorids": "/t/tobias-eder/; /v/viktor-hangya/; /a/alexander-fraser/", "bibtex": "@inproceedings{eder-etal-2021-anchor,\n title = \"Anchor-based Bilingual Word Embeddings for Low-Resource Languages\",\n author = \"Eder, Tobias and\n Hangya, Viktor and\n Fraser, Alexander\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-short.30/\",\n doi = \"10.18653/v1/2021.acl-short.30\",\n pages = \"227--232\"\n}", "pdf": "https://aclanthology.org/2021.acl-short.30.pdf", "site": "https://aclanthology.org/2021.acl-short.30/", "pdf_size": 230073, "gs_citation": 14, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=15301873668640757942&as_sdt=5,31&sciodt=0,31&hl=en", "gs_version_total": 8, "aff": "Research Group Social Computing, Technical University of Munich; Center for Information and Language Processing, LMU Munich; Center for Information and Language Processing, LMU Munich", "aff_domain": "in.tum.de;cis.lmu.de;cis.lmu.de", "email": "in.tum.de;cis.lmu.de;cis.lmu.de", "github": "", "project": "", "author_num": 3, "aff_unique_index": "0;1;1", "aff_unique_norm": "Technical University of Munich;LMU Munich", "aff_unique_dep": "Research Group Social Computing;Center for Information and Language Processing", "aff_unique_url": "https://www.tum.de;https://www.lmu.de", "aff_unique_abbr": "TUM;LMU", "aff_campus_unique_index": "1;1", "aff_campus_unique": ";Munich", "aff_country_unique_index": "0;0;0", "aff_country_unique": "Germany" }, { "id": "2021.acl-long.247", "title": "Annotating Online Misogyny", "track": "main", "status": "Long", "award": false, "abstract": "Online misogyny, a category of online abusive language, has serious and harmful social consequences. Automatic detection of misogynistic language online, while imperative, poses complicated challenges to both data gathering, data annotation, and bias mitigation, as this type of data is linguistically complex and diverse. This paper makes three contributions in this area: Firstly, we describe the detailed design of our iterative annotation process and codebook. Secondly, we present a comprehensive taxonomy of labels for annotating misogyny in natural written language, and finally, we introduce a high-quality dataset of annotated posts sampled from social media posts.", "author": "Philine Zeinert; Nanna Inie; Leon Derczynski", "authorids": "/p/philine-zeinert/; /n/nanna-inie/; /l/leon-derczynski/", "bibtex": "@inproceedings{zeinert-etal-2021-annotating,\n title = \"Annotating Online Misogyny\",\n author = \"Zeinert, Philine and\n Inie, Nanna and\n Derczynski, Leon\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.247/\",\n doi = \"10.18653/v1/2021.acl-long.247\",\n pages = \"3181--3197\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.247.pdf", "site": "https://aclanthology.org/2021.acl-long.247/", "pdf_size": 596473, "gs_citation": 69, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=4810774392704128075&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 5, "aff": "IT University of Copenhagen; IT University of Copenhagen; IT University of Copenhagen", "aff_domain": "itu.dk;itu.dk;itu.dk", "email": "itu.dk;itu.dk;itu.dk", "github": "https://github.com/phze22/Online-Misogyny-in-Danish-Bajer", "project": "", "author_num": 3, "aff_unique_index": "0;0;0", "aff_unique_norm": "IT University of Copenhagen", "aff_unique_dep": "", "aff_unique_url": "https://itu.dk", "aff_unique_abbr": "ITU", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0;0;0", "aff_country_unique": "Denmark" }, { "id": "2021.acl-long.323", "title": "Anonymisation Models for Text Data: State of the art, Challenges and Future Directions", "track": "main", "status": "Long", "award": false, "abstract": "This position paper investigates the problem of automated text anonymisation, which is a prerequisite for secure sharing of documents containing sensitive information about individuals. We summarise the key concepts behind text anonymisation and provide a review of current approaches. Anonymisation methods have so far been developed in two fields with little mutual interaction, namely natural language processing and privacy-preserving data publishing. Based on a case study, we outline the benefits and limitations of these approaches and discuss a number of open challenges, such as (1) how to account for multiple types of semantic inferences, (2) how to strike a balance between disclosure risk and data utility and (3) how to evaluate the quality of the resulting anonymisation. We lay out a case for moving beyond sequence labelling models and incorporate explicit measures of disclosure risk into the text anonymisation process.", "author": "Pierre Lison; Ildik\u00f3 Pil\u00e1n; David Sanchez; Montserrat Batet; Lilja \u00d8vrelid", "authorids": "/p/pierre-lison/; /i/ildiko-pilan/; /d/david-sanchez/; /m/montserrat-batet/; /l/lilja-ovrelid/", "bibtex": "@inproceedings{lison-etal-2021-anonymisation,\n title = \"Anonymisation Models for Text Data: State of the art, Challenges and Future Directions\",\n author = \"Lison, Pierre and\n Pil{\\'a}n, Ildik{\\'o} and\n Sanchez, David and\n Batet, Montserrat and\n {\\O}vrelid, Lilja\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.323/\",\n doi = \"10.18653/v1/2021.acl-long.323\",\n pages = \"4188--4203\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.323.pdf", "site": "https://aclanthology.org/2021.acl-long.323/", "pdf_size": 478513, "gs_citation": 122, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=11146716458548675695&as_sdt=5,33&sciodt=0,33&hl=en", "gs_version_total": 4, "aff": "Norwegian Computing Center, Oslo, Norway; Norwegian Computing Center, Oslo, Norway; Universitat Rovira i Virgili, CYBERCAT, UNESCO Chair in Data Privacy, Spain; Universitat Rovira i Virgili, CYBERCAT, UNESCO Chair in Data Privacy, Spain; Language Technology Group, University of Oslo, Norway", "aff_domain": "nr.no;nr.no;urv.cat;urv.cat;ifi.uio.no", "email": "nr.no;nr.no;urv.cat;urv.cat;ifi.uio.no", "github": "", "project": "", "author_num": 5, "aff_unique_index": "0;0;1;1;2", "aff_unique_norm": "Norwegian Computing Center;Universitat Rovira i Virgili;University of Oslo", "aff_unique_dep": ";CYBERCAT, UNESCO Chair in Data Privacy;Language Technology Group", "aff_unique_url": "https://www.ncc.no;https://www.urv.cat;https://www.uio.no", "aff_unique_abbr": ";URV;UiO", "aff_campus_unique_index": "0;0", "aff_campus_unique": "Oslo;", "aff_country_unique_index": "0;0;1;1;0", "aff_country_unique": "Norway;Spain" }, { "id": "2021.acl-long.253", "title": "Answering Ambiguous Questions through Generative Evidence Fusion and Round-Trip Prediction", "track": "main", "status": "Long", "award": false, "abstract": "In open-domain question answering, questions are highly likely to be ambiguous because users may not know the scope of relevant topics when formulating them. Therefore, a system needs to find possible interpretations of the question, and predict one or multiple plausible answers. When multiple plausible answers are found, the system should rewrite the question for each answer to resolve the ambiguity. In this paper, we present a model that aggregates and combines evidence from multiple passages to adaptively predict a single answer or a set of question-answer pairs for ambiguous questions. In addition, we propose a novel round-trip prediction approach to iteratively generate additional interpretations that our model fails to find in the first pass, and then verify and filter out the incorrect question-answer pairs to arrive at the final disambiguated output. Our model, named Refuel, achieves a new state-of-the-art performance on the AmbigQA dataset, and shows competitive performance on NQ-Open and TriviaQA. The proposed round-trip prediction is a model-agnostic general approach for answering ambiguous open-domain questions, which improves our Refuel as well as several baseline models. We release source code for our models and experiments at https://github.com/amzn/refuel-open-domain-qa.", "author": "Yifan Gao; Henghui Zhu; Patrick Ng; Cicero Nogueira dos Santos; Zhiguo Wang; Feng Nan; Dejiao Zhang; Ramesh Nallapati; Andrew O. Arnold; Bing Xiang", "authorids": "/y/yifan-gao/; /h/henghui-zhu/; /p/patrick-ng/; /c/cicero-dos-santos/; /z/zhiguo-wang/; /f/feng-nan/; /d/dejiao-zhang/; /r/ramesh-nallapati/; /a/andrew-o-arnold/; /b/bing-xiang/", "bibtex": "@inproceedings{gao-etal-2021-answering,\n title = \"Answering Ambiguous Questions through Generative Evidence Fusion and Round-Trip Prediction\",\n author = \"Gao, Yifan and\n Zhu, Henghui and\n Ng, Patrick and\n Nogueira dos Santos, Cicero and\n Wang, Zhiguo and\n Nan, Feng and\n Zhang, Dejiao and\n Nallapati, Ramesh and\n Arnold, Andrew O. and\n Xiang, Bing\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.253/\",\n doi = \"10.18653/v1/2021.acl-long.253\",\n pages = \"3263--3276\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.253.pdf", "site": "https://aclanthology.org/2021.acl-long.253/", "pdf_size": 757353, "gs_citation": 22, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=5260570727036270455&as_sdt=5,33&sciodt=0,33&hl=en", "gs_version_total": 6, "aff": "AWS AI+The Chinese University of Hong Kong; AWS AI; AWS AI; AWS AI; AWS AI; AWS AI; AWS AI; AWS AI; AWS AI; AWS AI", "aff_domain": "gmail.com;amazon.com;amazon.com;amazon.com;amazon.com;amazon.com;amazon.com;amazon.com;amazon.com;amazon.com", "email": "gmail.com;amazon.com;amazon.com;amazon.com;amazon.com;amazon.com;amazon.com;amazon.com;amazon.com;amazon.com", "github": "https://github.com/amzn/refuel-open-domain-qa", "project": "", "author_num": 10, "aff_unique_index": "0+1;0;0;0;0;0;0;0;0;0", "aff_unique_norm": "Amazon Web Services;The Chinese University of Hong Kong", "aff_unique_dep": "AWS AI;", "aff_unique_url": "https://aws.amazon.com;https://www.cuhk.edu.hk", "aff_unique_abbr": "AWS;CUHK", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0+1;0;0;0;0;0;0;0;0;0", "aff_country_unique": "United States;China" }, { "id": "2021.acl-long.534", "title": "Are Missing Links Predictable? An Inferential Benchmark for Knowledge Graph Completion", "track": "main", "status": "Long", "award": false, "abstract": "We present InferWiki, a Knowledge Graph Completion (KGC) dataset that improves upon existing benchmarks in inferential ability, assumptions, and patterns. First, each testing sample is predictable with supportive data in the training set. To ensure it, we propose to utilize rule-guided train/test generation, instead of conventional random split. Second, InferWiki initiates the evaluation following the open-world assumption and improves the inferential difficulty of the closed-world assumption, by providing manually annotated negative and unknown triples. Third, we include various inference patterns (e.g., reasoning path length and types) for comprehensive evaluation. In experiments, we curate two settings of InferWiki varying in sizes and structures, and apply the construction process on CoDEx as comparative datasets. The results and empirical analyses demonstrate the necessity and high-quality of InferWiki. Nevertheless, the performance gap among various inferential assumptions and patterns presents the difficulty and inspires future research direction. Our datasets can be found in https://github.com/TaoMiner/inferwiki.", "author": "Yixin Cao; Xiang Ji; Xin Lv; Juanzi Li; Yonggang Wen; Hanwang Zhang", "authorids": "/y/yixin-cao/; /x/xiang-ji/; /x/xin-lv/; /j/juanzi-li/; /y/yonggang-wen/; /h/hanwang-zhang/", "bibtex": "@inproceedings{cao-etal-2021-missing,\n title = \"Are Missing Links Predictable? An Inferential Benchmark for Knowledge Graph Completion\",\n author = \"Cao, Yixin and\n Ji, Xiang and\n Lv, Xin and\n Li, Juanzi and\n Wen, Yonggang and\n Zhang, Hanwang\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.534/\",\n doi = \"10.18653/v1/2021.acl-long.534\",\n pages = \"6855--6865\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.534.pdf", "site": "https://aclanthology.org/2021.acl-long.534/", "pdf_size": 2155628, "gs_citation": 33, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=15619915320105516297&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 6, "aff": "School of CSE, Nanyang Technological University, Singapore + S-Lab, Nanyang Technological University, Singapore; School of CSE, Nanyang Technological University, Singapore; Department of CST, Tsinghua University, Beijing, China; Department of CST, Tsinghua University, Beijing, China; School of CSE, Nanyang Technological University, Singapore; School of CSE, Nanyang Technological University, Singapore", "aff_domain": "gmail.com; ;mails.tsinghua.edu.cn;tsinghua.edu.cn;ntu.edu.sg;ntu.edu.sg", "email": "gmail.com; ;mails.tsinghua.edu.cn;tsinghua.edu.cn;ntu.edu.sg;ntu.edu.sg", "github": "https://github.com/TaoMiner/inferwiki", "project": "", "author_num": 6, "aff_unique_index": "0+0;0;1;1;0;0", "aff_unique_norm": "Nanyang Technological University;Tsinghua University", "aff_unique_dep": "School of CSE;Department of CST", "aff_unique_url": "https://www.ntu.edu.sg;https://www.tsinghua.edu.cn", "aff_unique_abbr": "NTU;THU", "aff_campus_unique_index": ";1;1", "aff_campus_unique": ";Beijing", "aff_country_unique_index": "0+0;0;1;1;0;0", "aff_country_unique": "Singapore;China" }, { "id": "2021.acl-long.335", "title": "Are Pretrained Convolutions Better than Pretrained Transformers?", "track": "main", "status": "Long", "award": false, "abstract": "In the era of pre-trained language models, Transformers are the de facto choice of model architectures. While recent research has shown promise in entirely convolutional, or CNN, architectures, they have not been explored using the pre-train-fine-tune paradigm. In the context of language models, are convolutional models competitive to Transformers when pre-trained? This paper investigates this research question and presents several interesting findings. Across an extensive set of experiments on 8 datasets/tasks, we find that CNN-based pre-trained models are competitive and outperform their Transformer counterpart in certain scenarios, albeit with caveats. Overall, the findings outlined in this paper suggest that conflating pre-training and architectural advances is misguided and that both advances should be considered independently. We believe our research paves the way for a healthy amount of optimism in alternative architectures.", "author": "Yi Tay; Mostafa Dehghani; Jai Prakash Gupta; Vamsi Aribandi; Dara Bahri; Zhen Qin; Donald Metzler", "authorids": "/y/yi-tay/; /m/mostafa-dehghani/; /j/jai-prakash-gupta/; /v/vamsi-aribandi/; /d/dara-bahri/; /z/zhen-qin/; /d/donald-metzler/", "bibtex": "@inproceedings{tay-etal-2021-pretrained,\n title = \"Are Pretrained Convolutions Better than Pretrained Transformers?\",\n author = \"Tay, Yi and\n Dehghani, Mostafa and\n Gupta, Jai Prakash and\n Aribandi, Vamsi and\n Bahri, Dara and\n Qin, Zhen and\n Metzler, Donald\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.335/\",\n doi = \"10.18653/v1/2021.acl-long.335\",\n pages = \"4349--4359\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.335.pdf", "site": "https://aclanthology.org/2021.acl-long.335/", "pdf_size": 495295, "gs_citation": 94, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=905317145613274381&as_sdt=5,47&sciodt=0,47&hl=en", "gs_version_total": 6, "aff": "Google Research, Mountain View, California; Google Research, Brain Team, Amsterdam, Netherlands; Google Research, Mountain View, California; Google Research, Mountain View, California; Google Research, Mountain View, California; Google Research, Mountain View, California; Google Research, Mountain View, California", "aff_domain": "google.com;google.com;google.com;google.com;google.com;google.com;google.com", "email": "google.com;google.com;google.com;google.com;google.com;google.com;google.com", "github": "", "project": "", "author_num": 7, "aff_unique_index": "0;1;0;0;0;0;0", "aff_unique_norm": "Google;Google Research", "aff_unique_dep": "Google Research;Brain Team", "aff_unique_url": "https://research.google;https://research.google", "aff_unique_abbr": "Google;Google", "aff_campus_unique_index": "0;1;0;0;0;0;0", "aff_campus_unique": "Mountain View;Amsterdam", "aff_country_unique_index": "0;1;0;0;0;0;0", "aff_country_unique": "United States;Netherlands" }, { "id": "2021.acl-short.10", "title": "Are VQA Systems RAD? Measuring Robustness to Augmented Data with Focused Interventions", "track": "main", "status": "Short", "award": false, "abstract": "Deep learning algorithms have shown promising results in visual question answering (VQA) tasks, but a more careful look reveals that they often do not understand the rich signal they are being fed with. To understand and better measure the generalization capabilities of VQA systems, we look at their robustness to counterfactually augmented data. Our proposed augmentations are designed to make a focused intervention on a specific property of the question such that the answer changes. Using these augmentations, we propose a new robustness measure, Robustness to Augmented Data (RAD), which measures the consistency of model predictions between original and augmented examples. Through extensive experimentation, we show that RAD, unlike classical accuracy measures, can quantify when state-of-the-art systems are not robust to counterfactuals. We find substantial failure cases which reveal that current VQA systems are still brittle. Finally, we connect between robustness and generalization, demonstrating the predictive power of RAD for performance on unseen augmentations.", "author": "Daniel Rosenberg; Itai Gat; Amir Feder; Roi Reichart", "authorids": "/d/daniel-rosenberg/; /i/itai-gat/; /a/amir-feder/; /r/roi-reichart/", "bibtex": "@inproceedings{rosenberg-etal-2021-vqa,\n title = \"Are {VQA} Systems {RAD}? {M}easuring Robustness to Augmented Data with Focused Interventions\",\n author = \"Rosenberg, Daniel and\n Gat, Itai and\n Feder, Amir and\n Reichart, Roi\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-short.10/\",\n doi = \"10.18653/v1/2021.acl-short.10\",\n pages = \"61--70\"\n}", "pdf": "https://aclanthology.org/2021.acl-short.10.pdf", "site": "https://aclanthology.org/2021.acl-short.10/", "pdf_size": 1928931, "gs_citation": 15, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=1692042828556222724&as_sdt=40005&sciodt=0,10&hl=en", "gs_version_total": 5, "aff": "Technion - Israel Institute of Technology; Technion - Israel Institute of Technology; Technion - Israel Institute of Technology; Technion - Israel Institute of Technology", "aff_domain": "gmail.com;technion.ac.il;campus.technion.ac.il;technion.ac.il", "email": "gmail.com;technion.ac.il;campus.technion.ac.il;technion.ac.il", "github": "https://github.com/danrosenberg/rad-measure", "project": "https://danrosenberg.github.io/rad-measure/", "author_num": 4, "aff_unique_index": "0;0;0;0", "aff_unique_norm": "Technion - Israel Institute of Technology", "aff_unique_dep": "", "aff_unique_url": "https://www.technion.ac.il/en/", "aff_unique_abbr": "Technion", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0;0;0;0", "aff_country_unique": "Israel" }, { "id": "2021.acl-long.496", "title": "Argument Pair Extraction via Attention-guided Multi-Layer Multi-Cross Encoding", "track": "main", "status": "Long", "award": false, "abstract": "Argument pair extraction (APE) is a research task for extracting arguments from two passages and identifying potential argument pairs. Prior research work treats this task as a sequence labeling problem and a binary classification problem on two passages that are directly concatenated together, which has a limitation of not fully utilizing the unique characteristics and inherent relations of two different passages. This paper proposes a novel attention-guided multi-layer multi-cross encoding scheme to address the challenges. The new model processes two passages with two individual sequence encoders and updates their representations using each other\u2019s representations through attention. In addition, the pair prediction part is formulated as a table-filling problem by updating the representations of two sequences\u2019 Cartesian product. Furthermore, an auxiliary attention loss is introduced to guide each argument to align to its paired argument. An extensive set of experiments show that the new model significantly improves the APE performance over several alternatives.", "author": "Liying Cheng; Tianyu Wu; Lidong Bing; Luo Si", "authorids": "/l/liying-cheng/; /t/tianyu-wu/; /l/lidong-bing/; /l/luo-si/", "bibtex": "@inproceedings{cheng-etal-2021-argument,\n title = \"Argument Pair Extraction via Attention-guided Multi-Layer Multi-Cross Encoding\",\n author = \"Cheng, Liying and\n Wu, Tianyu and\n Bing, Lidong and\n Si, Luo\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.496/\",\n doi = \"10.18653/v1/2021.acl-long.496\",\n pages = \"6341--6353\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.496.pdf", "site": "https://aclanthology.org/2021.acl-long.496/", "pdf_size": 1223383, "gs_citation": 31, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=17585470978037428617&as_sdt=5,44&sciodt=0,44&hl=en", "gs_version_total": 3, "aff": "Singapore University of Technology and Design+DAMO Academy, Alibaba Group; Singapore University of Technology and Design; DAMO Academy, Alibaba Group; DAMO Academy, Alibaba Group", "aff_domain": "alibaba-inc.com;alumni.sutd.edu.sg;alibaba-inc.com;alibaba-inc.com", "email": "alibaba-inc.com;alumni.sutd.edu.sg;alibaba-inc.com;alibaba-inc.com", "github": "https://github.com/TianyuTerry/MLMC", "project": "", "author_num": 4, "aff_unique_index": "0+1;0;1;1", "aff_unique_norm": "Singapore University of Technology and Design;Alibaba Group", "aff_unique_dep": ";DAMO Academy", "aff_unique_url": "https://www.sutd.edu.sg;https://www.alibaba-group.com", "aff_unique_abbr": "SUTD;Alibaba", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0+1;0;1;1", "aff_country_unique": "Singapore;China" }, { "id": "2021.acl-long.425", "title": "Article Reranking by Memory-Enhanced Key Sentence Matching for Detecting Previously Fact-Checked Claims", "track": "main", "status": "Long", "award": false, "abstract": "False claims that have been previously fact-checked can still spread on social media. To mitigate their continual spread, detecting previously fact-checked claims is indispensable. Given a claim, existing works focus on providing evidence for detection by reranking candidate fact-checking articles (FC-articles) retrieved by BM25. However, these performances may be limited because they ignore the following characteristics of FC-articles: (1) claims are often quoted to describe the checked events, providing lexical information besides semantics; (2) sentence templates to introduce or debunk claims are common across articles, providing pattern information. Models that ignore the two aspects only leverage semantic relevance and may be misled by sentences that describe similar but irrelevant events. In this paper, we propose a novel reranker, MTM (Memory-enhanced Transformers for Matching) to rank FC-articles using key sentences selected with event (lexical and semantic) and pattern information. For event information, we propose a ROUGE-guided Transformer which is finetuned with regression of ROUGE. For pattern information, we generate pattern vectors for matching with sentences. By fusing event and pattern information, we select key sentences to represent an article and then predict if the article fact-checks the given claim using the claim, key sentences, and patterns. Experiments on two real-world datasets show that MTM outperforms existing methods. Human evaluation proves that MTM can capture key sentences for explanations.", "author": "Qiang Sheng; Juan Cao; Xueyao Zhang; Xirong Li; Lei Zhong", "authorids": "/q/qiang-sheng/; /j/juan-cao/; /x/xueyao-zhang/; /x/xirong-li/; /l/lei-zhong/", "bibtex": "@inproceedings{sheng-etal-2021-article,\n title = \"Article Reranking by Memory-Enhanced Key Sentence Matching for Detecting Previously Fact-Checked Claims\",\n author = \"Sheng, Qiang and\n Cao, Juan and\n Zhang, Xueyao and\n Li, Xirong and\n Zhong, Lei\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.425/\",\n doi = \"10.18653/v1/2021.acl-long.425\",\n pages = \"5468--5481\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.425.pdf", "site": "https://aclanthology.org/2021.acl-long.425/", "pdf_size": 4039542, "gs_citation": 34, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=10345310784320483702&as_sdt=80000005&sciodt=0,23&hl=en", "gs_version_total": 7, "aff": "Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences+University of Chinese Academy of Sciences; Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences+University of Chinese Academy of Sciences; Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences+University of Chinese Academy of Sciences; Key Lab of Data Engineering and Knowledge Engineering, Renmin University of China; Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences+University of Chinese Academy of Sciences", "aff_domain": "ict.ac.cn;ict.ac.cn;ict.ac.cn;ruc.edu.cn;ict.ac.cn", "email": "ict.ac.cn;ict.ac.cn;ict.ac.cn;ruc.edu.cn;ict.ac.cn", "github": "https://github.com/ICTMCG/MTM", "project": "", "author_num": 5, "aff_unique_index": "0+1;0+1;0+1;2;0+1", "aff_unique_norm": "Chinese Academy of Sciences;University of Chinese Academy of Sciences;Renmin University of China", "aff_unique_dep": "Institute of Computing Technology;;Key Lab of Data Engineering and Knowledge Engineering", "aff_unique_url": "http://www.cas.cn;http://www.ucas.ac.cn;http://www.ruc.edu.cn", "aff_unique_abbr": "CAS;UCAS;RUC", "aff_campus_unique_index": ";;;", "aff_campus_unique": "", "aff_country_unique_index": "0+0;0+0;0+0;0;0+0", "aff_country_unique": "China" }, { "id": "2021.acl-long.29", "title": "Aspect-Category-Opinion-Sentiment Quadruple Extraction with Implicit Aspects and Opinions", "track": "main", "status": "Long", "award": false, "abstract": "Product reviews contain a large number of implicit aspects and implicit opinions. However, most of the existing studies in aspect-based sentiment analysis ignored this problem. In this work, we introduce a new task, named Aspect-Category-Opinion-Sentiment (ACOS) Quadruple Extraction, with the goal to extract all aspect-category-opinion-sentiment quadruples in a review sentence and provide full support for aspect-based sentiment analysis with implicit aspects and opinions. We furthermore construct two new datasets, Restaurant-ACOS and Laptop-ACOS, for this new task, both of which contain the annotations of not only aspect-category-opinion-sentiment quadruples but also implicit aspects and opinions. The former is an extension of the SemEval Restaurant dataset; the latter is a newly collected and annotated Laptop dataset, twice the size of the SemEval Laptop dataset. We finally benchmark the task with four baseline systems. Experiments demonstrate the feasibility of the new task and its effectiveness in extracting and describing implicit aspects and implicit opinions. The two datasets and source code of four systems are publicly released at https://github.com/NUSTM/ACOS.", "author": "Hongjie Cai; Rui Xia; Jianfei Yu", "authorids": "/h/hongjie-cai/; /r/rui-xia/; /j/jianfei-yu/", "bibtex": "@inproceedings{cai-etal-2021-aspect,\n title = \"Aspect-Category-Opinion-Sentiment Quadruple Extraction with Implicit Aspects and Opinions\",\n author = \"Cai, Hongjie and\n Xia, Rui and\n Yu, Jianfei\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.29/\",\n doi = \"10.18653/v1/2021.acl-long.29\",\n pages = \"340--350\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.29.pdf", "site": "https://aclanthology.org/2021.acl-long.29/", "pdf_size": 560576, "gs_citation": 233, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=5295149944344120368&as_sdt=5,33&sciodt=0,33&hl=en", "gs_version_total": 4, "aff": "School of Computer Science and Engineering, Nanjing University of Science and Technology, China; School of Computer Science and Engineering, Nanjing University of Science and Technology, China; School of Computer Science and Engineering, Nanjing University of Science and Technology, China", "aff_domain": "njust.edu.cn;njust.edu.cn;njust.edu.cn", "email": "njust.edu.cn;njust.edu.cn;njust.edu.cn", "github": "https://github.com/NUSTM/ACOS", "project": "", "author_num": 3, "aff_unique_index": "0;0;0", "aff_unique_norm": "Nanjing University of Science and Technology", "aff_unique_dep": "School of Computer Science and Engineering", "aff_unique_url": "", "aff_unique_abbr": "", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0;0;0", "aff_country_unique": "China" }, { "id": "2021.acl-long.110", "title": "Assessing Emoji Use in Modern Text Processing Tools", "track": "main", "status": "Long", "award": false, "abstract": "Emojis have become ubiquitous in digital communication, due to their visual appeal as well as their ability to vividly convey human emotion, among other factors. This also leads to an increased need for systems and tools to operate on text containing emojis. In this study, we assess this support by considering test sets of tweets with emojis, based on which we perform a series of experiments investigating the ability of prominent NLP and text processing tools to adequately process them. In particular, we consider tokenization, part-of-speech tagging, dependency parsing, as well as sentiment analysis. Our findings show that many systems still have notable shortcomings when operating on text containing emojis.", "author": "Abu Awal Md Shoeb; Gerard de Melo", "authorids": "/a/abu-awal-md-shoeb/; /g/gerard-de-melo/", "bibtex": "@inproceedings{shoeb-de-melo-2021-assessing,\n title = \"Assessing Emoji Use in Modern Text Processing Tools\",\n author = \"Shoeb, Abu Awal Md and\n de Melo, Gerard\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.110/\",\n doi = \"10.18653/v1/2021.acl-long.110\",\n pages = \"1379--1388\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.110.pdf", "site": "https://aclanthology.org/2021.acl-long.110/", "pdf_size": 525611, "gs_citation": 16, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=6390121092594583170&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 12, "aff": "Dept. of Computer Science, Rutgers University, New Brunswick, NJ, USA; HassoPlattner Institute / University of Potsdam, Potsdam, Germany", "aff_domain": "rutgers.edu;demelo.org", "email": "rutgers.edu;demelo.org", "github": "", "project": "", "author_num": 2, "aff_unique_index": "0;1", "aff_unique_norm": "Rutgers University;Hasso Plattner Institute", "aff_unique_dep": "Department of Computer Science;", "aff_unique_url": "https://www.rutgers.edu;https://www.hpi.de", "aff_unique_abbr": "Rutgers;HPI", "aff_campus_unique_index": "0;1", "aff_campus_unique": "New Brunswick;Potsdam", "aff_country_unique_index": "0;1", "aff_country_unique": "United States;Germany" }, { "id": "2021.acl-long.212", "title": "Assessing the Representations of Idiomaticity in Vector Models with a Noun Compound Dataset Labeled at Type and Token Levels", "track": "main", "status": "Long", "award": false, "abstract": "Accurate assessment of the ability of embedding models to capture idiomaticity may require evaluation at token rather than type level, to account for degrees of idiomaticity and possible ambiguity between literal and idiomatic usages. However, most existing resources with annotation of idiomaticity include ratings only at type level. This paper presents the Noun Compound Type and Token Idiomaticity (NCTTI) dataset, with human annotations for 280 noun compounds in English and 180 in Portuguese at both type and token level. We compiled 8,725 and 5,091 token level annotations for English and Portuguese, respectively, which are strongly correlated with the corresponding scores obtained at type level. The NCTTI dataset is used to explore how vector space models reflect the variability of idiomaticity across sentences. Several experiments using state-of-the-art contextualised models suggest that their representations are not capturing the noun compounds idiomaticity as human annotators. This new multilingual resource also contains suggestions for paraphrases of the noun compounds both at type and token levels, with uses for lexical substitution or disambiguation in context.", "author": "Marcos Garcia; Tiago Kramer Vieira; Carolina Scarton; Marco Idiart; Aline Villavicencio", "authorids": "/m/marcos-garcia/; /t/tiago-kramer-vieira/; /c/carolina-scarton/; /m/marco-idiart/; /a/aline-villavicencio/", "bibtex": "@inproceedings{garcia-etal-2021-assessing,\n title = \"Assessing the Representations of Idiomaticity in Vector Models with a Noun Compound Dataset Labeled at Type and Token Levels\",\n author = \"Garcia, Marcos and\n Kramer Vieira, Tiago and\n Scarton, Carolina and\n Idiart, Marco and\n Villavicencio, Aline\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.212/\",\n doi = \"10.18653/v1/2021.acl-long.212\",\n pages = \"2730--2741\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.212.pdf", "site": "https://aclanthology.org/2021.acl-long.212/", "pdf_size": 312618, "gs_citation": -1, "gs_cited_by_link": "", "gs_version_total": 0, "aff": "CiTIUS Research Centre, Universidade de Santiago de Compostela, Galiza (Spain); Institute of Informatics, Federal University of Rio Grande do Sul (Brazil); Department of Computer Science, University of Sheffield (UK) + Institute of Informatics, Federal University of Rio Grande do Sul (Brazil); Institute of Physics, Federal University of Rio Grande do Sul (Brazil); Department of Computer Science, University of Sheffield (UK) + Institute of Informatics, Federal University of Rio Grande do Sul (Brazil)", "aff_domain": "udc.gal;hotmail.com;sheffield.ac.uk;gmail.com;sheffield.ac.uk", "email": "udc.gal;hotmail.com;sheffield.ac.uk;gmail.com;sheffield.ac.uk", "github": "", "project": "", "author_num": 5, "aff_unique_index": "0;1;2+1;1;2+1", "aff_unique_norm": "Universidade de Santiago de Compostela;Federal University of Rio Grande do Sul;University of Sheffield", "aff_unique_dep": "CiTIUS Research Centre;Institute of Informatics;Department of Computer Science", "aff_unique_url": "https://www.usc.es;https://www.ufrgs.br;https://www.sheffield.ac.uk", "aff_unique_abbr": "USC;;Sheffield", "aff_campus_unique_index": "0;;", "aff_campus_unique": "Compostela;", "aff_country_unique_index": "0;1;2+1;1;2+1", "aff_country_unique": "Spain;Brazil;United Kingdom" }, { "id": "2021.acl-long.481", "title": "Attend What You Need: Motion-Appearance Synergistic Networks for Video Question Answering", "track": "main", "status": "Long", "award": false, "abstract": "Video Question Answering is a task which requires an AI agent to answer questions grounded in video. This task entails three key challenges: (1) understand the intention of various questions, (2) capturing various elements of the input video (e.g., object, action, causality), and (3) cross-modal grounding between language and vision information. We propose Motion-Appearance Synergistic Networks (MASN), which embed two cross-modal features grounded on motion and appearance information and selectively utilize them depending on the question\u2019s intentions. MASN consists of a motion module, an appearance module, and a motion-appearance fusion module. The motion module computes the action-oriented cross-modal joint representations, while the appearance module focuses on the appearance aspect of the input video. Finally, the motion-appearance fusion module takes each output of the motion module and the appearance module as input, and performs question-guided fusion. As a result, MASN achieves new state-of-the-art performance on the TGIF-QA and MSVD-QA datasets. We also conduct qualitative analysis by visualizing the inference results of MASN.", "author": "Ahjeong Seo; Gi-Cheon Kang; Joonhan Park; Byoung-Tak Zhang", "authorids": "/a/ahjeong-seo/; /g/gi-cheon-kang/; /j/joonhan-park/; /b/byoung-tak-zhang/", "bibtex": "@inproceedings{seo-etal-2021-attend,\n title = \"Attend What You Need: Motion-Appearance Synergistic Networks for Video Question Answering\",\n author = \"Seo, Ahjeong and\n Kang, Gi-Cheon and\n Park, Joonhan and\n Zhang, Byoung-Tak\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.481/\",\n doi = \"10.18653/v1/2021.acl-long.481\",\n pages = \"6167--6177\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.481.pdf", "site": "https://aclanthology.org/2021.acl-long.481/", "pdf_size": 3842524, "gs_citation": 72, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=15542512696832404766&as_sdt=80000005&sciodt=0,23&hl=en", "gs_version_total": 5, "aff": "Seoul National University; Seoul National University + AI Institute for Seoul National University (AIIS); Hanyang University; Seoul National University + AI Institute for Seoul National University (AIIS)", "aff_domain": "bi.snu.ac.kr;bi.snu.ac.kr;bi.snu.ac.kr;bi.snu.ac.kr", "email": "bi.snu.ac.kr;bi.snu.ac.kr;bi.snu.ac.kr;bi.snu.ac.kr", "github": "https://github.com/ahjeongseo/MASN-pytorch", "project": "", "author_num": 4, "aff_unique_index": "0;0+0;1;0+0", "aff_unique_norm": "Seoul National University;Hanyang University", "aff_unique_dep": ";", "aff_unique_url": "https://www.snu.ac.kr;https://www.hanyang.ac.kr", "aff_unique_abbr": "SNU;HYU", "aff_campus_unique_index": ";", "aff_campus_unique": "", "aff_country_unique_index": "0;0+0;0;0+0", "aff_country_unique": "South Korea" }, { "id": "2021.acl-long.103", "title": "Attention Calibration for Transformer in Neural Machine Translation", "track": "main", "status": "Long", "award": false, "abstract": "Attention mechanisms have achieved substantial improvements in neural machine translation by dynamically selecting relevant inputs for different predictions. However, recent studies have questioned the attention mechanisms\u2019 capability for discovering decisive inputs. In this paper, we propose to calibrate the attention weights by introducing a mask perturbation model that automatically evaluates each input\u2019s contribution to the model outputs. We increase the attention weights assigned to the indispensable tokens, whose removal leads to a dramatic performance decrease. The extensive experiments on the Transformer-based translation have demonstrated the effectiveness of our model. We further find that the calibrated attention weights are more uniform at lower layers to collect multiple information while more concentrated on the specific inputs at higher layers. Detailed analyses also show a great need for calibration in the attention weights with high entropy where the model is unconfident about its decision.", "author": "Yu Lu; Jiali Zeng; Jiajun Zhang; Shuangzhi Wu; Mu Li", "authorids": "/y/yu-lu/; /j/jiali-zeng/; /j/jiajun-zhang/; /s/shuangzhi-wu/; /m/mu-li/", "bibtex": "@inproceedings{lu-etal-2021-attention,\n title = \"Attention Calibration for Transformer in Neural Machine Translation\",\n author = \"Lu, Yu and\n Zeng, Jiali and\n Zhang, Jiajun and\n Wu, Shuangzhi and\n Li, Mu\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.103/\",\n doi = \"10.18653/v1/2021.acl-long.103\",\n pages = \"1288--1298\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.103.pdf", "site": "https://aclanthology.org/2021.acl-long.103/", "pdf_size": 767088, "gs_citation": 29, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=643579364206161963&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 2, "aff": "National Laboratory of Pattern Recognition, Institute of Automation, CAS, Beijing, China+School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China; Tencent Cloud Xiaowei, Beijing, China; National Laboratory of Pattern Recognition, Institute of Automation, CAS, Beijing, China+School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China; Tencent Cloud Xiaowei, Beijing, China; Tencent Cloud Xiaowei, Beijing, China", "aff_domain": "nlpr.ia.ac.cn;tencent.com;nlpr.ia.ac.cn;tencent.com;tencent.com", "email": "nlpr.ia.ac.cn;tencent.com;nlpr.ia.ac.cn;tencent.com;tencent.com", "github": "https://github.com/yulu-dada/Attention-calibration-NMT", "project": "", "author_num": 5, "aff_unique_index": "0+1;2;0+1;2;2", "aff_unique_norm": "National Laboratory of Pattern Recognition;University of Chinese Academy of Sciences;Tencent Cloud Xiaowei", "aff_unique_dep": "Institute of Automation;School of Artificial Intelligence;", "aff_unique_url": ";http://www.ucas.ac.cn;https://cloud.tencent.com", "aff_unique_abbr": ";UCAS;Tencent Cloud", "aff_campus_unique_index": "0+0;0;0+0;0;0", "aff_campus_unique": "Beijing", "aff_country_unique_index": "0+0;0;0+0;0;0", "aff_country_unique": "China" }, { "id": "2021.acl-short.8", "title": "Attention Flows are Shapley Value Explanations", "track": "main", "status": "Short", "award": false, "abstract": "Shapley Values, a solution to the credit assignment problem in cooperative game theory, are a popular type of explanation in machine learning, having been used to explain the importance of features, embeddings, and even neurons. In NLP, however, leave-one-out and attention-based explanations still predominate. Can we draw a connection between these different methods? We formally prove that \u2014 save for the degenerate case \u2014 attention weights and leave-one-out values cannot be Shapley Values. Attention flow is a post-processed variant of attention weights obtained by running the max-flow algorithm on the attention graph. Perhaps surprisingly, we prove that attention flows are indeed Shapley Values, at least at the layerwise level. Given the many desirable theoretical qualities of Shapley Values \u2014 which has driven their adoption among the ML community \u2014 we argue that NLP practitioners should, when possible, adopt attention flow explanations alongside more traditional ones.", "author": "Kawin Ethayarajh; Dan Jurafsky", "authorids": "/k/kawin-ethayarajh/; /d/dan-jurafsky/", "bibtex": "@inproceedings{ethayarajh-jurafsky-2021-attention,\n title = \"Attention Flows are Shapley Value Explanations\",\n author = \"Ethayarajh, Kawin and\n Jurafsky, Dan\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-short.8/\",\n doi = \"10.18653/v1/2021.acl-short.8\",\n pages = \"49--54\"\n}", "pdf": "https://aclanthology.org/2021.acl-short.8.pdf", "site": "https://aclanthology.org/2021.acl-short.8/", "pdf_size": 284693, "gs_citation": 53, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=1957717549849733291&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 4, "aff": "Stanford University; Stanford University", "aff_domain": "stanford.edu;stanford.edu", "email": "stanford.edu;stanford.edu", "github": "", "project": "", "author_num": 2, "aff_unique_index": "0;0", "aff_unique_norm": "Stanford University", "aff_unique_dep": "", "aff_unique_url": "https://www.stanford.edu", "aff_unique_abbr": "Stanford", "aff_campus_unique_index": "0;0", "aff_campus_unique": "Stanford", "aff_country_unique_index": "0;0", "aff_country_unique": "United States" }, { "id": "2021.acl-short.128", "title": "Attentive Multiview Text Representation for Differential Diagnosis", "track": "main", "status": "Short", "award": false, "abstract": "We present a text representation approach that can combine different views (representations) of the same input through effective data fusion and attention strategies for ranking purposes. We apply our model to the problem of differential diagnosis, which aims to find the most probable diseases that match with clinical descriptions of patients, using data from the Undiagnosed Diseases Network. Our model outperforms several ranking approaches (including a commercially-supported system) by effectively prioritizing and combining representations obtained from traditional and recent text representation techniques. We elaborate on several aspects of our model and shed light on its improved performance.", "author": "Hadi Amiri; Mitra Mohtarami; Isaac Kohane", "authorids": "/h/hadi-amiri/; /m/mitra-mohtarami/; /i/isaac-kohane/", "bibtex": "@inproceedings{amiri-etal-2021-attentive,\n title = \"Attentive Multiview Text Representation for Differential Diagnosis\",\n author = \"Amiri, Hadi and\n Mohtarami, Mitra and\n Kohane, Isaac\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-short.128/\",\n doi = \"10.18653/v1/2021.acl-short.128\",\n pages = \"1012--1019\"\n}", "pdf": "https://aclanthology.org/2021.acl-short.128.pdf", "site": "https://aclanthology.org/2021.acl-short.128/", "pdf_size": 348340, "gs_citation": 7, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=3709373704670506376&as_sdt=5,33&sciodt=0,33&hl=en", "gs_version_total": 3, "aff": "Department of Computer Science, University of Massachusetts, Lowell + Department of Biomedical Informatics, Harvard University; MIT Computer Science and Arti\ufb01cial Intelligence Laboratory; Department of Biomedical Informatics, Harvard University", "aff_domain": "uml.edu;mit.edu;harvard.edu", "email": "uml.edu;mit.edu;harvard.edu", "github": "", "project": "https://clu.cs.uml.edu/tools.html", "author_num": 3, "aff_unique_index": "0+1;2;1", "aff_unique_norm": "University of Massachusetts Lowell;Harvard University;Massachusetts Institute of Technology", "aff_unique_dep": "Department of Computer Science;Department of Biomedical Informatics;Computer Science and Arti\ufb01cial Intelligence Laboratory", "aff_unique_url": "https://www.uml.edu;https://www.harvard.edu;https://www.mit.edu", "aff_unique_abbr": "UMass Lowell;Harvard;MIT", "aff_campus_unique_index": "0+1;1;1", "aff_campus_unique": "Lowell;Cambridge", "aff_country_unique_index": "0+0;0;0", "aff_country_unique": "United States" }, { "id": "2021.acl-long.95", "title": "AugNLG: Few-shot Natural Language Generation using Self-trained Data Augmentation", "track": "main", "status": "Long", "award": false, "abstract": "Natural Language Generation (NLG) is a key component in a task-oriented dialogue system, which converts the structured meaning representation (MR) to the natural language. For large-scale conversational systems, where it is common to have over hundreds of intents and thousands of slots, neither template-based approaches nor model-based approaches are scalable. Recently, neural NLGs started leveraging transfer learning and showed promising results in few-shot settings. This paper proposes AugNLG, a novel data augmentation approach that combines a self-trained neural retrieval model with a few-shot learned NLU model, to automatically create MR-to-Text data from open-domain texts. The proposed system mostly outperforms the state-of-the-art methods on the FewshotWOZ data in both BLEU and Slot Error Rate. We further confirm improved results on the FewshotSGD data and provide comprehensive analysis results on key components of our system. Our code and data are available at https://github.com/XinnuoXu/AugNLG.", "author": "Xinnuo Xu; Guoyin Wang; Young-Bum Kim; Sungjin Lee", "authorids": "/x/xinnuo-xu/; /g/guoyin-wang/; /y/young-bum-kim/; /s/sungjin-lee/", "bibtex": "@inproceedings{xu-etal-2021-augnlg,\n title = \"{A}ug{NLG}: Few-shot Natural Language Generation using Self-trained Data Augmentation\",\n author = \"Xu, Xinnuo and\n Wang, Guoyin and\n Kim, Young-Bum and\n Lee, Sungjin\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.95/\",\n doi = \"10.18653/v1/2021.acl-long.95\",\n pages = \"1183--1195\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.95.pdf", "site": "https://aclanthology.org/2021.acl-long.95/", "pdf_size": 880808, "gs_citation": 37, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=10042802075939087065&as_sdt=5,44&sciodt=0,44&hl=en", "gs_version_total": 6, "aff": "The Interaction Lab, Heriot-Watt University, Edinburgh; Amazon Alexa AI, Seattle, WA, USA; Amazon Alexa AI, Seattle, WA, USA; Amazon Alexa AI, Seattle, WA, USA", "aff_domain": "hw.ac.uk;amazon.com;amazon.com;amazon.com", "email": "hw.ac.uk;amazon.com;amazon.com;amazon.com", "github": "https://github.com/XinnuoXu/AugNLG", "project": "", "author_num": 4, "aff_unique_index": "0;1;1;1", "aff_unique_norm": "Heriot-Watt University;Amazon Alexa AI", "aff_unique_dep": "The Interaction Lab;AI", "aff_unique_url": "https://www.hw.ac.uk;https://www.amazon.com/alexa", "aff_unique_abbr": "HWU;Amazon Alexa AI", "aff_campus_unique_index": "0;1;1;1", "aff_campus_unique": "Edinburgh;Seattle", "aff_country_unique_index": "0;1;1;1", "aff_country_unique": "United Kingdom;United States" }, { "id": "2021.acl-long.400", "title": "AutoTinyBERT: Automatic Hyper-parameter Optimization for Efficient Pre-trained Language Models", "track": "main", "status": "Long", "award": false, "abstract": "Pre-trained language models (PLMs) have achieved great success in natural language processing. Most of PLMs follow the default setting of architecture hyper-parameters (e.g., the hidden dimension is a quarter of the intermediate dimension in feed-forward sub-networks) in BERT. Few studies have been conducted to explore the design of architecture hyper-parameters in BERT, especially for the more efficient PLMs with tiny sizes, which are essential for practical deployment on resource-constrained devices. In this paper, we adopt the one-shot Neural Architecture Search (NAS) to automatically search architecture hyper-parameters. Specifically, we carefully design the techniques of one-shot learning and the search space to provide an adaptive and efficient development way of tiny PLMs for various latency constraints. We name our method AutoTinyBERT and evaluate its effectiveness on the GLUE and SQuAD benchmarks. The extensive experiments show that our method outperforms both the SOTA search-based baseline (NAS-BERT) and the SOTA distillation-based methods (such as DistilBERT, TinyBERT, MiniLM, and MobileBERT). In addition, based on the obtained architectures, we propose a more efficient development method that is even faster than the development of a single PLM. The source code and models will be publicly available upon publication.", "author": "Yichun Yin; Cheng Chen; Lifeng Shang; Xin Jiang; Xiao Chen; Qun Liu", "authorids": "/y/yichun-yin/; /c/cheng-chen/; /l/lifeng-shang/; /x/xin-jiang/; /x/xiao-chen/; /q/qun-liu/", "bibtex": "@inproceedings{yin-etal-2021-autotinybert,\n title = \"{A}uto{T}iny{BERT}: Automatic Hyper-parameter Optimization for Efficient Pre-trained Language Models\",\n author = \"Yin, Yichun and\n Chen, Cheng and\n Shang, Lifeng and\n Jiang, Xin and\n Chen, Xiao and\n Liu, Qun\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.400/\",\n doi = \"10.18653/v1/2021.acl-long.400\",\n pages = \"5146--5157\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.400.pdf", "site": "https://aclanthology.org/2021.acl-long.400/", "pdf_size": 1463905, "gs_citation": 61, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=5572480408361949274&as_sdt=5,44&sciodt=0,44&hl=en", "gs_version_total": 5, "aff": "Huawei Noah\u2019s Ark Lab; Department of Computer Science and Technology, Tsinghua University; Huawei Noah\u2019s Ark Lab; Huawei Noah\u2019s Ark Lab; Huawei Noah\u2019s Ark Lab; Huawei Noah\u2019s Ark Lab", "aff_domain": "huawei.com;mails.tsinghua.edu.cn;huawei.com;huawei.com;huawei.com;huawei.com", "email": "huawei.com;mails.tsinghua.edu.cn;huawei.com;huawei.com;huawei.com;huawei.com", "github": "https://github.com/huawei-noah/Pretrained-Language-Model", "project": "", "author_num": 6, "aff_unique_index": "0;1;0;0;0;0", "aff_unique_norm": "Huawei;Tsinghua University", "aff_unique_dep": "Noah\u2019s Ark Lab;Department of Computer Science and Technology", "aff_unique_url": "https://www.huawei.com;https://www.tsinghua.edu.cn", "aff_unique_abbr": "Huawei;THU", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0;0;0;0;0;0", "aff_country_unique": "China" }, { "id": "2021.acl-long.206", "title": "Automated Concatenation of Embeddings for Structured Prediction", "track": "main", "status": "Long", "award": false, "abstract": "Pretrained contextualized embeddings are powerful word representations for structured prediction tasks. Recent work found that better word representations can be obtained by concatenating different types of embeddings. However, the selection of embeddings to form the best concatenated representation usually varies depending on the task and the collection of candidate embeddings, and the ever-increasing number of embedding types makes it a more difficult problem. In this paper, we propose Automated Concatenation of Embeddings (ACE) to automate the process of finding better concatenations of embeddings for structured prediction tasks, based on a formulation inspired by recent progress on neural architecture search. Specifically, a controller alternately samples a concatenation of embeddings, according to its current belief of the effectiveness of individual embedding types in consideration for a task, and updates the belief based on a reward. We follow strategies in reinforcement learning to optimize the parameters of the controller and compute the reward based on the accuracy of a task model, which is fed with the sampled concatenation as input and trained on a task dataset. Empirical results on 6 tasks and 21 datasets show that our approach outperforms strong baselines and achieves state-of-the-art performance with fine-tuned embeddings in all the evaluations.", "author": "Xinyu Wang; Yong Jiang; Nguyen Bach; Tao Wang; Zhongqiang Huang; Fei Huang; Kewei Tu", "authorids": "/x/xinyu-wang/; /y/yong-jiang/; /n/nguyen-bach/; /t/tao-wang/; /z/zhongqiang-huang/; /f/fei-huang/; /k/kewei-tu/", "bibtex": "@inproceedings{wang-etal-2021-automated,\n title = \"Automated Concatenation of Embeddings for Structured Prediction\",\n author = \"Wang, Xinyu and\n Jiang, Yong and\n Bach, Nguyen and\n Wang, Tao and\n Huang, Zhongqiang and\n Huang, Fei and\n Tu, Kewei\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.206/\",\n doi = \"10.18653/v1/2021.acl-long.206\",\n pages = \"2643--2660\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.206.pdf", "site": "https://aclanthology.org/2021.acl-long.206/", "pdf_size": 574163, "gs_citation": 228, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=15059824245889465831&as_sdt=5,44&sciodt=0,44&hl=en", "gs_version_total": 9, "aff": "School of Information Science and Technology, ShanghaiTech University + Shanghai Engineering Research Center of Intelligent Vision and Imaging + Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences + University of Chinese Academy of Sciences; DAMO Academy, Alibaba Group; DAMO Academy, Alibaba Group; DAMO Academy, Alibaba Group; DAMO Academy, Alibaba Group; DAMO Academy, Alibaba Group; School of Information Science and Technology, ShanghaiTech University + Shanghai Engineering Research Center of Intelligent Vision and Imaging + Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences + University of Chinese Academy of Sciences", "aff_domain": "shanghaitech.edu.cn;alibaba-inc.com;alibaba-inc.com;alibaba-inc.com;alibaba-inc.com;alibaba-inc.com;shanghaitech.edu.cn", "email": "shanghaitech.edu.cn;alibaba-inc.com;alibaba-inc.com;alibaba-inc.com;alibaba-inc.com;alibaba-inc.com;shanghaitech.edu.cn", "github": "https://github.com/Alibaba-NLP/ACE", "project": "", "author_num": 7, "aff_unique_index": "0+1+2+3;4;4;4;4;4;0+1+2+3", "aff_unique_norm": "ShanghaiTech University;Shanghai Engineering Research Center of Intelligent Vision and Imaging;Shanghai Institute of Microsystem and Information Technology;University of Chinese Academy of Sciences;Alibaba Group", "aff_unique_dep": "School of Information Science and Technology;;;;DAMO Academy", "aff_unique_url": "https://www.shanghaitech.edu.cn;;http://www.sim.cas.cn;http://www.ucas.ac.cn;https://www.alibaba-group.com", "aff_unique_abbr": "ShanghaiTech;;SIM;UCAS;Alibaba", "aff_campus_unique_index": "0+0;0+0", "aff_campus_unique": "Shanghai;", "aff_country_unique_index": "0+0+0+0;0;0;0;0;0;0+0+0+0", "aff_country_unique": "China" }, { "id": "2021.acl-long.108", "title": "Automated Generation of Storytelling Vocabulary from Photographs for use in AAC", "track": "main", "status": "Long", "award": false, "abstract": "Research on the application of NLP in symbol-based Augmentative and Alternative Communication (AAC) tools for improving social interaction support is scarce. We contribute a novel method for generating context-related vocabulary from photographs of personally relevant events aimed at supporting people with language impairments in retelling their past experiences. Performance was calculated with information retrieval concepts on the relevance of vocabulary generated for communicating a corpus of 9730 narrative phrases about events depicted in 1946 photographs. In comparison to a baseline generation composed of frequent English words, our method generated vocabulary with a 4.6 gain in mean average precision, regardless of the level of contextual information in the input photographs, and 6.9 for photographs in which contextual information was extracted correctly. We conclude by discussing how our findings provide insights for system optimization and usage.", "author": "Mauricio Fontana de Vargas; Karyn Moffatt", "authorids": "/m/mauricio-fontana-de-vargas/; /k/karyn-moffatt/", "bibtex": "@inproceedings{fontana-de-vargas-moffatt-2021-automated,\n title = \"{A}utomated Generation of Storytelling Vocabulary from Photographs for use in {AAC}\",\n author = \"Fontana de Vargas, Mauricio and\n Moffatt, Karyn\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.108/\",\n doi = \"10.18653/v1/2021.acl-long.108\",\n pages = \"1353--1364\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.108.pdf", "site": "https://aclanthology.org/2021.acl-long.108/", "pdf_size": 6270506, "gs_citation": 8, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=6205887072666576851&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 3, "aff": "School of Information Studies, McGill University, Montreal, Canada; School of Information Studies, McGill University, Montreal, Canada", "aff_domain": "mail.mcgill.ca;mcgill.ca", "email": "mail.mcgill.ca;mcgill.ca", "github": "", "project": "", "author_num": 2, "aff_unique_index": "0;0", "aff_unique_norm": "McGill University", "aff_unique_dep": "School of Information Studies", "aff_unique_url": "https://www.mcgill.ca", "aff_unique_abbr": "McGill", "aff_campus_unique_index": "0;0", "aff_campus_unique": "Montreal", "aff_country_unique_index": "0;0", "aff_country_unique": "Canada" }, { "id": "2021.acl-short.12", "title": "Automatic Fake News Detection: Are Models Learning to Reason?", "track": "main", "status": "Short", "award": false, "abstract": "Most fact checking models for automatic fake news detection are based on reasoning: given a claim with associated evidence, the models aim to estimate the claim veracity based on the supporting or refuting content within the evidence. When these models perform well, it is generally assumed to be due to the models having learned to reason over the evidence with regards to the claim. In this paper, we investigate this assumption of reasoning, by exploring the relationship and importance of both claim and evidence. Surprisingly, we find on political fact checking datasets that most often the highest effectiveness is obtained by utilizing only the evidence, as the impact of including the claim is either negligible or harmful to the effectiveness. This highlights an important problem in what constitutes evidence in existing approaches for automatic fake news detection.", "author": "Casper Hansen; Christian Hansen; Lucas Chaves Lima", "authorids": "/c/casper-hansen/; /c/christian-hansen/; /l/lucas-chaves-lima/", "bibtex": "@inproceedings{hansen-etal-2021-automatic,\n title = \"Automatic Fake News Detection: Are Models Learning to Reason?\",\n author = \"Hansen, Casper and\n Hansen, Christian and\n Chaves Lima, Lucas\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-short.12/\",\n doi = \"10.18653/v1/2021.acl-short.12\",\n pages = \"80--86\"\n}", "pdf": "https://aclanthology.org/2021.acl-short.12.pdf", "site": "https://aclanthology.org/2021.acl-short.12/", "pdf_size": 389396, "gs_citation": 20, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=17299755309949956004&as_sdt=5,38&sciodt=0,38&hl=en", "gs_version_total": 6, "aff": "University of Copenhagen; University of Copenhagen; University of Copenhagen", "aff_domain": "di.ku.dk;di.ku.dk;di.ku.dk", "email": "di.ku.dk;di.ku.dk;di.ku.dk", "github": "https://github.com/casperhansen/fake-news-reasoning", "project": "", "author_num": 3, "aff_unique_index": "0;0;0", "aff_unique_norm": "University of Copenhagen", "aff_unique_dep": "", "aff_unique_url": "https://www.ku.dk", "aff_unique_abbr": "UCPH", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0;0;0", "aff_country_unique": "Denmark" }, { "id": "2021.acl-long.463", "title": "Automatic ICD Coding via Interactive Shared Representation Networks with Self-distillation Mechanism", "track": "main", "status": "Long", "award": false, "abstract": "The ICD coding task aims at assigning codes of the International Classification of Diseases in clinical notes. Since manual coding is very laborious and prone to errors, many methods have been proposed for the automatic ICD coding task. However, existing works either ignore the long-tail of code frequency or the noisy clinical notes. To address the above issues, we propose an Interactive Shared Representation Network with Self-Distillation Mechanism. Specifically, an interactive shared representation network targets building connections among codes while modeling the co-occurrence, consequently alleviating the long-tail problem. Moreover, to cope with the noisy text issue, we encourage the model to focus on the clinical note\u2019s noteworthy part and extract valuable information through a self-distillation learning mechanism. Experimental results on two MIMIC datasets demonstrate the effectiveness of our method.", "author": "Tong Zhou; Pengfei Cao; Yubo Chen; Kang Liu; Jun Zhao; Kun Niu; Weifeng Chong; Shengping Liu", "authorids": "/t/tong-zhou/; /p/pengfei-cao/; /y/yubo-chen/; /k/kang-liu/; /j/jun-zhao/; /k/kun-niu/; /w/weifeng-chong/; /s/shengping-liu/", "bibtex": "@inproceedings{zhou-etal-2021-automatic,\n title = \"Automatic {ICD} Coding via Interactive Shared Representation Networks with Self-distillation Mechanism\",\n author = \"Zhou, Tong and\n Cao, Pengfei and\n Chen, Yubo and\n Liu, Kang and\n Zhao, Jun and\n Niu, Kun and\n Chong, Weifeng and\n Liu, Shengping\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.463/\",\n doi = \"10.18653/v1/2021.acl-long.463\",\n pages = \"5948--5957\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.463.pdf", "site": "https://aclanthology.org/2021.acl-long.463/", "pdf_size": 749616, "gs_citation": 67, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=6902753147453938626&as_sdt=5,33&sciodt=0,33&hl=en", "gs_version_total": 3, "aff": "National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences+School of Artificial Intelligence University of Chinese Academy of Sciences; National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences+School of Artificial Intelligence University of Chinese Academy of Sciences; National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences+School of Artificial Intelligence University of Chinese Academy of Sciences; National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences+School of Artificial Intelligence University of Chinese Academy of Sciences; National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences+School of Artificial Intelligence University of Chinese Academy of Sciences; Beijing University of Posts and Telecommunications; Beijing Unisound Information Technology Co., Ltd; Beijing Unisound Information Technology Co., Ltd", "aff_domain": "bupt.edu.cn;nlpr.ia.ac.cn;nlpr.ia.ac.cn;nlpr.ia.ac.cn;nlpr.ia.ac.cn;bupt.edu.cn;unisound.com;unisound.com", "email": "bupt.edu.cn;nlpr.ia.ac.cn;nlpr.ia.ac.cn;nlpr.ia.ac.cn;nlpr.ia.ac.cn;bupt.edu.cn;unisound.com;unisound.com", "github": "", "project": "", "author_num": 8, "aff_unique_index": "0+1;0+1;0+1;0+1;0+1;2;3;3", "aff_unique_norm": "Chinese Academy of Sciences;University of Chinese Academy of Sciences;Beijing University of Posts and Telecommunications;Beijing Unisound Information Technology Co., Ltd", "aff_unique_dep": "Institute of Automation;School of Artificial Intelligence;;", "aff_unique_url": "http://www.ia.cas.cn;http://www.ucas.ac.cn;http://www.bupt.edu.cn/;", "aff_unique_abbr": "CAS;UCAS;BUPT;", "aff_campus_unique_index": ";;;;;1", "aff_campus_unique": ";Beijing", "aff_country_unique_index": "0+0;0+0;0+0;0+0;0+0;0;0;0", "aff_country_unique": "China" }, { "id": "2021.acl-short.132", "title": "Avoiding Overlap in Data Augmentation for AMR-to-Text Generation", "track": "main", "status": "Short", "award": false, "abstract": "Leveraging additional unlabeled data to boost model performance is common practice in machine learning and natural language processing. For generation tasks, if there is overlap between the additional data and the target text evaluation data, then training on the additional data is training on answers of the test set. This leads to overly-inflated scores with the additional data compared to real-world testing scenarios and problems when comparing models. We study the AMR dataset and Gigaword, which is popularly used for improving AMR-to-text generators, and find significant overlap between Gigaword and a subset of the AMR dataset. We propose methods for excluding parts of Gigaword to remove this overlap, and show that our approach leads to a more realistic evaluation of the task of AMR-to-text generation. Going forward, we give simple best-practice recommendations for leveraging additional data in AMR-to-text generation.", "author": "Wenchao Du; Jeffrey Flanigan", "authorids": "/w/wenchao-du/; /j/jeffrey-flanigan/", "bibtex": "@inproceedings{du-flanigan-2021-avoiding,\n title = \"Avoiding Overlap in Data Augmentation for {AMR}-to-Text Generation\",\n author = \"Du, Wenchao and\n Flanigan, Jeffrey\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-short.132/\",\n doi = \"10.18653/v1/2021.acl-short.132\",\n pages = \"1043--1048\"\n}", "pdf": "https://aclanthology.org/2021.acl-short.132.pdf", "site": "https://aclanthology.org/2021.acl-short.132/", "pdf_size": 161914, "gs_citation": 2, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=669923482251130663&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 4, "aff": "University of California, Santa Cruz; University of California, Santa Cruz", "aff_domain": "gmail.com;ucsc.edu", "email": "gmail.com;ucsc.edu", "github": "https://github.com/jlab-nlp/amr-clean", "project": "", "author_num": 2, "aff_unique_index": "0;0", "aff_unique_norm": "University of California, Santa Cruz", "aff_unique_dep": "", "aff_unique_url": "https://www.ucsc.edu", "aff_unique_abbr": "UCSC", "aff_campus_unique_index": "0;0", "aff_campus_unique": "Santa Cruz", "aff_country_unique_index": "0;0", "aff_country_unique": "United States" }, { "id": "2021.acl-long.116", "title": "BACO: A Background Knowledge- and Content-Based Framework for Citing Sentence Generation", "track": "main", "status": "Long", "award": false, "abstract": "In this paper, we focus on the problem of citing sentence generation, which entails generating a short text to capture the salient information in a cited paper and the connection between the citing and cited paper. We present BACO, a BAckground knowledge- and COntent-based framework for citing sentence generation, which considers two types of information: (1) background knowledge by leveraging structural information from a citation network; and (2) content, which represents in-depth information about what to cite and why to cite. First, a citation network is encoded to provide background knowledge. Second, we apply salience estimation to identify what to cite by estimating the importance of sentences in the cited paper. During the decoding stage, both types of information are combined to facilitate the text generation, and then we conduct a joint training for the generator and citation function classification to make the model aware of why to cite. Our experimental results show that our framework outperforms comparative baselines.", "author": "Yubin Ge; Ly Dinh; Xiaofeng Liu; Jinsong Su; Ziyao Lu; Ante Wang; Jana Diesner", "authorids": "/y/yubin-ge/; /l/ly-dinh/; /x/xiaofeng-liu/; /j/jinsong-su/; /z/ziyao-lu/; /a/ante-wang/; /j/jana-diesner/", "bibtex": "@inproceedings{ge-etal-2021-baco,\n title = \"{BACO}: A Background Knowledge- and Content-Based Framework for Citing Sentence Generation\",\n author = \"Ge, Yubin and\n Dinh, Ly and\n Liu, Xiaofeng and\n Su, Jinsong and\n Lu, Ziyao and\n Wang, Ante and\n Diesner, Jana\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.116/\",\n doi = \"10.18653/v1/2021.acl-long.116\",\n pages = \"1466--1478\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.116.pdf", "site": "https://aclanthology.org/2021.acl-long.116/", "pdf_size": 999872, "gs_citation": 41, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=16817421623352507866&as_sdt=5,44&sciodt=0,44&hl=en", "gs_version_total": 8, "aff": "University of Illinois at Urbana-Champaign, USA; University of Illinois at Urbana-Champaign, USA; Harvard University, USA; Xiamen University, China; Xiamen University, China; Xiamen University, China; University of Illinois at Urbana-Champaign, USA", "aff_domain": "illinois.edu;illinois.edu;illinois.edu;xmu.edu.cn; ; ; ", "email": "illinois.edu;illinois.edu;illinois.edu;xmu.edu.cn; ; ; ", "github": "", "project": "", "author_num": 7, "aff_unique_index": "0;0;1;2;2;2;0", "aff_unique_norm": "University of Illinois at Urbana-Champaign;Harvard University;Xiamen University", "aff_unique_dep": ";;", "aff_unique_url": "https://illinois.edu;https://www.harvard.edu;https://www.xmu.edu.cn", "aff_unique_abbr": "UIUC;Harvard;XMU", "aff_campus_unique_index": "0;0;0", "aff_campus_unique": "Urbana-Champaign;", "aff_country_unique_index": "0;0;0;1;1;1;0", "aff_country_unique": "United States;China" }, { "id": "2021.acl-long.472", "title": "BASS: Boosting Abstractive Summarization with Unified Semantic Graph", "track": "main", "status": "Long", "award": false, "abstract": "Abstractive summarization for long-document or multi-document remains challenging for the Seq2Seq architecture, as Seq2Seq is not good at analyzing long-distance relations in text. In this paper, we present BASS, a novel framework for Boosting Abstractive Summarization based on a unified Semantic graph, which aggregates co-referent phrases distributing across a long range of context and conveys rich relations between phrases. Further, a graph-based encoder-decoder model is proposed to improve both the document representation and summary generation process by leveraging the graph structure. Specifically, several graph augmentation methods are designed to encode both the explicit and implicit relations in the text while the graph-propagation attention mechanism is developed in the decoder to select salient content into the summary. Empirical results show that the proposed architecture brings substantial improvements for both long-document and multi-document summarization tasks.", "author": "Wenhao Wu; Wei Li; Xinyan Xiao; Jiachen Liu; Ziqiang Cao; Sujian Li; Hua Wu; Haifeng Wang", "authorids": "/w/wenhao-wu/; /w/wei-li/; /x/xinyan-xiao/; /j/jiachen-liu/; /z/ziqiang-cao/; /s/sujian-li/; /h/hua-wu/; /h/haifeng-wang/", "bibtex": "@inproceedings{wu-etal-2021-bass,\n title = \"{BASS}: Boosting Abstractive Summarization with Unified Semantic Graph\",\n author = \"Wu, Wenhao and\n Li, Wei and\n Xiao, Xinyan and\n Liu, Jiachen and\n Cao, Ziqiang and\n Li, Sujian and\n Wu, Hua and\n Wang, Haifeng\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.472/\",\n doi = \"10.18653/v1/2021.acl-long.472\",\n pages = \"6052--6067\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.472.pdf", "site": "https://aclanthology.org/2021.acl-long.472/", "pdf_size": 515783, "gs_citation": 51, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=11077617293237548509&as_sdt=5,33&sciodt=0,33&hl=en", "gs_version_total": 5, "aff": "Key Laboratory of Computational Linguistics, MOE, Peking University; Baidu Inc., Beijing, China; Baidu Inc., Beijing, China; Baidu Inc., Beijing, China; Institute of Artificial Intelligence, Soochow University, China; Key Laboratory of Computational Linguistics, MOE, Peking University+Baidu Inc., Beijing, China; Baidu Inc., Beijing, China; Baidu Inc., Beijing, China", "aff_domain": "pku.edu.cn;baidu.com;baidu.com;baidu.com;suda.edu.cn;pku.edu.cn;baidu.com;baidu.com", "email": "pku.edu.cn;baidu.com;baidu.com;baidu.com;suda.edu.cn;pku.edu.cn;baidu.com;baidu.com", "github": "", "project": "", "author_num": 8, "aff_unique_index": "0;1;1;1;2;0+1;1;1", "aff_unique_norm": "Peking University;Baidu Inc.;Soochow University", "aff_unique_dep": "Key Laboratory of Computational Linguistics;;Institute of Artificial Intelligence", "aff_unique_url": "http://www.pku.edu.cn;https://www.baidu.com;https://www.soochow.edu.cn", "aff_unique_abbr": "PKU;Baidu;", "aff_campus_unique_index": "1;1;1;1;1;1", "aff_campus_unique": ";Beijing", "aff_country_unique_index": "0;0;0;0;0;0+0;0;0", "aff_country_unique": "China" }, { "id": "2021.acl-long.280", "title": "BERT is to NLP what AlexNet is to CV: Can Pre-Trained Language Models Identify Analogies?", "track": "main", "status": "Long", "award": false, "abstract": "Analogies play a central role in human commonsense reasoning. The ability to recognize analogies such as \u201ceye is to seeing what ear is to hearing\u201d, sometimes referred to as analogical proportions, shape how we structure knowledge and understand language. Surprisingly, however, the task of identifying such analogies has not yet received much attention in the language model era. In this paper, we analyze the capabilities of transformer-based language models on this unsupervised task, using benchmarks obtained from educational settings, as well as more commonly used datasets. We find that off-the-shelf language models can identify analogies to a certain extent, but struggle with abstract and complex relations, and results are highly sensitive to model architecture and hyperparameters. Overall the best results were obtained with GPT-2 and RoBERTa, while configurations using BERT were not able to outperform word embedding models. Our results raise important questions for future work about how, and to what extent, pre-trained language models capture knowledge about abstract semantic relations.", "author": "Asahi Ushio; Luis Espinosa Anke; Steven Schockaert; Jose Camacho-Collados", "authorids": "/a/asahi-ushio/; /l/luis-espinosa-anke/; /s/steven-schockaert/; /j/jose-camacho-collados/", "bibtex": "@inproceedings{ushio-etal-2021-bert,\n title = \"{BERT} is to {NLP} what {A}lex{N}et is to {CV}: Can Pre-Trained Language Models Identify Analogies?\",\n author = \"Ushio, Asahi and\n Espinosa Anke, Luis and\n Schockaert, Steven and\n Camacho-Collados, Jose\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.280/\",\n doi = \"10.18653/v1/2021.acl-long.280\",\n pages = \"3609--3624\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.280.pdf", "site": "https://aclanthology.org/2021.acl-long.280/", "pdf_size": 785291, "gs_citation": 98, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=8283665108986497589&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 10, "aff": "Cardiff NLP, School of Computer Science and Informatics, Cardiff University, United Kingdom; Cardiff NLP, School of Computer Science and Informatics, Cardiff University, United Kingdom; Cardiff NLP, School of Computer Science and Informatics, Cardiff University, United Kingdom; Cardiff NLP, School of Computer Science and Informatics, Cardiff University, United Kingdom", "aff_domain": "cardiff.ac.uk;cardiff.ac.uk;cardiff.ac.uk;cardiff.ac.uk", "email": "cardiff.ac.uk;cardiff.ac.uk;cardiff.ac.uk;cardiff.ac.uk", "github": "https://github.com/asahi417/analogy-language-model", "project": "", "author_num": 4, "aff_unique_index": "0;0;0;0", "aff_unique_norm": "Cardiff University", "aff_unique_dep": "School of Computer Science and Informatics", "aff_unique_url": "https://www.cardiff.ac.uk", "aff_unique_abbr": "Cardiff", "aff_campus_unique_index": "0;0;0;0", "aff_campus_unique": "Cardiff", "aff_country_unique_index": "0;0;0;0", "aff_country_unique": "United Kingdom" }, { "id": "2021.acl-long.164", "title": "BERTAC: Enhancing Transformer-based Language Models with Adversarially Pretrained Convolutional Neural Networks", "track": "main", "status": "Long", "award": false, "abstract": "Transformer-based language models (TLMs), such as BERT, ALBERT and GPT-3, have shown strong performance in a wide range of NLP tasks and currently dominate the field of NLP. However, many researchers wonder whether these models can maintain their dominance forever. Of course, we do not have answers now, but, as an attempt to find better neural architectures and training schemes, we pretrain a simple CNN using a GAN-style learning scheme and Wikipedia data, and then integrate it with standard TLMs. We show that on the GLUE tasks, the combination of our pretrained CNN with ALBERT outperforms the original ALBERT and achieves a similar performance to that of SOTA. Furthermore, on open-domain QA (Quasar-T and SearchQA), the combination of the CNN with ALBERT or RoBERTa achieved stronger performance than SOTA and the original TLMs. We hope that this work provides a hint for developing a novel strong network architecture along with its training scheme. Our source code and models are available at https://github.com/nict-wisdom/bertac.", "author": "Jong-Hoon Oh; Ryu Iida; Julien Kloetzer; Kentaro Torisawa", "authorids": "/j/jong-hoon-oh/; /r/ryu-iida/; /j/julien-kloetzer/; /k/kentaro-torisawa/", "bibtex": "@inproceedings{oh-etal-2021-bertac,\n title = \"{BERTAC}: Enhancing Transformer-based Language Models with Adversarially Pretrained Convolutional Neural Networks\",\n author = \"Oh, Jong-Hoon and\n Iida, Ryu and\n Kloetzer, Julien and\n Torisawa, Kentaro\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.164/\",\n doi = \"10.18653/v1/2021.acl-long.164\",\n pages = \"2103--2115\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.164.pdf", "site": "https://aclanthology.org/2021.acl-long.164/", "pdf_size": 392688, "gs_citation": 8, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=9375581078119872580&as_sdt=40005&sciodt=0,10&hl=en", "gs_version_total": 5, "aff": "Data-driven Intelligent System Research Center (DIRECT), National Institute of Information and Communications Technology (NICT); Graduate School of Science and Technology, NAIST; Data-driven Intelligent System Research Center (DIRECT), National Institute of Information and Communications Technology (NICT); Graduate School of Science and Technology, NAIST", "aff_domain": "nict.go.jp;nict.go.jp;nict.go.jp;nict.go.jp", "email": "nict.go.jp;nict.go.jp;nict.go.jp;nict.go.jp", "github": "https://github.com/nict-wisdom/bertac", "project": "", "author_num": 4, "aff_unique_index": "0;1;0;1", "aff_unique_norm": "National Institute of Information and Communications Technology;Nara Institute of Science and Technology", "aff_unique_dep": "Data-driven Intelligent System Research Center (DIRECT);Graduate School of Science and Technology", "aff_unique_url": "https://www.nict.go.jp/;https://www.naist.jp", "aff_unique_abbr": "NICT;NAIST", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0;0;0;0", "aff_country_unique": "Japan" }, { "id": "2021.acl-long.503", "title": "BERTGen: Multi-task Generation through BERT", "track": "main", "status": "Long", "award": false, "abstract": "We present BERTGen, a novel, generative, decoder-only model which extends BERT by fusing multimodal and multilingual pre-trained models VL-BERT and M-BERT, respectively. BERTGen is auto-regressively trained for language generation tasks, namely image captioning, machine translation and multimodal machine translation, under a multi-task setting. With a comprehensive set of evaluations, we show that BERTGen outperforms many strong baselines across the tasks explored. We also show BERTGen\u2019s ability for zero-shot language generation, where it exhibits competitive performance to supervised counterparts. Finally, we conduct ablation studies which demonstrate that BERTGen substantially benefits from multi-tasking and effectively transfers relevant inductive biases from the pre-trained models.", "author": "Faidon Mitzalis; Ozan Caglayan; Pranava Madhyastha; Lucia Specia", "authorids": "/f/faidon-mitzalis/; /o/ozan-caglayan/; /p/pranava-swaroop-madhyastha/; /l/lucia-specia/", "bibtex": "@inproceedings{mitzalis-etal-2021-bertgen,\n title = \"{BERTG}en: Multi-task Generation through {BERT}\",\n author = \"Mitzalis, Faidon and\n Caglayan, Ozan and\n Madhyastha, Pranava and\n Specia, Lucia\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.503/\",\n doi = \"10.18653/v1/2021.acl-long.503\",\n pages = \"6440--6455\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.503.pdf", "site": "https://aclanthology.org/2021.acl-long.503/", "pdf_size": 2428945, "gs_citation": 12, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=4756096105600265440&as_sdt=80000005&sciodt=0,23&hl=en", "gs_version_total": 7, "aff": "Department of Computing, Imperial College London, UK; Department of Computing, Imperial College London, UK; Department of Computing, Imperial College London, UK; Department of Computing, Imperial College London, UK + Department of Computer Science, University of Sheffield, UK", "aff_domain": "gmail.com;ic.ac.uk;ic.ac.uk;ic.ac.uk", "email": "gmail.com;ic.ac.uk;ic.ac.uk;ic.ac.uk", "github": "", "project": "", "author_num": 4, "aff_unique_index": "0;0;0;0+1", "aff_unique_norm": "Imperial College London;University of Sheffield", "aff_unique_dep": "Department of Computing;Department of Computer Science", "aff_unique_url": "https://www.imperial.ac.uk;https://www.sheffield.ac.uk", "aff_unique_abbr": "Imperial;Sheffield", "aff_campus_unique_index": "0;0;0;0", "aff_campus_unique": "London;", "aff_country_unique_index": "0;0;0;0+0", "aff_country_unique": "United Kingdom" }, { "id": "2021.acl-short.115", "title": "BERTTune: Fine-Tuning Neural Machine Translation with BERTScore", "track": "main", "status": "Short", "award": false, "abstract": "Neural machine translation models are often biased toward the limited translation references seen during training. To amend this form of overfitting, in this paper we propose fine-tuning the models with a novel training objective based on the recently-proposed BERTScore evaluation metric. BERTScore is a scoring function based on contextual embeddings that overcomes the typical limitations of n-gram-based metrics (e.g. synonyms, paraphrases), allowing translations that are different from the references, yet close in the contextual embedding space, to be treated as substantially correct. To be able to use BERTScore as a training objective, we propose three approaches for generating soft predictions, allowing the network to remain completely differentiable end-to-end. Experiments carried out over four, diverse language pairs show improvements of up to 0.58 pp (3.28%) in BLEU score and up to 0.76 pp (0.98%) in BERTScore (F_BERT) when fine-tuning a strong baseline.", "author": "Inigo Jauregi Unanue; Jacob Parnell; Massimo Piccardi", "authorids": "/i/inigo-jauregi-unanue/; /j/jacob-parnell/; /m/massimo-piccardi/", "bibtex": "@inproceedings{jauregi-unanue-etal-2021-berttune,\n title = \"{BERTT}une: Fine-Tuning Neural Machine Translation with {BERTS}core\",\n author = \"Jauregi Unanue, Inigo and\n Parnell, Jacob and\n Piccardi, Massimo\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-short.115/\",\n doi = \"10.18653/v1/2021.acl-short.115\",\n pages = \"915--924\"\n}", "pdf": "https://aclanthology.org/2021.acl-short.115.pdf", "site": "https://aclanthology.org/2021.acl-short.115/", "pdf_size": 662579, "gs_citation": 43, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=4159896694095013943&as_sdt=5,44&sciodt=0,44&hl=en", "gs_version_total": 6, "aff": "University of Technology Sydney, NSW 2007, Australia+RoZetta Technology, NSW 2000, Australia; University of Technology Sydney, NSW 2007, Australia+RoZetta Technology, NSW 2000, Australia; University of Technology Sydney, NSW 2007, Australia", "aff_domain": "rozettatechnology.com;rozettatechnology.com;uts.edu.au", "email": "rozettatechnology.com;rozettatechnology.com;uts.edu.au", "github": "", "project": "", "author_num": 3, "aff_unique_index": "0+1;0+1;0", "aff_unique_norm": "University of Technology Sydney;RoZetta Technology", "aff_unique_dep": ";", "aff_unique_url": "https://www.uts.edu.au;", "aff_unique_abbr": "UTS;", "aff_campus_unique_index": "0;0;0", "aff_campus_unique": "Sydney;", "aff_country_unique_index": "0+0;0+0;0", "aff_country_unique": "Australia" }, { "id": "2021.acl-long.482", "title": "BERTifying the Hidden Markov Model for Multi-Source Weakly Supervised Named Entity Recognition", "track": "main", "status": "Long", "award": false, "abstract": "We study the problem of learning a named entity recognition (NER) tagger using noisy labels from multiple weak supervision sources. Though cheap to obtain, the labels from weak supervision sources are often incomplete, inaccurate, and contradictory, making it difficult to learn an accurate NER model. To address this challenge, we propose a conditional hidden Markov model (CHMM), which can effectively infer true labels from multi-source noisy labels in an unsupervised way. CHMM enhances the classic hidden Markov model with the contextual representation power of pre-trained language models. Specifically, CHMM learns token-wise transition and emission probabilities from the BERT embeddings of the input tokens to infer the latent true labels from noisy observations. We further refine CHMM with an alternate-training approach (CHMM-ALT). It fine-tunes a BERT-NER model with the labels inferred by CHMM, and this BERT-NER\u2019s output is regarded as an additional weak source to train the CHMM in return. Experiments on four NER benchmarks from various domains show that our method outperforms state-of-the-art weakly supervised NER models by wide margins.", "author": "Yinghao Li; Pranav Shetty; Lucas Liu; Chao Zhang; Le Song", "authorids": "/y/yinghao-li/; /p/pranav-shetty/; /l/lucas-liu/; /c/chao-zhang-tu/; /l/le-song/", "bibtex": "@inproceedings{li-etal-2021-bertifying,\n title = \"{BERT}ifying the Hidden {M}arkov Model for Multi-Source Weakly Supervised Named Entity Recognition\",\n author = \"Li, Yinghao and\n Shetty, Pranav and\n Liu, Lucas and\n Zhang, Chao and\n Song, Le\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.482/\",\n doi = \"10.18653/v1/2021.acl-long.482\",\n pages = \"6178--6190\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.482.pdf", "site": "https://aclanthology.org/2021.acl-long.482/", "pdf_size": 824639, "gs_citation": 39, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=5024252915154147057&as_sdt=5,44&sciodt=0,44&hl=en", "gs_version_total": 7, "aff": "Georgia Institute of Technology; Georgia Institute of Technology; Georgia Institute of Technology; Georgia Institute of Technology; Mohamed bin Zayed University of Artificial Intelligence", "aff_domain": "gatech.edu;gatech.edu;gatech.edu;gatech.edu;mbzuai.ac.ae", "email": "gatech.edu;gatech.edu;gatech.edu;gatech.edu;mbzuai.ac.ae", "github": "", "project": "", "author_num": 5, "aff_unique_index": "0;0;0;0;1", "aff_unique_norm": "Georgia Institute of Technology;Mohamed bin Zayed University of Artificial Intelligence", "aff_unique_dep": ";", "aff_unique_url": "https://www.gatech.edu;https://www.mbzuai.ac.ae", "aff_unique_abbr": "Georgia Tech;MBZUAI", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0;0;0;0;1", "aff_country_unique": "United States;United Arab Emirates" }, { "id": "2021.acl-long.148", "title": "Bad Seeds: Evaluating Lexical Methods for Bias Measurement", "track": "main", "status": "Long", "award": false, "abstract": "A common factor in bias measurement methods is the use of hand-curated seed lexicons, but there remains little guidance for their selection. We gather seeds used in prior work, documenting their common sources and rationales, and in case studies of three English-language corpora, we enumerate the different types of social biases and linguistic features that, once encoded in the seeds, can affect subsequent bias measurements. Seeds developed in one context are often re-used in other contexts, but documentation and evaluation remain necessary precursors to relying on seeds for sensitive measurements.", "author": "Maria Antoniak; David Mimno", "authorids": "/m/maria-antoniak/; /d/david-mimno/", "bibtex": "@inproceedings{antoniak-mimno-2021-bad,\n title = \"Bad Seeds: Evaluating Lexical Methods for Bias Measurement\",\n author = \"Antoniak, Maria and\n Mimno, David\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.148/\",\n doi = \"10.18653/v1/2021.acl-long.148\",\n pages = \"1889--1904\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.148.pdf", "site": "https://aclanthology.org/2021.acl-long.148/", "pdf_size": 4987765, "gs_citation": 79, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=4170250682479147930&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 4, "aff": "Cornell University; Cornell University", "aff_domain": "cornell.edu;cornell.edu", "email": "cornell.edu;cornell.edu", "github": "", "project": "", "author_num": 2, "aff_unique_index": "0;0", "aff_unique_norm": "Cornell University", "aff_unique_dep": "", "aff_unique_url": "https://www.cornell.edu", "aff_unique_abbr": "Cornell", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0;0", "aff_country_unique": "United States" }, { "id": "2021.acl-long.428", "title": "BanditMTL: Bandit-based Multi-task Learning for Text Classification", "track": "main", "status": "Long", "award": false, "abstract": "Task variance regularization, which can be used to improve the generalization of Multi-task Learning (MTL) models, remains unexplored in multi-task text classification. Accordingly, to fill this gap, this paper investigates how the task might be effectively regularized, and consequently proposes a multi-task learning method based on adversarial multi-armed bandit. The proposed method, named BanditMTL, regularizes the task variance by means of a mirror gradient ascent-descent algorithm. Adopting BanditMTL in the multi-task text classification context is found to achieve state-of-the-art performance. The results of extensive experiments back up our theoretical analysis and validate the superiority of our proposals.", "author": "Yuren Mao; Zekai Wang; Weiwei Liu; Xuemin Lin; Wenbin Hu", "authorids": "/y/yuren-mao/; /z/zekai-wang/; /w/weiwei-liu/; /x/xuemin-lin/; /w/wenbin-hu/", "bibtex": "@inproceedings{mao-etal-2021-banditmtl,\n title = \"{B}andit{MTL}: Bandit-based Multi-task Learning for Text Classification\",\n author = \"Mao, Yuren and\n Wang, Zekai and\n Liu, Weiwei and\n Lin, Xuemin and\n Hu, Wenbin\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.428/\",\n doi = \"10.18653/v1/2021.acl-long.428\",\n pages = \"5506--5516\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.428.pdf", "site": "https://aclanthology.org/2021.acl-long.428/", "pdf_size": 1574361, "gs_citation": 19, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=13018615202572845688&as_sdt=400005&sciodt=0,14&hl=en", "gs_version_total": 2, "aff": "School of Computer Science and Engineering, University of New South Wales; School of Computer Science, Wuhan University; School of Computer Science, Wuhan University; School of Computer Science and Engineering, University of New South Wales; School of Computer Science, Wuhan University", "aff_domain": "unsw.edu.au;gmail.com;gmail.com;cse.unsw.edu.au;whu.edu.cn", "email": "unsw.edu.au;gmail.com;gmail.com;cse.unsw.edu.au;whu.edu.cn", "github": "", "project": "", "author_num": 5, "aff_unique_index": "0;1;1;0;1", "aff_unique_norm": "University of New South Wales;Wuhan University", "aff_unique_dep": "School of Computer Science and Engineering;School of Computer Science", "aff_unique_url": "https://www.unsw.edu.au;http://www.whu.edu.cn", "aff_unique_abbr": "UNSW;WHU", "aff_campus_unique_index": "1;1;1", "aff_campus_unique": ";Wuhan", "aff_country_unique_index": "0;1;1;0;1", "aff_country_unique": "Australia;China" }, { "id": "2021.acl-long.364", "title": "Benchmarking Scalable Methods for Streaming Cross Document Entity Coreference", "track": "main", "status": "Long", "award": false, "abstract": "Streaming cross document entity coreference (CDC) systems disambiguate mentions of named entities in a scalable manner via incremental clustering. Unlike other approaches for named entity disambiguation (e.g., entity linking), streaming CDC allows for the disambiguation of entities that are unknown at inference time. Thus, it is well-suited for processing streams of data where new entities are frequently introduced. Despite these benefits, this task is currently difficult to study, as existing approaches are either evaluated on datasets that are no longer available, or omit other crucial details needed to ensure fair comparison. In this work, we address this issue by compiling a large benchmark adapted from existing free datasets, and performing a comprehensive evaluation of a number of novel and existing baseline models. We investigate: how to best encode mentions, which clustering algorithms are most effective for grouping mentions, how models transfer to different domains, and how bounding the number of mentions tracked during inference impacts performance. Our results show that the relative performance of neural and feature-based mention encoders varies across different domains, and in most cases the best performance is achieved using a combination of both approaches. We also find that performance is minimally impacted by limiting the number of tracked mentions.", "author": "Robert L Logan IV; Andrew McCallum; Sameer Singh; Dan Bikel", "authorids": "/r/robert-l-logan-iv/; /a/andrew-mccallum/; /s/sameer-singh/; /d/daniel-m-bikel/", "bibtex": "@inproceedings{logan-iv-etal-2021-benchmarking,\n title = \"Benchmarking Scalable Methods for Streaming Cross Document Entity Coreference\",\n author = \"Logan IV, Robert L and\n McCallum, Andrew and\n Singh, Sameer and\n Bikel, Dan\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.364/\",\n doi = \"10.18653/v1/2021.acl-long.364\",\n pages = \"4717--4731\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.364.pdf", "site": "https://aclanthology.org/2021.acl-long.364/", "pdf_size": 600297, "gs_citation": 13, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=17469596691421778618&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 3, "aff": "University of California, Irvine + Google Research; Google Research; University of California, Irvine; Google Research + University of Massachusetts, Amherst", "aff_domain": "uci.edu;google.com;uci.edu;google.com", "email": "uci.edu;google.com;uci.edu;google.com", "github": "https://github.com/rloganiv/streaming-cdc", "project": "", "author_num": 4, "aff_unique_index": "0+1;1;0;1+2", "aff_unique_norm": "University of California, Irvine;Google;University of Massachusetts Amherst", "aff_unique_dep": ";Google Research;", "aff_unique_url": "https://www.uci.edu;https://research.google;https://www.umass.edu", "aff_unique_abbr": "UCI;Google Research;UMass Amherst", "aff_campus_unique_index": "0+1;1;0;1+2", "aff_campus_unique": "Irvine;Mountain View;Amherst", "aff_country_unique_index": "0+0;0;0;0+0", "aff_country_unique": "United States" }, { "id": "2021.acl-long.286", "title": "Best of Both Worlds: Making High Accuracy Non-incremental Transformer-based Disfluency Detection Incremental", "track": "main", "status": "Long", "award": false, "abstract": "While Transformer-based text classifiers pre-trained on large volumes of text have yielded significant improvements on a wide range of computational linguistics tasks, their implementations have been unsuitable for live incremental processing thus far, operating only on the level of complete sentence inputs. We address the challenge of introducing methods for word-by-word left-to-right incremental processing to Transformers such as BERT, models without an intrinsic sense of linear order. We modify the training method and live decoding of non-incremental models to detect speech disfluencies with minimum latency and without pre-segmentation of dialogue acts. We experiment with several decoding methods to predict the rightward context of the word currently being processed using a GPT-2 language model and apply a BERT-based disfluency detector to sequences, including predicted words. We show our method of incrementalising Transformers maintains most of their high non-incremental performance while operating strictly incrementally. We also evaluate our models\u2019 incremental performance to establish the trade-off between incremental performance and final performance, using different prediction strategies. We apply our system to incremental speech recognition results as they arrive into a live system and achieve state-of-the-art results in this setting.", "author": "Morteza Rohanian; Julian Hough", "authorids": "/m/morteza-rohanian/; /j/julian-hough/", "bibtex": "@inproceedings{rohanian-hough-2021-best,\n title = \"Best of Both Worlds: Making High Accuracy Non-incremental Transformer-based Disfluency Detection Incremental\",\n author = \"Rohanian, Morteza and\n Hough, Julian\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.286/\",\n doi = \"10.18653/v1/2021.acl-long.286\",\n pages = \"3693--3703\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.286.pdf", "site": "https://aclanthology.org/2021.acl-long.286/", "pdf_size": 933097, "gs_citation": 28, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=6916850593244081718&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 5, "aff": "Cognitive Science Group, School of Electronic Engineering and Computer Science, Queen Mary University of London; Cognitive Science Group, School of Electronic Engineering and Computer Science, Queen Mary University of London", "aff_domain": "qmul.ac.uk;qmul.ac.uk", "email": "qmul.ac.uk;qmul.ac.uk", "github": "", "project": "", "author_num": 2, "aff_unique_index": "0;0", "aff_unique_norm": "Queen Mary University of London", "aff_unique_dep": "School of Electronic Engineering and Computer Science", "aff_unique_url": "https://www.qmul.ac.uk", "aff_unique_abbr": "QMUL", "aff_campus_unique_index": "0;0", "aff_campus_unique": "London", "aff_country_unique_index": "0;0", "aff_country_unique": "United Kingdom" }, { "id": "2021.acl-long.179", "title": "Better than Average: Paired Evaluation of NLP systems", "track": "main", "status": "Long", "award": false, "abstract": "Evaluation in NLP is usually done by comparing the scores of competing systems independently averaged over a common set of test instances. In this work, we question the use of averages for aggregating evaluation scores into a final number used to decide which system is best, since the average, as well as alternatives such as the median, ignores the pairing arising from the fact that systems are evaluated on the same test instances. We illustrate the importance of taking the instancelevel pairing of evaluation scores into account and demonstrate, both theoretically and empirically, the advantages of aggregation methods based on pairwise comparisons, such as the Bradley\u2013Terry (BT) model, a mechanism based on the estimated probability that a given system scores better than another on the test set. By re-evaluating 296 real NLP evaluation setups across four tasks and 18 evaluation metrics, we show that the choice of aggregation mechanism matters and yields different conclusions as to which systems are state of the art in about 30% of the setups. To facilitate the adoption of pairwise evaluation, we release a practical tool for performing the full analysis of evaluation scores with the mean, median, BT, and two variants of BT (Elo and TrueSkill), alongside functionality for appropriate statistical testing.", "author": "Maxime Peyrard; Wei Zhao; Steffen Eger; Robert West", "authorids": "/m/maxime-peyrard/; /w/wei-zhao/; /s/steffen-eger/; /r/robert-west/", "bibtex": "@inproceedings{peyrard-etal-2021-better,\n title = \"Better than Average: Paired Evaluation of {NLP} systems\",\n author = \"Peyrard, Maxime and\n Zhao, Wei and\n Eger, Steffen and\n West, Robert\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.179/\",\n doi = \"10.18653/v1/2021.acl-long.179\",\n pages = \"2301--2315\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.179.pdf", "site": "https://aclanthology.org/2021.acl-long.179/", "pdf_size": 650483, "gs_citation": 30, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=7212206152756327606&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 6, "aff": "EPFL, Switzerland; Technische Universit\u00e4t Darmstadt, Germany; Technische Universit\u00e4t Darmstadt, Germany; EPFL, Switzerland", "aff_domain": "epfl.ch;epfl.ch;aiphes.tu-darmstadt.de;aiphes.tu-darmstadt.de", "email": "epfl.ch;epfl.ch;aiphes.tu-darmstadt.de;aiphes.tu-darmstadt.de", "github": "", "project": "", "author_num": 4, "aff_unique_index": "0;1;1;0", "aff_unique_norm": "\u00c9cole Polytechnique F\u00e9d\u00e9rale de Lausanne;Technische Universit\u00e4t Darmstadt", "aff_unique_dep": ";", "aff_unique_url": "https://www.epfl.ch;https://www.tu-darmstadt.de", "aff_unique_abbr": "EPFL;TUD", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0;1;1;0", "aff_country_unique": "Switzerland;Germany" }, { "id": "2021.acl-short.75", "title": "Beyond Laurel/Yanny: An Autoencoder-Enabled Search for Polyperceivable Audio", "track": "main", "status": "Short", "award": false, "abstract": "The famous \u201claurel/yanny\u201d phenomenon references an audio clip that elicits dramatically different responses from different listeners. For the original clip, roughly half the population hears the word \u201claurel,\u201d while the other half hears \u201cyanny.\u201d How common are such \u201cpolyperceivable\u201d audio clips? In this paper we apply ML techniques to study the prevalence of polyperceivability in spoken language. We devise a metric that correlates with polyperceivability of audio clips, use it to efficiently find new \u201claurel/yanny\u201d-type examples, and validate these results with human experiments. Our results suggest that polyperceivable examples are surprisingly prevalent in natural language, existing for >2% of English words.", "author": "Kartik Chandra; Chuma Kabaghe; Gregory Valiant", "authorids": "/k/kartik-chandra/; /c/chuma-kabaghe/; /g/gregory-valiant/", "bibtex": "@inproceedings{chandra-etal-2021-beyond,\n title = \"Beyond Laurel/Yanny: An Autoencoder-Enabled Search for Polyperceivable Audio\",\n author = \"Chandra, Kartik and\n Kabaghe, Chuma and\n Valiant, Gregory\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-short.75/\",\n doi = \"10.18653/v1/2021.acl-short.75\",\n pages = \"593--598\"\n}", "pdf": "https://aclanthology.org/2021.acl-short.75.pdf", "site": "https://aclanthology.org/2021.acl-short.75/", "pdf_size": 319465, "gs_citation": 7, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=7097692426726347519&as_sdt=5,47&sciodt=0,47&hl=en", "gs_version_total": 5, "aff": "Stanford University; Stanford University; Stanford University", "aff_domain": "cs.stanford.edu;alumni.stanford.edu;cs.stanford.edu", "email": "cs.stanford.edu;alumni.stanford.edu;cs.stanford.edu", "github": "", "project": "https://theory.stanford.edu/~valiant/polyperceivable/index.html", "author_num": 3, "aff_unique_index": "0;0;0", "aff_unique_norm": "Stanford University", "aff_unique_dep": "", "aff_unique_url": "https://www.stanford.edu", "aff_unique_abbr": "Stanford", "aff_campus_unique_index": "0;0;0", "aff_campus_unique": "Stanford", "aff_country_unique_index": "0;0;0", "aff_country_unique": "United States" }, { "id": "2021.acl-long.562", "title": "Beyond Noise: Mitigating the Impact of Fine-grained Semantic Divergences on Neural Machine Translation", "track": "main", "status": "Long", "award": false, "abstract": "While it has been shown that Neural Machine Translation (NMT) is highly sensitive to noisy parallel training samples, prior work treats all types of mismatches between source and target as noise. As a result, it remains unclear how samples that are mostly equivalent but contain a small number of semantically divergent tokens impact NMT training. To close this gap, we analyze the impact of different types of fine-grained semantic divergences on Transformer models. We show that models trained on synthetic divergences output degenerated text more frequently and are less confident in their predictions. Based on these findings, we introduce a divergent-aware NMT framework that uses factors to help NMT recover from the degradation caused by naturally occurring divergences, improving both translation quality and model calibration on EN-FR tasks.", "author": "Eleftheria Briakou; Marine Carpuat", "authorids": "/e/eleftheria-briakou/; /m/marine-carpuat/", "bibtex": "@inproceedings{briakou-carpuat-2021-beyond,\n title = \"Beyond Noise: Mitigating the Impact of Fine-grained Semantic Divergences on Neural Machine Translation\",\n author = \"Briakou, Eleftheria and\n Carpuat, Marine\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.562/\",\n doi = \"10.18653/v1/2021.acl-long.562\",\n pages = \"7236--7249\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.562.pdf", "site": "https://aclanthology.org/2021.acl-long.562/", "pdf_size": 1241672, "gs_citation": 16, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=4644383389200589686&as_sdt=400005&sciodt=0,14&hl=en", "gs_version_total": 5, "aff": "Department of Computer Science, University of Maryland; Department of Computer Science, University of Maryland", "aff_domain": "cs.umd.edu;cs.umd.edu", "email": "cs.umd.edu;cs.umd.edu", "github": "", "project": "", "author_num": 2, "aff_unique_index": "0;0", "aff_unique_norm": "University of Maryland", "aff_unique_dep": "Department of Computer Science", "aff_unique_url": "https://www/umd.edu", "aff_unique_abbr": "UMD", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0;0", "aff_country_unique": "United States" }, { "id": "2021.acl-long.506", "title": "Beyond Offline Mapping: Learning Cross-lingual Word Embeddings through Context Anchoring", "track": "main", "status": "Long", "award": false, "abstract": "Recent research on cross-lingual word embeddings has been dominated by unsupervised mapping approaches that align monolingual embeddings. Such methods critically rely on those embeddings having a similar structure, but it was recently shown that the separate training in different languages causes departures from this assumption. In this paper, we propose an alternative approach that does not have this limitation, while requiring a weak seed dictionary (e.g., a list of identical words) as the only form of supervision. Rather than aligning two fixed embedding spaces, our method works by fixing the target language embeddings, and learning a new set of embeddings for the source language that are aligned with them. To that end, we use an extension of skip-gram that leverages translated context words as anchor points, and incorporates self-learning and iterative restarts to reduce the dependency on the initial dictionary. Our approach outperforms conventional mapping methods on bilingual lexicon induction, and obtains competitive results in the downstream XNLI task.", "author": "Aitor Ormazabal; Mikel Artetxe; Aitor Soroa; Gorka Labaka; Eneko Agirre", "authorids": "/a/aitor-ormazabal/; /m/mikel-artetxe/; /a/aitor-soroa/; /g/gorka-labaka/; /e/eneko-agirre/", "bibtex": "@inproceedings{ormazabal-etal-2021-beyond,\n title = \"Beyond Offline Mapping: Learning Cross-lingual Word Embeddings through Context Anchoring\",\n author = \"Ormazabal, Aitor and\n Artetxe, Mikel and\n Soroa, Aitor and\n Labaka, Gorka and\n Agirre, Eneko\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.506/\",\n doi = \"10.18653/v1/2021.acl-long.506\",\n pages = \"6479--6489\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.506.pdf", "site": "https://aclanthology.org/2021.acl-long.506/", "pdf_size": 230729, "gs_citation": 15, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=458572516832001218&as_sdt=400005&sciodt=0,14&hl=en", "gs_version_total": 4, "aff": "HiTZ Center, University of the Basque Country (UPV/EHU); Facebook AI Research; HiTZ Center, University of the Basque Country (UPV/EHU); HiTZ Center, University of the Basque Country (UPV/EHU); HiTZ Center, University of the Basque Country (UPV/EHU)", "aff_domain": "ehu.eus;fb.com;ehu.eus;ehu.eus;ehu.eus", "email": "ehu.eus;fb.com;ehu.eus;ehu.eus;ehu.eus", "github": "", "project": "", "author_num": 5, "aff_unique_index": "0;1;0;0;0", "aff_unique_norm": "University of the Basque Country;Facebook", "aff_unique_dep": "HiTZ Center;Facebook AI Research", "aff_unique_url": "https://www.ehu.eus/en;https://research.facebook.com", "aff_unique_abbr": "UPV/EHU;FAIR", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0;1;0;0;0", "aff_country_unique": "Spain;United States" }, { "id": "2021.acl-long.200", "title": "Beyond Sentence-Level End-to-End Speech Translation: Context Helps", "track": "main", "status": "Long", "award": false, "abstract": "Document-level contextual information has shown benefits to text-based machine translation, but whether and how context helps end-to-end (E2E) speech translation (ST) is still under-studied. We fill this gap through extensive experiments using a simple concatenation-based context-aware ST model, paired with adaptive feature selection on speech encodings for computational efficiency. We investigate several decoding approaches, and introduce in-model ensemble decoding which jointly performs document- and sentence-level translation using the same model. Our results on the MuST-C benchmark with Transformer demonstrate the effectiveness of context to E2E ST. Compared to sentence-level ST, context-aware ST obtains better translation quality (+0.18-2.61 BLEU), improves pronoun and homophone translation, shows better robustness to (artificial) audio segmentation errors, and reduces latency and flicker to deliver higher quality for simultaneous translation.", "author": "Biao Zhang; Ivan Titov; Barry Haddow; Rico Sennrich", "authorids": "/b/biao-zhang/; /i/ivan-titov/; /b/barry-haddow/; /r/rico-sennrich/", "bibtex": "@inproceedings{zhang-etal-2021-beyond,\n title = \"Beyond Sentence-Level End-to-End Speech Translation: Context Helps\",\n author = \"Zhang, Biao and\n Titov, Ivan and\n Haddow, Barry and\n Sennrich, Rico\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.200/\",\n doi = \"10.18653/v1/2021.acl-long.200\",\n pages = \"2566--2578\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.200.pdf", "site": "https://aclanthology.org/2021.acl-long.200/", "pdf_size": 637950, "gs_citation": 23, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=1478708601816014629&as_sdt=5,44&sciodt=0,44&hl=en", "gs_version_total": 8, "aff": "School of Informatics, University of Edinburgh; ILLC, University of Amsterdam + School of Informatics, University of Edinburgh; School of Informatics, University of Edinburgh; Department of Computational Linguistics, University of Zurich + School of Informatics, University of Edinburgh", "aff_domain": "ed.ac.uk;inf.ed.ac.uk;inf.ed.ac.uk;cl.uzh.ch", "email": "ed.ac.uk;inf.ed.ac.uk;inf.ed.ac.uk;cl.uzh.ch", "github": "https://github.com/bzhangGo/zero", "project": "", "author_num": 4, "aff_unique_index": "0;1+0;0;2+0", "aff_unique_norm": "University of Edinburgh;University of Amsterdam;University of Zurich", "aff_unique_dep": "School of Informatics;ILLC;Department of Computational Linguistics", "aff_unique_url": "https://www.ed.ac.uk;https://www.uva.nl;https://www.unizh.ch", "aff_unique_abbr": "Edinburgh;UvA;UZH", "aff_campus_unique_index": "0;1+0;0;0", "aff_campus_unique": "Edinburgh;Amsterdam;", "aff_country_unique_index": "0;1+0;0;2+0", "aff_country_unique": "United Kingdom;Netherlands;Switzerland" }, { "id": "2021.acl-long.67", "title": "Bilingual Lexicon Induction via Unsupervised Bitext Construction and Word Alignment", "track": "main", "status": "Long", "award": false, "abstract": "Bilingual lexicons map words in one language to their translations in another, and are typically induced by learning linear projections to align monolingual word embedding spaces. In this paper, we show it is possible to produce much higher quality lexicons with methods that combine (1) unsupervised bitext mining and (2) unsupervised word alignment. Directly applying a pipeline that uses recent algorithms for both subproblems significantly improves induced lexicon quality and further gains are possible by learning to filter the resulting lexical entries, with both unsupervised and semi-supervised schemes. Our final model outperforms the state of the art on the BUCC 2020 shared task by 14 F1 points averaged over 12 language pairs, while also providing a more interpretable approach that allows for rich reasoning of word meaning in context. Further analysis of our output and the standard reference lexicons suggests they are of comparable quality, and new benchmarks may be needed to measure further progress on this task.", "author": "Haoyue Shi; Luke Zettlemoyer; Sida I. Wang", "authorids": "/f/freda-shi/; /l/luke-zettlemoyer/; /s/sida-i-wang/", "bibtex": "@inproceedings{shi-etal-2021-bilingual,\n title = \"Bilingual Lexicon Induction via Unsupervised Bitext Construction and Word Alignment\",\n author = \"Shi, Haoyue and\n Zettlemoyer, Luke and\n Wang, Sida I.\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.67/\",\n doi = \"10.18653/v1/2021.acl-long.67\",\n pages = \"813--826\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.67.pdf", "site": "https://aclanthology.org/2021.acl-long.67/", "pdf_size": 1640299, "gs_citation": 41, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=4841621456068479944&as_sdt=5,33&sciodt=0,33&hl=en", "gs_version_total": 4, "aff": "TTI-Chicago; University of Washington + Facebook AI Research; Facebook AI Research", "aff_domain": "ttic.edu;fb.com;fb.com", "email": "ttic.edu;fb.com;fb.com", "github": "https://github.com/facebookresearch/bitext-lexind", "project": "", "author_num": 3, "aff_unique_index": "0;1+2;2", "aff_unique_norm": "Toyota Technological Institute at Chicago;University of Washington;Facebook", "aff_unique_dep": ";;Facebook AI Research", "aff_unique_url": "https://www.tti-chicago.org;https://www.washington.edu;https://research.facebook.com", "aff_unique_abbr": "TTI;UW;FAIR", "aff_campus_unique_index": "0;", "aff_campus_unique": "Chicago;", "aff_country_unique_index": "0;0+0;0", "aff_country_unique": "United States" }, { "id": "2021.acl-short.65", "title": "Bilingual Mutual Information Based Adaptive Training for Neural Machine Translation", "track": "main", "status": "Short", "award": false, "abstract": "Recently, token-level adaptive training has achieved promising improvement in machine translation, where the cross-entropy loss function is adjusted by assigning different training weights to different tokens, in order to alleviate the token imbalance problem. However, previous approaches only use static word frequency information in the target language without considering the source language, which is insufficient for bilingual tasks like machine translation. In this paper, we propose a novel bilingual mutual information (BMI) based adaptive objective, which measures the learning difficulty for each target token from the perspective of bilingualism, and assigns an adaptive weight accordingly to improve token-level adaptive training. This method assigns larger training weights to tokens with higher BMI, so that easy tokens are updated with coarse granularity while difficult tokens are updated with fine granularity. Experimental results on WMT14 English-to-German and WMT19 Chinese-to-English demonstrate the superiority of our approach compared with the Transformer baseline and previous token-level adaptive training approaches. Further analyses confirm that our method can improve the lexical diversity.", "author": "Yangyifan Xu; Yijin Liu; Fandong Meng; Jiajun Zhang; Jinan Xu; Jie Zhou", "authorids": "/y/yangyifan-xu/; /y/yijin-liu/; /f/fandong-meng/; /j/jiajun-zhang/; /j/jinan-xu/; /j/jie-zhou/", "bibtex": "@inproceedings{xu-etal-2021-bilingual,\n title = \"Bilingual Mutual Information Based Adaptive Training for Neural Machine Translation\",\n author = \"Xu, Yangyifan and\n Liu, Yijin and\n Meng, Fandong and\n Zhang, Jiajun and\n Xu, Jinan and\n Zhou, Jie\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-short.65/\",\n doi = \"10.18653/v1/2021.acl-short.65\",\n pages = \"511--516\"\n}", "pdf": "https://aclanthology.org/2021.acl-short.65.pdf", "site": "https://aclanthology.org/2021.acl-short.65/", "pdf_size": 357326, "gs_citation": 13, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=7527324986628299216&as_sdt=5,33&sciodt=0,33&hl=en", "gs_version_total": 4, "aff": "Beijing Jiaotong University, China+Pattern Recognition Center, WeChat AI, Tencent Inc, China; Beijing Jiaotong University, China+Pattern Recognition Center, WeChat AI, Tencent Inc, China+National Laboratory of Pattern Recognition, Institute of Automation, CAS+School of Arti\ufb01cial Intelligence, University of Chinese Academy of Sciences; Pattern Recognition Center, WeChat AI, Tencent Inc, China; National Laboratory of Pattern Recognition, Institute of Automation, CAS+School of Arti\ufb01cial Intelligence, University of Chinese Academy of Sciences; Beijing Jiaotong University, China; Pattern Recognition Center, WeChat AI, Tencent Inc, China", "aff_domain": "ia.ac.cn;gmail.com;tencent.com;tencent.com;nlpr.ia.ac.cn;bjtu.edu.cn", "email": "ia.ac.cn;gmail.com;tencent.com;tencent.com;nlpr.ia.ac.cn;bjtu.edu.cn", "github": "", "project": "", "author_num": 6, "aff_unique_index": "0+1;0+1+2+3;1;2+3;0;1", "aff_unique_norm": "Beijing Jiaotong University;Tencent Inc;Chinese Academy of Sciences;University of Chinese Academy of Sciences", "aff_unique_dep": ";Pattern Recognition Center, WeChat AI;Institute of Automation;School of Arti\ufb01cial Intelligence", "aff_unique_url": "http://www.bjtu.edu.cn;https://www.tencent.com;http://www.ia.cas.cn;http://www.ucas.ac.cn", "aff_unique_abbr": "BJTU;Tencent;CAS;UCAS", "aff_campus_unique_index": ";;", "aff_campus_unique": "", "aff_country_unique_index": "0+0;0+0+0+0;0;0+0;0;0", "aff_country_unique": "China" }, { "id": "2021.acl-long.334", "title": "BinaryBERT: Pushing the Limit of BERT Quantization", "track": "main", "status": "Long", "award": false, "abstract": "The rapid development of large pre-trained language models has greatly increased the demand for model compression techniques, among which quantization is a popular solution. In this paper, we propose BinaryBERT, which pushes BERT quantization to the limit by weight binarization. We find that a binary BERT is hard to be trained directly than a ternary counterpart due to its complex and irregular loss landscape. Therefore, we propose ternary weight splitting, which initializes BinaryBERT by equivalently splitting from a half-sized ternary network. The binary model thus inherits the good performance of the ternary one, and can be further enhanced by fine-tuning the new architecture after splitting. Empirical results show that our BinaryBERT has only a slight performance drop compared with the full-precision model while being 24x smaller, achieving the state-of-the-art compression results on the GLUE and SQuAD benchmarks. Code will be released.", "author": "Haoli Bai; Wei Zhang; Lu Hou; Lifeng Shang; Jin Jin; Xin Jiang; Qun Liu; Michael Lyu; Irwin King", "authorids": "/h/haoli-bai/; /w/wei-zhang/; /l/lu-hou/; /l/lifeng-shang/; /j/jin-jin/; /x/xin-jiang/; /q/qun-liu/; /m/michael-lyu/; /i/irwin-king/", "bibtex": "@inproceedings{bai-etal-2021-binarybert,\n title = \"{B}inary{BERT}: Pushing the Limit of {BERT} Quantization\",\n author = \"Bai, Haoli and\n Zhang, Wei and\n Hou, Lu and\n Shang, Lifeng and\n Jin, Jin and\n Jiang, Xin and\n Liu, Qun and\n Lyu, Michael and\n King, Irwin\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.334/\",\n doi = \"10.18653/v1/2021.acl-long.334\",\n pages = \"4334--4348\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.334.pdf", "site": "https://aclanthology.org/2021.acl-long.334/", "pdf_size": 10347935, "gs_citation": 256, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=4738854168274854065&as_sdt=5,48&sciodt=0,48&hl=en", "gs_version_total": 4, "aff": "The Chinese University of Hong Kong; Huawei Noah\u2019s Ark Lab; Huawei Noah\u2019s Ark Lab; Huawei Noah\u2019s Ark Lab; Huawei Technologies Co., Ltd.; Huawei Noah\u2019s Ark Lab; Huawei Noah\u2019s Ark Lab; The Chinese University of Hong Kong; The Chinese University of Hong Kong", "aff_domain": "cse.cuhk.edu.hk;huawei.com;huawei.com;huawei.com;huawei.com;huawei.com;huawei.com;cse.cuhk.edu.hk;cse.cuhk.edu.hk", "email": "cse.cuhk.edu.hk;huawei.com;huawei.com;huawei.com;huawei.com;huawei.com;huawei.com;cse.cuhk.edu.hk;cse.cuhk.edu.hk", "github": "", "project": "", "author_num": 9, "aff_unique_index": "0;1;1;1;2;1;1;0;0", "aff_unique_norm": "The Chinese University of Hong Kong;Huawei;Huawei Technologies", "aff_unique_dep": ";Noah\u2019s Ark Lab;", "aff_unique_url": "https://www.cuhk.edu.hk;https://www.huawei.com;https://www.huawei.com", "aff_unique_abbr": "CUHK;Huawei;Huawei", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0;0;0;0;0;0;0;0;0", "aff_country_unique": "China" }, { "id": "2021.acl-long.145", "title": "Bird\u2019s Eye: Probing for Linguistic Graph Structures with a Simple Information-Theoretic Approach", "track": "main", "status": "Long", "award": false, "abstract": "NLP has a rich history of representing our prior understanding of language in the form of graphs. Recent work on analyzing contextualized text representations has focused on hand-designed probe models to understand how and to what extent do these representations encode a particular linguistic phenomenon. However, due to the inter-dependence of various phenomena and randomness of training probe models, detecting how these representations encode the rich information in these linguistic graphs remains a challenging problem. In this paper, we propose a new information-theoretic probe, Bird\u2019s Eye, which is a fairly simple probe method for detecting if and how these representations encode the information in these linguistic graphs. Instead of using model performance, our probe takes an information-theoretic view of probing and estimates the mutual information between the linguistic graph embedded in a continuous space and the contextualized word representations. Furthermore, we also propose an approach to use our probe to investigate localized linguistic information in the linguistic graphs using perturbation analysis. We call this probing setup Worm\u2019s Eye. Using these probes, we analyze the BERT models on its ability to encode a syntactic and a semantic graph structure, and find that these models encode to some degree both syntactic as well as semantic information; albeit syntactic information to a greater extent.", "author": "Yifan Hou; Mrinmaya Sachan", "authorids": "/y/yifan-hou/; /m/mrinmaya-sachan/", "bibtex": "@inproceedings{hou-sachan-2021-birds,\n title = \"Bird`s Eye: Probing for Linguistic Graph Structures with a Simple Information-Theoretic Approach\",\n author = \"Hou, Yifan and\n Sachan, Mrinmaya\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.145/\",\n doi = \"10.18653/v1/2021.acl-long.145\",\n pages = \"1844--1859\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.145.pdf", "site": "https://aclanthology.org/2021.acl-long.145/", "pdf_size": 645867, "gs_citation": 11, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=7357180742374173612&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 5, "aff": "Department of Computer Science, ETH Z\u00fcrich; Department of Computer Science, ETH Z\u00fcrich", "aff_domain": "inf.ethz.ch;inf.ethz.ch", "email": "inf.ethz.ch;inf.ethz.ch", "github": "https://github.com/yifan-h/Graph_Probe-Birds_Eye", "project": "", "author_num": 2, "aff_unique_index": "0;0", "aff_unique_norm": "ETH Z\u00fcrich", "aff_unique_dep": "Department of Computer Science", "aff_unique_url": "https://www.ethz.ch", "aff_unique_abbr": "ETHZ", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0;0", "aff_country_unique": "Switzerland" }, { "id": "2021.acl-long.14", "title": "BoB: BERT Over BERT for Training Persona-based Dialogue Models from Limited Personalized Data", "track": "main", "status": "Long", "award": false, "abstract": "Maintaining a consistent persona is essential for dialogue agents. Although tremendous advancements have been brought, the limited-scale of annotated personalized dialogue datasets is still a barrier towards training robust and consistent persona-based dialogue models. This work shows how this challenge can be addressed by disentangling persona-based dialogue generation into two sub-tasks with a novel BERT-over-BERT (BoB) model. Specifically, the model consists of a BERT-based encoder and two BERT-based decoders, where one decoder is for response generation, and another is for consistency understanding. In particular, to learn the ability of consistency understanding from large-scale non-dialogue inference data, we train the second decoder in an unlikelihood manner. Under different limited data settings, both automatic and human evaluations demonstrate that the proposed model outperforms strong baselines in response quality and persona consistency.", "author": "Haoyu Song; Yan Wang; Kaiyan Zhang; Wei-Nan Zhang; Ting Liu", "authorids": "/h/haoyu-song/; /y/yan-wang/; /k/kaiyan-zhang/; /w/weinan-zhang/; /t/ting-liu/", "bibtex": "@inproceedings{song-etal-2021-bob,\n title = \"{B}o{B}: {BERT} Over {BERT} for Training Persona-based Dialogue Models from Limited Personalized Data\",\n author = \"Song, Haoyu and\n Wang, Yan and\n Zhang, Kaiyan and\n Zhang, Wei-Nan and\n Liu, Ting\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.14/\",\n doi = \"10.18653/v1/2021.acl-long.14\",\n pages = \"167--177\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.14.pdf", "site": "https://aclanthology.org/2021.acl-long.14/", "pdf_size": 408517, "gs_citation": 140, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=6709842365871447404&as_sdt=5,31&sciodt=0,31&hl=en", "gs_version_total": 8, "aff": "Research Center for Social Computing and Information Retrieval, Harbin Institute of Technology, Heilongjiang, China; Research Center for Social Computing and Information Retrieval, Harbin Institute of Technology, Heilongjiang, China; Research Center for Social Computing and Information Retrieval, Harbin Institute of Technology, Heilongjiang, China; Research Center for Social Computing and Information Retrieval, Harbin Institute of Technology, Heilongjiang, China; Research Center for Social Computing and Information Retrieval, Harbin Institute of Technology, Heilongjiang, China", "aff_domain": "ir.hit.edu.cn;gmail.com;ir.hit.edu.cn;ir.hit.edu.cn;ir.hit.edu.cn", "email": "ir.hit.edu.cn;gmail.com;ir.hit.edu.cn;ir.hit.edu.cn;ir.hit.edu.cn", "github": "", "project": "", "author_num": 5, "aff_unique_index": "0;0;0;0;0", "aff_unique_norm": "Harbin Institute of Technology", "aff_unique_dep": "Research Center for Social Computing and Information Retrieval", "aff_unique_url": "http://www.hit.edu.cn/", "aff_unique_abbr": "HIT", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0;0;0;0;0", "aff_country_unique": "China" }, { "id": "2021.acl-long.402", "title": "Bootstrapped Unsupervised Sentence Representation Learning", "track": "main", "status": "Long", "award": false, "abstract": "As high-quality labeled data is scarce, unsupervised sentence representation learning has attracted much attention. In this paper, we propose a new framework with a two-branch Siamese Network which maximizes the similarity between two augmented views of each sentence. Specifically, given one augmented view of the input sentence, the online network branch is trained by predicting the representation yielded by the target network of the same sentence under another augmented view. Meanwhile, the target network branch is bootstrapped with a moving average of the online network. The proposed method significantly outperforms other state-of-the-art unsupervised methods on semantic textual similarity (STS) and classification tasks. It can be adopted as a post-training procedure to boost the performance of the supervised methods. We further extend our method for learning multilingual sentence representations and demonstrate its effectiveness on cross-lingual STS tasks. Our code is available at https://github.com/yanzhangnlp/BSL.", "author": "Yan Zhang; Ruidan He; Zuozhu Liu; Lidong Bing; Haizhou Li", "authorids": "/y/yan-zhang/; /r/ruidan-he/; /z/zuozhu-liu/; /l/lidong-bing/; /h/haizhou-li/", "bibtex": "@inproceedings{zhang-etal-2021-bootstrapped,\n title = \"Bootstrapped Unsupervised Sentence Representation Learning\",\n author = \"Zhang, Yan and\n He, Ruidan and\n Liu, Zuozhu and\n Bing, Lidong and\n Li, Haizhou\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.402/\",\n doi = \"10.18653/v1/2021.acl-long.402\",\n pages = \"5168--5180\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.402.pdf", "site": "https://aclanthology.org/2021.acl-long.402/", "pdf_size": 425268, "gs_citation": 40, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=16997120855601141829&as_sdt=5,33&sciodt=0,33&hl=en", "gs_version_total": 3, "aff": "National University of Singapore; DAMO Academy, Alibaba Group; ZJU-UIUC Institute; DAMO Academy, Alibaba Group; National University of Singapore + Kriston AI Lab, China", "aff_domain": "nus.edu.sg;alibaba-inc.com;intl.zju.edu.cn;alibaba-inc.com;nus.edu.sg", "email": "nus.edu.sg;alibaba-inc.com;intl.zju.edu.cn;alibaba-inc.com;nus.edu.sg", "github": "https://github.com/yanzhangnlp/BSL", "project": "", "author_num": 5, "aff_unique_index": "0;1;2;1;0+3", "aff_unique_norm": "National University of Singapore;Alibaba Group;Zhejiang University - University of Illinois at Urbana-Champaign Institute;Kriston AI Lab", "aff_unique_dep": ";DAMO Academy;;", "aff_unique_url": "https://www.nus.edu.sg;https://www.alibaba-group.com;;", "aff_unique_abbr": "NUS;Alibaba;ZJU-UIUC;", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0;1;1;1;0+1", "aff_country_unique": "Singapore;China" }, { "id": "2021.acl-long.319", "title": "Breaking Down Walls of Text: How Can NLP Benefit Consumer Privacy?", "track": "main", "status": "Long", "award": false, "abstract": "Privacy plays a crucial role in preserving democratic ideals and personal autonomy. The dominant legal approach to privacy in many jurisdictions is the \u201cNotice and Choice\u201d paradigm, where privacy policies are the primary instrument used to convey information to users. However, privacy policies are long and complex documents that are difficult for users to read and comprehend. We discuss how language technologies can play an important role in addressing this information gap, reporting on initial progress towards helping three specific categories of stakeholders take advantage of digital privacy policies: consumers, enterprises, and regulators. Our goal is to provide a roadmap for the development and use of language technologies to empower users to reclaim control over their privacy, limit privacy harms, and rally research efforts from the community towards addressing an issue with large social impact. We highlight many remaining opportunities to develop language technologies that are more precise or nuanced in the way in which they use the text of privacy policies.", "author": "Abhilasha Ravichander; Alan W Black; Thomas Norton; Shomir Wilson; Norman Sadeh", "authorids": "/a/abhilasha-ravichander/; /a/alan-w-black/; /t/thomas-norton/; /s/shomir-wilson/; /n/norman-sadeh/", "bibtex": "@inproceedings{ravichander-etal-2021-breaking,\n title = \"Breaking Down Walls of Text: How Can {NLP} Benefit Consumer Privacy?\",\n author = \"Ravichander, Abhilasha and\n Black, Alan W and\n Norton, Thomas and\n Wilson, Shomir and\n Sadeh, Norman\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.319/\",\n doi = \"10.18653/v1/2021.acl-long.319\",\n pages = \"4125--4140\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.319.pdf", "site": "https://aclanthology.org/2021.acl-long.319/", "pdf_size": 573308, "gs_citation": 34, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=17937192143046991158&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 11, "aff": "Carnegie Mellon University, Pittsburgh, PA; Carnegie Mellon University, Pittsburgh, PA; Fordham Law School, New York, NY; Penn State University, University Park, PA; Carnegie Mellon University, Pittsburgh, PA", "aff_domain": "cs.cmu.edu;cs.cmu.edu;law.fordham.edu;psu.edu;cs.cmu.edu", "email": "cs.cmu.edu;cs.cmu.edu;law.fordham.edu;psu.edu;cs.cmu.edu", "github": "", "project": "", "author_num": 5, "aff_unique_index": "0;0;1;2;0", "aff_unique_norm": "Carnegie Mellon University;Fordham University;Penn State University", "aff_unique_dep": ";Fordham Law School;", "aff_unique_url": "https://www.cmu.edu;https://www.fordham.edu;https://www.psu.edu", "aff_unique_abbr": "CMU;Fordham;PSU", "aff_campus_unique_index": "0;0;1;2;0", "aff_campus_unique": "Pittsburgh;New York;University Park", "aff_country_unique_index": "0;0;0;0;0", "aff_country_unique": "United States" }, { "id": "2021.acl-long.53", "title": "Breaking Down the Invisible Wall of Informal Fallacies in Online Discussions", "track": "main", "status": "Long", "award": false, "abstract": "People debate on a variety of topics on online platforms such as Reddit, or Facebook. Debates can be lengthy, with users exchanging a wealth of information and opinions. However, conversations do not always go smoothly, and users sometimes engage in unsound argumentation techniques to prove a claim. These techniques are called fallacies. Fallacies are persuasive arguments that provide insufficient or incorrect evidence to support the claim. In this paper, we study the most frequent fallacies on Reddit, and we present them using the pragma-dialectical theory of argumentation. We construct a new annotated dataset of fallacies, using user comments containing fallacy mentions as noisy labels, and cleaning the data via crowdsourcing. Finally, we study the task of classifying fallacies using neural models. We find that generally the models perform better in the presence of conversational context. We have released the data and the code at github.com/sahaisaumya/informal_fallacies.", "author": "Saumya Sahai; Oana Balalau; Roxana Horincar", "authorids": "/s/saumya-sahai/; /o/oana-balalau/; /r/roxana-horincar/", "bibtex": "@inproceedings{sahai-etal-2021-breaking,\n title = \"Breaking Down the Invisible Wall of Informal Fallacies in Online Discussions\",\n author = \"Sahai, Saumya and\n Balalau, Oana and\n Horincar, Roxana\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.53/\",\n doi = \"10.18653/v1/2021.acl-long.53\",\n pages = \"644--657\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.53.pdf", "site": "https://aclanthology.org/2021.acl-long.53/", "pdf_size": 309286, "gs_citation": 37, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=16162820059685802967&as_sdt=5,33&sciodt=0,33&hl=en", "gs_version_total": 6, "aff": "The Ohio State University, USA+Inria, Institut Polytechnique de Paris, France; Inria, Institut Polytechnique de Paris, France; Thales Research & Technology, France", "aff_domain": "osu.edu;inria.fr;thalesgroup.com", "email": "osu.edu;inria.fr;thalesgroup.com", "github": "github.com/sahaisaumya/informal_fallacies", "project": "", "author_num": 3, "aff_unique_index": "0+1;1;2", "aff_unique_norm": "The Ohio State University;Inria;Thales Research & Technology", "aff_unique_dep": ";;", "aff_unique_url": "https://www.osu.edu;https://www.inria.fr;https://www.thalesgroup.com/en/research-thales", "aff_unique_abbr": "OSU;Inria;TRT", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0+1;1;1", "aff_country_unique": "United States;France" }, { "id": "2021.acl-long.27", "title": "Bridge-Based Active Domain Adaptation for Aspect Term Extraction", "track": "main", "status": "Long", "award": false, "abstract": "As a fine-grained task, the annotation cost of aspect term extraction is extremely high. Recent attempts alleviate this issue using domain adaptation that transfers common knowledge across domains. Since most aspect terms are domain-specific, they cannot be transferred directly. Existing methods solve this problem by associating aspect terms with pivot words (we call this passive domain adaptation because the transfer of aspect terms relies on the links to pivots). However, all these methods need either manually labeled pivot words or expensive computing resources to build associations. In this paper, we propose a novel active domain adaptation method. Our goal is to transfer aspect terms by actively supplementing transferable knowledge. To this end, we construct syntactic bridges by recognizing syntactic roles as pivots instead of as links to pivots. We also build semantic bridges by retrieving transferable semantic prototypes. Extensive experiments show that our method significantly outperforms previous approaches.", "author": "Zhuang Chen; Tieyun Qian", "authorids": "/z/zhuang-chen/; /t/tieyun-qian/", "bibtex": "@inproceedings{chen-qian-2021-bridge,\n title = \"Bridge-Based Active Domain Adaptation for Aspect Term Extraction\",\n author = \"Chen, Zhuang and\n Qian, Tieyun\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.27/\",\n doi = \"10.18653/v1/2021.acl-long.27\",\n pages = \"317--327\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.27.pdf", "site": "https://aclanthology.org/2021.acl-long.27/", "pdf_size": 757068, "gs_citation": 39, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=1025052016987210770&as_sdt=5,44&sciodt=0,44&hl=en", "gs_version_total": 4, "aff": "School of Computer Science, Wuhan University, China; School of Computer Science, Wuhan University, China", "aff_domain": "whu.edu.cn;whu.edu.cn", "email": "whu.edu.cn;whu.edu.cn", "github": "", "project": "", "author_num": 2, "aff_unique_index": "0;0", "aff_unique_norm": "Wuhan University", "aff_unique_dep": "School of Computer Science", "aff_unique_url": "http://www.whu.edu.cn", "aff_unique_abbr": "WHU", "aff_campus_unique_index": "0;0", "aff_campus_unique": "Wuhan", "aff_country_unique_index": "0;0", "aff_country_unique": "China" }, { "id": "2021.acl-long.468", "title": "Bridging Subword Gaps in Pretrain-Finetune Paradigm for Natural Language Generation", "track": "main", "status": "Long", "award": false, "abstract": "A well-known limitation in pretrain-finetune paradigm lies in its inflexibility caused by the one-size-fits-all vocabulary. This potentially weakens the effect when applying pretrained models into natural language generation (NLG) tasks, especially for the subword distributions between upstream and downstream tasks with significant discrepancy. Towards approaching this problem, we extend the vanilla pretrain-finetune pipeline with an extra embedding transfer step. Specifically, a plug-and-play embedding generator is introduced to produce the representation of any input token, according to pre-trained embeddings of its morphologically similar ones. Thus, embeddings of mismatch tokens in downstream tasks can also be efficiently initialized. We conduct experiments on a variety of NLG tasks under the pretrain-finetune fashion. Experimental results and extensive analyses show that the proposed strategy offers us opportunities to feel free to transfer the vocabulary, leading to more efficient and better performed downstream NLG models.", "author": "Xin Liu; Baosong Yang; Dayiheng Liu; Haibo Zhang; Weihua Luo; Min Zhang; Haiying Zhang; Jinsong Su", "authorids": "/x/xin-liu/; /b/baosong-yang/; /d/dayiheng-liu/; /h/haibo-zhang/; /w/weihua-luo/; /m/min-zhang/; /h/haiying-zhang/; /j/jinsong-su/", "bibtex": "@inproceedings{liu-etal-2021-bridging,\n title = \"Bridging Subword Gaps in Pretrain-Finetune Paradigm for Natural Language Generation\",\n author = \"Liu, Xin and\n Yang, Baosong and\n Liu, Dayiheng and\n Zhang, Haibo and\n Luo, Weihua and\n Zhang, Min and\n Zhang, Haiying and\n Su, Jinsong\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.468/\",\n doi = \"10.18653/v1/2021.acl-long.468\",\n pages = \"6001--6011\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.468.pdf", "site": "https://aclanthology.org/2021.acl-long.468/", "pdf_size": 632522, "gs_citation": 20, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=17262356759949934714&as_sdt=5,33&sciodt=0,33&hl=en", "gs_version_total": 4, "aff": "School of Informatics, Xiamen University+Institute of Artificial Intelligence, Xiamen University; Alibaba Group; Alibaba Group; Alibaba Group; Alibaba Group; Soochow University, China; School of Informatics, Xiamen University+Institute of Artificial Intelligence, Xiamen University; School of Informatics, Xiamen University+Institute of Artificial Intelligence, Xiamen University+Pengcheng Lab, Shenzhen", "aff_domain": "stu.xmu.edu.cn;alibaba-inc.com;alibaba-inc.com;alibaba-inc.com;alibaba-inc.com;suda.edu.cn;xmu.edu.cn;xmu.edu.cn", "email": "stu.xmu.edu.cn;alibaba-inc.com;alibaba-inc.com;alibaba-inc.com;alibaba-inc.com;suda.edu.cn;xmu.edu.cn;xmu.edu.cn", "github": "https://github.com/DeepLearnXMU/embedding-transfer", "project": "", "author_num": 8, "aff_unique_index": "0+0;1;1;1;1;2;0+0;0+0+3", "aff_unique_norm": "Xiamen University;Alibaba Group;Soochow University;Pengcheng Lab", "aff_unique_dep": "School of Informatics;;;", "aff_unique_url": "https://www.xmu.edu.cn;https://www.alibaba.com;https://www.soochow.edu.cn;", "aff_unique_abbr": "XMU;Alibaba;Soochow U;", "aff_campus_unique_index": ";;1", "aff_campus_unique": ";Shenzhen", "aff_country_unique_index": "0+0;0;0;0;0;0;0+0;0+0+0", "aff_country_unique": "China" }, { "id": "2021.acl-short.137", "title": "Bringing Structure into Summaries: a Faceted Summarization Dataset for Long Scientific Documents", "track": "main", "status": "Short", "award": false, "abstract": "Faceted summarization provides briefings of a document from different perspectives. Readers can quickly comprehend the main points of a long document with the help of a structured outline. However, little research has been conducted on this subject, partially due to the lack of large-scale faceted summarization datasets. In this study, we present FacetSum, a faceted summarization benchmark built on Emerald journal articles, covering a diverse range of domains. Different from traditional document-summary pairs, FacetSum provides multiple summaries, each targeted at specific sections of a long document, including the purpose, method, findings, and value. Analyses and empirical results on our dataset reveal the importance of bringing structure into summaries. We believe FacetSum will spur further advances in summarization research and foster the development of NLP systems that can leverage the structured information in both long texts and summaries.", "author": "Rui Meng; Khushboo Thaker; Lei Zhang; Yue Dong; Xingdi Yuan; Tong Wang; Daqing He", "authorids": "/r/rui-meng/; /k/khushboo-thaker/; /l/lei-zhang/; /y/yue-dong/; /x/xingdi-yuan/; /t/tong-wang/; /d/daqing-he/", "bibtex": "@inproceedings{meng-etal-2021-bringing,\n title = \"Bringing Structure into Summaries: a Faceted Summarization Dataset for Long Scientific Documents\",\n author = \"Meng, Rui and\n Thaker, Khushboo and\n Zhang, Lei and\n Dong, Yue and\n Yuan, Xingdi and\n Wang, Tong and\n He, Daqing\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-short.137/\",\n doi = \"10.18653/v1/2021.acl-short.137\",\n pages = \"1080--1089\"\n}", "pdf": "https://aclanthology.org/2021.acl-short.137.pdf", "site": "https://aclanthology.org/2021.acl-short.137/", "pdf_size": 465049, "gs_citation": 54, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=16140302633271857823&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 6, "aff": "School of Computing and Information, University of Pittsburgh; School of Computing and Information, University of Pittsburgh; School of Computing and Information, University of Pittsburgh; Mila / McGill University; Microsoft Research, Montr \u00b4eal; Microsoft Research, Montr \u00b4eal; School of Computing and Information, University of Pittsburgh", "aff_domain": "pitt.edu;pitt.edu;pitt.edu;mail.mcgill.ca;microsoft.com;microsoft.com;pitt.edu", "email": "pitt.edu;pitt.edu;pitt.edu;mail.mcgill.ca;microsoft.com;microsoft.com;pitt.edu", "github": "", "project": "", "author_num": 7, "aff_unique_index": "0;0;0;1;2;2;0", "aff_unique_norm": "University of Pittsburgh;McGill University;Microsoft Research", "aff_unique_dep": "School of Computing and Information;Mila;", "aff_unique_url": "https://www.pitt.edu;https://www.mcgill.ca;https://www.microsoft.com/en-us/research/group/microsoft-research-montreal", "aff_unique_abbr": "Pitt;McGill;MSR", "aff_campus_unique_index": "0;0;0;1;2;2;0", "aff_campus_unique": "Pittsburgh;Montreal;Montr\u00e9al", "aff_country_unique_index": "0;0;0;1;1;1;0", "aff_country_unique": "United States;Canada" }, { "id": "2021.acl-long.507", "title": "CCMatrix: Mining Billions of High-Quality Parallel Sentences on the Web", "track": "main", "status": "Long", "award": false, "abstract": "We show that margin-based bitext mining in a multilingual sentence space can be successfully scaled to operate on monolingual corpora of billions of sentences. We use 32 snapshots of a curated common crawl corpus (Wenzel et al, 2019) totaling 71 billion unique sentences. Using one unified approach for 90 languages, we were able to mine 10.8 billion parallel sentences, out of which only 2.9 billions are aligned with English. We illustrate the capability of our scalable mining system to create high quality training sets from one language to any other by training hundreds of different machine translation models and evaluating them on the many-to-many TED benchmark. Further, we evaluate on competitive translation benchmarks such as WMT and WAT. Using only mined bitext, we set a new state of the art for a single system on the WMT\u201919 test set for English-German/Russian/Chinese. In particular, our English/German and English/Russian systems outperform the best single ones by over 4 BLEU points and are on par with best WMT\u201919 systems, which train on the WMT training data and augment it with backtranslation. We also achieve excellent results for distant languages pairs like Russian/Japanese, outperforming the best submission at the 2020 WAT workshop. All of the mined bitext will be freely available.", "author": "Holger Schwenk; Guillaume Wenzek; Sergey Edunov; Edouard Grave; Armand Joulin; Angela Fan", "authorids": "/h/holger-schwenk/; /g/guillaume-wenzek/; /s/sergey-edunov/; /e/edouard-grave/; /a/armand-joulin/; /a/angela-fan/", "bibtex": "@inproceedings{schwenk-etal-2021-ccmatrix,\n title = \"{CCM}atrix: Mining Billions of High-Quality Parallel Sentences on the Web\",\n author = \"Schwenk, Holger and\n Wenzek, Guillaume and\n Edunov, Sergey and\n Grave, Edouard and\n Joulin, Armand and\n Fan, Angela\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.507/\",\n doi = \"10.18653/v1/2021.acl-long.507\",\n pages = \"6490--6500\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.507.pdf", "site": "https://aclanthology.org/2021.acl-long.507/", "pdf_size": 412608, "gs_citation": 255, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=4045395778324076607&as_sdt=5,33&sciodt=0,33&hl=en", "gs_version_total": 6, "aff": "Facebook AI; Facebook AI; Facebook AI; Facebook AI; Facebook AI; Facebook AI", "aff_domain": "fb.com;fb.com;fb.com;fb.com;fb.com;fb.com", "email": "fb.com;fb.com;fb.com;fb.com;fb.com;fb.com", "github": "", "project": "", "author_num": 6, "aff_unique_index": "0;0;0;0;0;0", "aff_unique_norm": "Facebook", "aff_unique_dep": "Facebook AI", "aff_unique_url": "https://www.facebook.com", "aff_unique_abbr": "Facebook AI", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0;0;0;0;0;0", "aff_country_unique": "United States" }, { "id": "2021.acl-long.288", "title": "CDRNN: Discovering Complex Dynamics in Human Language Processing", "track": "main", "status": "Long", "award": false, "abstract": "The human mind is a dynamical system, yet many analysis techniques used to study it are limited in their ability to capture the complex dynamics that may characterize mental processes. This study proposes the continuous-time deconvolutional regressive neural network (CDRNN), a deep neural extension of continuous-time deconvolutional regression (Shain & Schuler, 2021) that jointly captures time-varying, non-linear, and delayed influences of predictors (e.g. word surprisal) on the response (e.g. reading time). Despite this flexibility, CDRNN is interpretable and able to illuminate patterns in human cognition that are otherwise difficult to study. Behavioral and fMRI experiments reveal detailed and plausible estimates of human language processing dynamics that generalize better than CDR and other baselines, supporting a potential role for CDRNN in studying human language processing.", "author": "Cory Shain", "authorids": "/c/cory-shain/", "bibtex": "@inproceedings{shain-2021-cdrnn,\n title = \"{CDRNN}: Discovering Complex Dynamics in Human Language Processing\",\n author = \"Shain, Cory\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.288/\",\n doi = \"10.18653/v1/2021.acl-long.288\",\n pages = \"3718--3734\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.288.pdf", "site": "https://aclanthology.org/2021.acl-long.288/", "pdf_size": 2537504, "gs_citation": 16, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=17499973109113950660&as_sdt=5,33&sciodt=0,33&hl=en", "gs_version_total": 6, "aff": "The Ohio State University", "aff_domain": "osu.edu", "email": "osu.edu", "github": "", "project": "", "author_num": 1, "aff_unique_index": "0", "aff_unique_norm": "The Ohio State University", "aff_unique_dep": "", "aff_unique_url": "https://www.osu.edu", "aff_unique_abbr": "OSU", "aff_country_unique_index": "0", "aff_country_unique": "United States" }, { "id": "2021.acl-long.483", "title": "CIL: Contrastive Instance Learning Framework for Distantly Supervised Relation Extraction", "track": "main", "status": "Long", "award": false, "abstract": "The journey of reducing noise from distant supervision (DS) generated training data has been started since the DS was first introduced into the relation extraction (RE) task. For the past decade, researchers apply the multi-instance learning (MIL) framework to find the most reliable feature from a bag of sentences. Although the pattern of MIL bags can greatly reduce DS noise, it fails to represent many other useful sentence features in the datasets. In many cases, these sentence features can only be acquired by extra sentence-level human annotation with heavy costs. Therefore, the performance of distantly supervised RE models is bounded. In this paper, we go beyond typical MIL framework and propose a novel contrastive instance learning (CIL) framework. Specifically, we regard the initial MIL as the relational triple encoder and constraint positive pairs against negative pairs for each instance. Experiments demonstrate the effectiveness of our proposed framework, with significant improvements over the previous methods on NYT10, GDS and KBP.", "author": "Tao Chen; Haizhou Shi; Siliang Tang; Zhigang Chen; Fei Wu; Yueting Zhuang", "authorids": "/t/tao-chen/; /h/haizhou-shi/; /s/siliang-tang/; /z/zhigang-chen/; /f/fei-wu/; /y/yueting-zhuang/", "bibtex": "@inproceedings{chen-etal-2021-cil,\n title = \"{CIL}: Contrastive Instance Learning Framework for Distantly Supervised Relation Extraction\",\n author = \"Chen, Tao and\n Shi, Haizhou and\n Tang, Siliang and\n Chen, Zhigang and\n Wu, Fei and\n Zhuang, Yueting\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.483/\",\n doi = \"10.18653/v1/2021.acl-long.483\",\n pages = \"6191--6200\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.483.pdf", "site": "https://aclanthology.org/2021.acl-long.483/", "pdf_size": 765961, "gs_citation": 70, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=13716108969984439715&as_sdt=5,33&sciodt=0,33&hl=en", "gs_version_total": 5, "aff": "Zhejiang University; Zhejiang University; Zhejiang University + Alibaba-Zhejiang University Joint Research Institute of Frontier Technologies; State Key Laboratory of Cognitive Intelligence, Hefei, China; Zhejiang University; Zhejiang University", "aff_domain": "zju.edu.cn;zju.edu.cn;zju.edu.cn;iflytek.com;cs.zju.edu.cn;zju.edu.cn", "email": "zju.edu.cn;zju.edu.cn;zju.edu.cn;iflytek.com;cs.zju.edu.cn;zju.edu.cn", "github": "", "project": "", "author_num": 6, "aff_unique_index": "0;0;0+0;1;0;0", "aff_unique_norm": "Zhejiang University;State Key Laboratory of Cognitive Intelligence", "aff_unique_dep": ";", "aff_unique_url": "https://www.zju.edu.cn;", "aff_unique_abbr": "ZJU;", "aff_campus_unique_index": ";1", "aff_campus_unique": ";Hefei", "aff_country_unique_index": "0;0;0+0;0;0;0", "aff_country_unique": "China" }, { "id": "2021.acl-long.491", "title": "CLEVE: Contrastive Pre-training for Event Extraction", "track": "main", "status": "Long", "award": false, "abstract": "Event extraction (EE) has considerably benefited from pre-trained language models (PLMs) by fine-tuning. However, existing pre-training methods have not involved modeling event characteristics, resulting in the developed EE models cannot take full advantage of large-scale unsupervised data. To this end, we propose CLEVE, a contrastive pre-training framework for EE to better learn event knowledge from large unsupervised data and their semantic structures (e.g. AMR) obtained with automatic parsers. CLEVE contains a text encoder to learn event semantics and a graph encoder to learn event structures respectively. Specifically, the text encoder learns event semantic representations by self-supervised contrastive learning to represent the words of the same events closer than those unrelated words; the graph encoder learns event structure representations by graph contrastive pre-training on parsed event-related semantic structures. The two complementary representations then work together to improve both the conventional supervised EE and the unsupervised \u201cliberal\u201d EE, which requires jointly extracting events and discovering event schemata without any annotated data. Experiments on ACE 2005 and MAVEN datasets show that CLEVE achieves significant improvements, especially in the challenging unsupervised setting. The source code and pre-trained checkpoints can be obtained from https://github.com/THU-KEG/CLEVE.", "author": "Ziqi Wang; Xiaozhi Wang; Xu Han; Yankai Lin; Lei Hou; Zhiyuan Liu; Peng Li; Juanzi Li; Jie Zhou", "authorids": "/z/ziqi-wang/; /x/xiaozhi-wang/; /x/xu-han/; /y/yankai-lin/; /l/lei-hou/; /z/zhiyuan-liu/; /p/peng-li/; /j/juanzi-li/; /j/jie-zhou/", "bibtex": "@inproceedings{wang-etal-2021-cleve,\n title = \"{CLEVE}: {C}ontrastive {P}re-training for {E}vent {E}xtraction\",\n author = \"Wang, Ziqi and\n Wang, Xiaozhi and\n Han, Xu and\n Lin, Yankai and\n Hou, Lei and\n Liu, Zhiyuan and\n Li, Peng and\n Li, Juanzi and\n Zhou, Jie\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.491/\",\n doi = \"10.18653/v1/2021.acl-long.491\",\n pages = \"6283--6297\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.491.pdf", "site": "https://aclanthology.org/2021.acl-long.491/", "pdf_size": 685620, "gs_citation": 123, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=6454434943388212892&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 6, "aff": "Department of Computer Science and Technology, BNRist, Tsinghua University; Department of Computer Science and Technology, BNRist, Tsinghua University; Department of Computer Science and Technology, BNRist, Tsinghua University; Pattern Recognition Center, WeChat AI, Tencent Inc; Department of Computer Science and Technology, BNRist+KIRC, Institute for Artificial Intelligence, Tsinghua University; Department of Computer Science and Technology, BNRist+KIRC, Institute for Artificial Intelligence, Tsinghua University; Pattern Recognition Center, WeChat AI, Tencent Inc; Department of Computer Science and Technology, BNRist+KIRC, Institute for Artificial Intelligence, Tsinghua University; Pattern Recognition Center, WeChat AI, Tencent Inc", "aff_domain": "mails.tsinghua.edu.cn;mails.tsinghua.edu.cn;mails.tsinghua.edu.cn; ;tsinghua.edu.cn; ; ; ; ", "email": "mails.tsinghua.edu.cn;mails.tsinghua.edu.cn;mails.tsinghua.edu.cn; ;tsinghua.edu.cn; ; ; ; ", "github": "https://github.com/THU-KEG/CLEVE", "project": "", "author_num": 9, "aff_unique_index": "0;0;0;1;2+0;2+0;1;2+0;1", "aff_unique_norm": "Tsinghua University;Tencent Inc;BNRist", "aff_unique_dep": "Department of Computer Science and Technology;Pattern Recognition Center, WeChat AI;Department of Computer Science and Technology", "aff_unique_url": "https://www.tsinghua.edu.cn;https://www.tencent.com;", "aff_unique_abbr": "THU;Tencent;", "aff_campus_unique_index": ";;", "aff_campus_unique": "", "aff_country_unique_index": "0;0;0;0;0;0;0;0;0", "aff_country_unique": "China;" }, { "id": "2021.acl-long.181", "title": "CLINE: Contrastive Learning with Semantic Negative Examples for Natural Language Understanding", "track": "main", "status": "Long", "award": false, "abstract": "Despite pre-trained language models have proven useful for learning high-quality semantic representations, these models are still vulnerable to simple perturbations. Recent works aimed to improve the robustness of pre-trained models mainly focus on adversarial training from perturbed examples with similar semantics, neglecting the utilization of different or even opposite semantics. Different from the image processing field, the text is discrete and few word substitutions can cause significant semantic changes. To study the impact of semantics caused by small perturbations, we conduct a series of pilot experiments and surprisingly find that adversarial training is useless or even harmful for the model to detect these semantic changes. To address this problem, we propose Contrastive Learning with semantIc Negative Examples (CLINE), which constructs semantic negative examples unsupervised to improve the robustness under semantically adversarial attacking. By comparing with similar and opposite semantic examples, the model can effectively perceive the semantic changes caused by small perturbations. Empirical results show that our approach yields substantial improvements on a range of sentiment analysis, reasoning, and reading comprehension tasks. And CLINE also ensures the compactness within the same semantics and separability across different semantics in sentence-level.", "author": "Dong Wang; Ning Ding; Piji Li; Haitao Zheng", "authorids": "/d/dong-wang/; /n/ning-ding/; /p/piji-li/; /h/haitao-zheng/", "bibtex": "@inproceedings{wang-etal-2021-cline,\n title = \"{CLINE}: Contrastive Learning with Semantic Negative Examples for Natural Language Understanding\",\n author = \"Wang, Dong and\n Ding, Ning and\n Li, Piji and\n Zheng, Haitao\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.181/\",\n doi = \"10.18653/v1/2021.acl-long.181\",\n pages = \"2332--2342\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.181.pdf", "site": "https://aclanthology.org/2021.acl-long.181/", "pdf_size": 1390757, "gs_citation": 130, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=17947317579768165031&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 4, "aff": "Department of Computer Science and Technology, Tsinghua University + Tsinghua ShenZhen International Graduate School, Tsinghua University; Department of Computer Science and Technology, Tsinghua University + Tsinghua ShenZhen International Graduate School, Tsinghua University; Tencent AI Lab; Department of Computer Science and Technology, Tsinghua University + Tsinghua ShenZhen International Graduate School, Tsinghua University", "aff_domain": "mails.tsinghua.edu.cn;mails.tsinghua.edu.cn;tencent.com;sz.tsinghua.edu.cn", "email": "mails.tsinghua.edu.cn;mails.tsinghua.edu.cn;tencent.com;sz.tsinghua.edu.cn", "github": "", "project": "", "author_num": 4, "aff_unique_index": "0+0;0+0;1;0+0", "aff_unique_norm": "Tsinghua University;Tencent", "aff_unique_dep": "Department of Computer Science and Technology;Tencent AI Lab", "aff_unique_url": "https://www.tsinghua.edu.cn;https://ai.tencent.com", "aff_unique_abbr": "THU;Tencent AI Lab", "aff_campus_unique_index": "1;1;1", "aff_campus_unique": ";Shenzhen", "aff_country_unique_index": "0+0;0+0;0;0+0", "aff_country_unique": "China" }, { "id": "2021.acl-long.109", "title": "CLIP: A Dataset for Extracting Action Items for Physicians from Hospital Discharge Notes", "track": "main", "status": "Long", "award": false, "abstract": "Continuity of care is crucial to ensuring positive health outcomes for patients discharged from an inpatient hospital setting, and improved information sharing can help. To share information, caregivers write discharge notes containing action items to share with patients and their future caregivers, but these action items are easily lost due to the lengthiness of the documents. In this work, we describe our creation of a dataset of clinical action items annotated over MIMIC-III, the largest publicly available dataset of real clinical notes. This dataset, which we call CLIP, is annotated by physicians and covers 718 documents representing 100K sentences. We describe the task of extracting the action items from these documents as multi-aspect extractive summarization, with each aspect representing a type of action to be taken. We evaluate several machine learning models on this task, and show that the best models exploit in-domain language model pre-training on 59K unannotated documents, and incorporate context from neighboring sentences. We also propose an approach to pre-training data selection that allows us to explore the trade-off between size and domain-specificity of pre-training datasets for this task.", "author": "James Mullenbach; Yada Pruksachatkun; Sean Adler; Jennifer Seale; Jordan Swartz; Greg McKelvey; Hui Dai; Yi Yang; David Sontag", "authorids": "/j/james-mullenbach/; /y/yada-pruksachatkun/; /s/sean-adler/; /j/jennifer-seale/; /j/jordan-swartz/; /g/greg-mckelvey/; /h/hui-dai/; /y/yi-yang/; /d/david-sontag/", "bibtex": "@inproceedings{mullenbach-etal-2021-clip,\n title = \"{CLIP}: A Dataset for Extracting Action Items for Physicians from Hospital Discharge Notes\",\n author = \"Mullenbach, James and\n Pruksachatkun, Yada and\n Adler, Sean and\n Seale, Jennifer and\n Swartz, Jordan and\n McKelvey, Greg and\n Dai, Hui and\n Yang, Yi and\n Sontag, David\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.109/\",\n doi = \"10.18653/v1/2021.acl-long.109\",\n pages = \"1365--1378\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.109.pdf", "site": "https://aclanthology.org/2021.acl-long.109/", "pdf_size": 456004, "gs_citation": 22, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=11955036253317898895&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 5, "aff": "ASAPP; Amazon Alexa AI; CUNY Graduate Center; United States Digital Service; ASAPP+MIT; ASAPP; ASAPP; ASAPP; ASAPP+MIT", "aff_domain": "asapp.com;amazon.com;asapp.com;cuny.edu;usds.gov;mckelvey.com;asapp.com;asapp.com;mit.edu", "email": "asapp.com;amazon.com;asapp.com;cuny.edu;usds.gov;mckelvey.com;asapp.com;asapp.com;mit.edu", "github": "", "project": "", "author_num": 9, "aff_unique_index": "0;1;2;3;0+4;0;0;0;0+4", "aff_unique_norm": "ASAPP;Amazon;City University of New York;United States Digital Service;Massachusetts Institute of Technology", "aff_unique_dep": ";Alexa AI;;;", "aff_unique_url": "https://www.asapp.com;https://www.amazon.com;https://www.gc.cuny.edu;https://www.usds.gov;https://web.mit.edu", "aff_unique_abbr": "ASAPP;Amazon;CUNY GC;USDS;MIT", "aff_campus_unique_index": "1;;", "aff_campus_unique": ";Graduate Center", "aff_country_unique_index": "0;0;0;0;0+0;0;0;0;0+0", "aff_country_unique": "United States" }, { "id": "2021.acl-long.395", "title": "COINS: Dynamically Generating COntextualized Inference Rules for Narrative Story Completion", "track": "main", "status": "Long", "award": false, "abstract": "Despite recent successes of large pre-trained language models in solving reasoning tasks, their inference capabilities remain opaque. We posit that such models can be made more interpretable by explicitly generating interim inference rules, and using them to guide the generation of task-specific textual outputs. In this paper we present Coins, a recursive inference framework that i) iteratively reads context sentences, ii) dynamically generates contextualized inference rules, encodes them, and iii) uses them to guide task-specific output generation. We apply to a Narrative Story Completion task that asks a model to complete a story with missing sentences, to produce a coherent story with plausible logical connections, causal relationships, and temporal dependencies. By modularizing inference and sentence generation steps in a recurrent model, we aim to make reasoning steps and their effects on next sentence generation transparent. Our automatic and manual evaluations show that the model generates better story sentences than SOTA baselines, especially in terms of coherence. We further demonstrate improved performance over strong pre-trained LMs in generating commonsense inference rules. The recursive nature of holds the potential for controlled generation of longer sequences.", "author": "Debjit Paul; Anette Frank", "authorids": "/d/debjit-paul/; /a/anette-frank/", "bibtex": "@inproceedings{paul-frank-2021-coins,\n title = \"{COINS}: Dynamically Generating {CO}ntextualized Inference Rules for Narrative Story Completion\",\n author = \"Paul, Debjit and\n Frank, Anette\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.395/\",\n doi = \"10.18653/v1/2021.acl-long.395\",\n pages = \"5086--5099\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.395.pdf", "site": "https://aclanthology.org/2021.acl-long.395/", "pdf_size": 992868, "gs_citation": 15, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=3178150200258944292&as_sdt=5,44&sciodt=0,44&hl=en", "gs_version_total": 5, "aff": "Research Training Group AIPHES, Institute for Computational Linguistics, Heidelberg University; Research Training Group AIPHES, Institute for Computational Linguistics, Heidelberg University", "aff_domain": "cl.uni-heidelberg.de;cl.uni-heidelberg.de", "email": "cl.uni-heidelberg.de;cl.uni-heidelberg.de", "github": "", "project": "", "author_num": 2, "aff_unique_index": "0;0", "aff_unique_norm": "Heidelberg University", "aff_unique_dep": "Institute for Computational Linguistics", "aff_unique_url": "https://www.uni-heidelberg.de", "aff_unique_abbr": "Uni Heidelberg", "aff_campus_unique_index": "0;0", "aff_campus_unique": "Heidelberg", "aff_country_unique_index": "0;0", "aff_country_unique": "Germany" }, { "id": "2021.acl-long.48", "title": "COSY: COunterfactual SYntax for Cross-Lingual Understanding", "track": "main", "status": "Long", "award": false, "abstract": "Pre-trained multilingual language models, e.g., multilingual-BERT, are widely used in cross-lingual tasks, yielding the state-of-the-art performance. However, such models suffer from a large performance gap between source and target languages, especially in the zero-shot setting, where the models are fine-tuned only on English but tested on other languages for the same task. We tackle this issue by incorporating language-agnostic information, specifically, universal syntax such as dependency relations and POS tags, into language models, based on the observation that universal syntax is transferable across different languages. Our approach, called COunterfactual SYntax (COSY), includes the design of SYntax-aware networks as well as a COunterfactual training method to implicitly force the networks to learn not only the semantics but also the syntax. To evaluate COSY, we conduct cross-lingual experiments on natural language inference and question answering using mBERT and XLM-R as network backbones. Our results show that COSY achieves the state-of-the-art performance for both tasks, without using auxiliary training data.", "author": "Sicheng Yu; Hao Zhang; Yulei Niu; Qianru Sun; Jing Jiang", "authorids": "/s/sicheng-yu/; /h/hao-zhang/; /y/yulei-niu/; /q/qianru-sun/; /j/jing-jiang/", "bibtex": "@inproceedings{yu-etal-2021-cosy,\n title = \"{COSY}: {CO}unterfactual {SY}ntax for Cross-Lingual Understanding\",\n author = \"Yu, Sicheng and\n Zhang, Hao and\n Niu, Yulei and\n Sun, Qianru and\n Jiang, Jing\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.48/\",\n doi = \"10.18653/v1/2021.acl-long.48\",\n pages = \"577--589\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.48.pdf", "site": "https://aclanthology.org/2021.acl-long.48/", "pdf_size": 461589, "gs_citation": 8, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=1425849978377101749&as_sdt=5,47&sciodt=0,47&hl=en", "gs_version_total": 5, "aff": "Singapore Management University; Nanyang Technological University + Agency for Science, Technology and Research; Nanyang Technological University; Singapore Management University; Singapore Management University", "aff_domain": "phdcs.smu.edu.sg;e.ntu.edu.sg;gmail.com;smu.edu.sg;smu.edu.sg", "email": "phdcs.smu.edu.sg;e.ntu.edu.sg;gmail.com;smu.edu.sg;smu.edu.sg", "github": "https://github.com/PluviophileYU/COSY", "project": "", "author_num": 5, "aff_unique_index": "0;1+2;1;0;0", "aff_unique_norm": "Singapore Management University;Nanyang Technological University;Agency for Science, Technology and Research", "aff_unique_dep": ";;", "aff_unique_url": "https://www.smu.edu.sg;https://www.ntu.edu.sg;https://www.a-star.edu.sg", "aff_unique_abbr": "SMU;NTU;A*STAR", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0;0+0;0;0;0", "aff_country_unique": "Singapore" }, { "id": "2021.acl-long.165", "title": "COVID-Fact: Fact Extraction and Verification of Real-World Claims on COVID-19 Pandemic", "track": "main", "status": "Long", "award": false, "abstract": "We introduce a FEVER-like dataset COVID-Fact of 4,086 claims concerning the COVID-19 pandemic. The dataset contains claims, evidence for the claims, and contradictory claims refuted by the evidence. Unlike previous approaches, we automatically detect true claims and their source articles and then generate counter-claims using automatic methods rather than employing human annotators. Along with our constructed resource, we formally present the task of identifying relevant evidence for the claims and verifying whether the evidence refutes or supports a given claim. In addition to scientific claims, our data contains simplified general claims from media sources, making it better suited for detecting general misinformation regarding COVID-19. Our experiments indicate that COVID-Fact will provide a challenging testbed for the development of new systems and our approach will reduce the costs of building domain-specific datasets for detecting misinformation.", "author": "Arkadiy Saakyan; Tuhin Chakrabarty; Smaranda Muresan", "authorids": "/a/arkadiy-saakyan/; /t/tuhin-chakrabarty/; /s/smaranda-muresan/", "bibtex": "@inproceedings{saakyan-etal-2021-covid,\n title = \"{COVID}-Fact: Fact Extraction and Verification of Real-World Claims on {COVID}-19 Pandemic\",\n author = \"Saakyan, Arkadiy and\n Chakrabarty, Tuhin and\n Muresan, Smaranda\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.165/\",\n doi = \"10.18653/v1/2021.acl-long.165\",\n pages = \"2116--2129\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.165.pdf", "site": "https://aclanthology.org/2021.acl-long.165/", "pdf_size": 2256289, "gs_citation": 133, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=3002377595036863325&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 6, "aff": "Department of Computer Science, Columbia University + Data Science Institute, Columbia University; Department of Computer Science, Columbia University + Data Science Institute, Columbia University; Department of Computer Science, Columbia University + Data Science Institute, Columbia University", "aff_domain": "columbia.edu;cs.columbia.edu;cs.columbia.edu", "email": "columbia.edu;cs.columbia.edu;cs.columbia.edu", "github": "https://github.com/asaakyan/covidfact", "project": "", "author_num": 3, "aff_unique_index": "0+0;0+0;0+0", "aff_unique_norm": "Columbia University", "aff_unique_dep": "Department of Computer Science", "aff_unique_url": "https://www.columbia.edu", "aff_unique_abbr": "Columbia", "aff_campus_unique_index": ";;", "aff_campus_unique": "", "aff_country_unique_index": "0+0;0+0;0+0", "aff_country_unique": "United States" }, { "id": "2021.acl-long.412", "title": "CTFN: Hierarchical Learning for Multimodal Sentiment Analysis Using Coupled-Translation Fusion Network", "track": "main", "status": "Long", "award": false, "abstract": "Multimodal sentiment analysis is the challenging research area that attends to the fusion of multiple heterogeneous modalities. The main challenge is the occurrence of some missing modalities during the multimodal fusion procedure. However, the existing techniques require all modalities as input, thus are sensitive to missing modalities at predicting time. In this work, the coupled-translation fusion network (CTFN) is firstly proposed to model bi-direction interplay via couple learning, ensuring the robustness in respect to missing modalities. Specifically, the cyclic consistency constraint is presented to improve the translation performance, allowing us directly to discard decoder and only embraces encoder of Transformer. This could contribute to a much lighter model. Due to the couple learning, CTFN is able to conduct bi-direction cross-modality intercorrelation parallelly. Based on CTFN, a hierarchical architecture is further established to exploit multiple bi-direction translations, leading to double multimodal fusing embeddings compared with traditional translation methods. Moreover, the convolution block is utilized to further highlight explicit interactions among those translations. For evaluation, CTFN was verified on two multimodal benchmarks with extensive ablation studies. The experiments demonstrate that the proposed framework achieves state-of-the-art or often competitive performance. Additionally, CTFN still maintains robustness when considering missing modality.", "author": "Jiajia Tang; Kang Li; Xuanyu Jin; Andrzej Cichocki; Qibin Zhao; Wanzeng Kong", "authorids": "/j/jiajia-tang/; /k/kang-li/; /x/xuanyu-jin/; /a/andrzej-cichocki/; /q/qibin-zhao/; /w/wanzeng-kong/", "bibtex": "@inproceedings{tang-etal-2021-ctfn,\n title = \"{CTFN}: Hierarchical Learning for Multimodal Sentiment Analysis Using Coupled-Translation Fusion Network\",\n author = \"Tang, Jiajia and\n Li, Kang and\n Jin, Xuanyu and\n Cichocki, Andrzej and\n Zhao, Qibin and\n Kong, Wanzeng\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.412/\",\n doi = \"10.18653/v1/2021.acl-long.412\",\n pages = \"5301--5311\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.412.pdf", "site": "https://aclanthology.org/2021.acl-long.412/", "pdf_size": 501877, "gs_citation": 75, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=12398638464551885058&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 4, "aff": "Key Laboratory of Brain Machine Collaborative Intelligence of Zhejiang Province, School of Computer Science and Technology, Hangzhou Dianzi University, China; Key Laboratory of Brain Machine Collaborative Intelligence of Zhejiang Province, School of Computer Science and Technology, Hangzhou Dianzi University, China; Key Laboratory of Brain Machine Collaborative Intelligence of Zhejiang Province, School of Computer Science and Technology, Hangzhou Dianzi University, China; Skolkovo Institute of Science and Technology, Moscow, Russia + Systems Research Institute, Polish Academy of Science, Warsaw, Poland; Center for Advanced Intelligence Project, RIKEN; Key Laboratory of Brain Machine Collaborative Intelligence of Zhejiang Province, School of Computer Science and Technology, Hangzhou Dianzi University, China", "aff_domain": "163.com;hdu.edu.cn;163.com;riken.jp;riken.jp;hdu.edu.cn", "email": "163.com;hdu.edu.cn;163.com;riken.jp;riken.jp;hdu.edu.cn", "github": "", "project": "", "author_num": 6, "aff_unique_index": "0;0;0;1+2;3;0", "aff_unique_norm": "Hangzhou Dianzi University;Skolkovo Institute of Science and Technology;Polish Academy of Sciences;RIKEN", "aff_unique_dep": "School of Computer Science and Technology;;Systems Research Institute;Center for Advanced Intelligence Project", "aff_unique_url": "http://www.hdu.edu.cn/;https://www.skoltech.ru;https://www.pan.pl;https://www.riken.jp", "aff_unique_abbr": "HDU;Skoltech;PAS;RIKEN", "aff_campus_unique_index": "1+2", "aff_campus_unique": ";Moscow;Warsaw", "aff_country_unique_index": "0;0;0;1+2;3;0", "aff_country_unique": "China;Russia;Poland;Japan" }, { "id": "2021.acl-long.251", "title": "Can Generative Pre-trained Language Models Serve As Knowledge Bases for Closed-book QA?", "track": "main", "status": "Long", "award": false, "abstract": "Recent work has investigated the interesting question using pre-trained language models (PLMs) as knowledge bases for answering open questions. However, existing work is limited in using small benchmarks with high test-train overlaps. We construct a new dataset of closed-book QA using SQuAD, and investigate the performance of BART. Experiments show that it is challenging for BART to remember training facts in high precision, and also challenging to answer closed-book questions even if relevant knowledge is retained. Some promising directions are found, including decoupling the knowledge memorizing process and the QA finetune process, forcing the model to recall relevant knowledge when question answering.", "author": "Cunxiang Wang; Pai Liu; Yue Zhang", "authorids": "/c/cunxiang-wang/; /p/pai-liu/; /y/yue-zhang/", "bibtex": "@inproceedings{wang-etal-2021-generative,\n title = \"Can Generative Pre-trained Language Models Serve As Knowledge Bases for Closed-book {QA}?\",\n author = \"Wang, Cunxiang and\n Liu, Pai and\n Zhang, Yue\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.251/\",\n doi = \"10.18653/v1/2021.acl-long.251\",\n pages = \"3241--3251\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.251.pdf", "site": "https://aclanthology.org/2021.acl-long.251/", "pdf_size": 607865, "gs_citation": 85, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=14758405442604155300&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 7, "aff": "Zhejiang University, China\u2660; School of Engineering, Westlake University, China\u2663; Institute of Advanced Technology, Westlake Institute for Advanced Study, China\u2665", "aff_domain": "westlake.edu.cn;westlake.edu.cn;westlake.edu.cn", "email": "westlake.edu.cn;westlake.edu.cn;westlake.edu.cn", "github": "", "project": "", "author_num": 3, "aff_unique_index": "0;1;2", "aff_unique_norm": "Zhejiang University;Westlake University;Westlake Institute for Advanced Study", "aff_unique_dep": ";School of Engineering;Institute of Advanced Technology", "aff_unique_url": "http://www.zju.edu.cn;https://www.westlake.edu.cn;http://www.wias.org.cn/", "aff_unique_abbr": "ZJU;;WIAS", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0;0;0", "aff_country_unique": "China" }, { "id": "2021.acl-long.561", "title": "Can Sequence-to-Sequence Models Crack Substitution Ciphers?", "track": "main", "status": "Long", "award": false, "abstract": "Decipherment of historical ciphers is a challenging problem. The language of the target plaintext might be unknown, and ciphertext can have a lot of noise. State-of-the-art decipherment methods use beam search and a neural language model to score candidate plaintext hypotheses for a given cipher, assuming the plaintext language is known. We propose an end-to-end multilingual model for solving simple substitution ciphers. We test our model on synthetic and real historical ciphers and show that our proposed method can decipher text without explicit language identification while still being robust to noise.", "author": "Nada Aldarrab; Jonathan May", "authorids": "/n/nada-aldarrab/; /j/jonathan-may/", "bibtex": "@inproceedings{aldarrab-may-2021-sequence,\n title = \"Can Sequence-to-Sequence Models Crack Substitution Ciphers?\",\n author = \"Aldarrab, Nada and\n May, Jonathan\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.561/\",\n doi = \"10.18653/v1/2021.acl-long.561\",\n pages = \"7226--7235\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.561.pdf", "site": "https://aclanthology.org/2021.acl-long.561/", "pdf_size": 1726526, "gs_citation": 11, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=838225263892132926&as_sdt=5,33&sciodt=0,33&hl=en", "gs_version_total": 4, "aff": "University of Southern California; University of Southern California", "aff_domain": "isi.edu;isi.edu", "email": "isi.edu;isi.edu", "github": "", "project": "https://cl.lingfil.uu.se/~bea/borg/", "author_num": 2, "aff_unique_index": "0;0", "aff_unique_norm": "University of Southern California", "aff_unique_dep": "", "aff_unique_url": "https://www.usc.edu", "aff_unique_abbr": "USC", "aff_campus_unique_index": "0;0", "aff_campus_unique": "Los Angeles", "aff_country_unique_index": "0;0", "aff_country_unique": "United States" }, { "id": "2021.acl-short.134", "title": "Can Transformer Models Measure Coherence In Text: Re-Thinking the Shuffle Test", "track": "main", "status": "Short", "award": false, "abstract": "The Shuffle Test is the most common task to evaluate whether NLP models can measure coherence in text. Most recent work uses direct supervision on the task; we show that by simply finetuning a RoBERTa model, we can achieve a near perfect accuracy of 97.8%, a state-of-the-art. We argue that this outstanding performance is unlikely to lead to a good model of text coherence, and suggest that the Shuffle Test should be approached in a Zero-Shot setting: models should be evaluated without being trained on the task itself. We evaluate common models in this setting, such as Generative and Bi-directional Transformers, and find that larger architectures achieve high-performance out-of-the-box. Finally, we suggest the k-Block Shuffle Test, a modification of the original by increasing the size of blocks shuffled. Even though human reader performance remains high (around 95% accuracy), model performance drops from 94% to 78% as block size increases, creating a conceptually simple challenge to benchmark NLP models.", "author": "Philippe Laban; Luke Dai; Lucas Bandarkar; Marti A. Hearst", "authorids": "/p/philippe-laban/; /l/luke-dai/; /l/lucas-bandarkar/; /m/marti-a-hearst/", "bibtex": "@inproceedings{laban-etal-2021-transformer,\n title = \"Can Transformer Models Measure Coherence In Text: Re-Thinking the Shuffle Test\",\n author = \"Laban, Philippe and\n Dai, Luke and\n Bandarkar, Lucas and\n Hearst, Marti A.\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-short.134/\",\n doi = \"10.18653/v1/2021.acl-short.134\",\n pages = \"1058--1064\"\n}", "pdf": "https://aclanthology.org/2021.acl-short.134.pdf", "site": "https://aclanthology.org/2021.acl-short.134/", "pdf_size": 282955, "gs_citation": 38, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=10250497807903820723&as_sdt=5,44&sciodt=0,44&hl=en", "gs_version_total": 5, "aff": "UC Berkeley; UC Berkeley; UC Berkeley; UC Berkeley", "aff_domain": "berkeley.edu;berkeley.edu;berkeley.edu;berkeley.edu", "email": "berkeley.edu;berkeley.edu;berkeley.edu;berkeley.edu", "github": "", "project": "", "author_num": 4, "aff_unique_index": "0;0;0;0", "aff_unique_norm": "University of California, Berkeley", "aff_unique_dep": "", "aff_unique_url": "https://www.berkeley.edu", "aff_unique_abbr": "UC Berkeley", "aff_campus_unique_index": "0;0;0;0", "aff_campus_unique": "Berkeley", "aff_country_unique_index": "0;0;0;0", "aff_country_unique": "United States" }, { "id": "2021.acl-long.96", "title": "Can vectors read minds better than experts? Comparing data augmentation strategies for the automated scoring of children\u2019s mindreading ability", "track": "main", "status": "Long", "award": false, "abstract": "In this paper we implement and compare 7 different data augmentation strategies for the task of automatic scoring of children\u2019s ability to understand others\u2019 thoughts, feelings, and desires (or \u201cmindreading\u201d). We recruit in-domain experts to re-annotate augmented samples and determine to what extent each strategy preserves the original rating. We also carry out multiple experiments to measure how much each augmentation strategy improves the performance of automatic scoring systems. To determine the capabilities of automatic systems to generalize to unseen data, we create UK-MIND-20 - a new corpus of children\u2019s performance on tests of mindreading, consisting of 10,320 question-answer pairs. We obtain a new state-of-the-art performance on the MIND-CA corpus, improving macro-F1-score by 6 points. Results indicate that both the number of training examples and the quality of the augmentation strategies affect the performance of the systems. The task-specific augmentations generally outperform task-agnostic augmentations. Automatic augmentations based on vectors (GloVe, FastText) perform the worst. We find that systems trained on MIND-CA generalize well to UK-MIND-20. We demonstrate that data augmentation strategies also improve the performance on unseen data.", "author": "Venelin Kovatchev; Phillip Smith; Mark Lee; Rory Devine", "authorids": "/v/venelin-kovatchev/; /p/phillip-smith/; /m/mark-lee/; /r/rory-devine/", "bibtex": "@inproceedings{kovatchev-etal-2021-vectors,\n title = \"Can vectors read minds better than experts? Comparing data augmentation strategies for the automated scoring of children`s mindreading ability\",\n author = \"Kovatchev, Venelin and\n Smith, Phillip and\n Lee, Mark and\n Devine, Rory\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.96/\",\n doi = \"10.18653/v1/2021.acl-long.96\",\n pages = \"1196--1206\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.96.pdf", "site": "https://aclanthology.org/2021.acl-long.96/", "pdf_size": 223277, "gs_citation": 10, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=1718635470898048533&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 11, "aff": "School of Psychology, University of Birmingham; School of Computer Science, University of Birmingham; School of Computer Science, University of Birmingham; School of Psychology, University of Birmingham", "aff_domain": "bham.ac.uk;bham.ac.uk;bham.ac.uk;bham.ac.uk", "email": "bham.ac.uk;bham.ac.uk;bham.ac.uk;bham.ac.uk", "github": "", "project": "", "author_num": 4, "aff_unique_index": "0;0;0;0", "aff_unique_norm": "University of Birmingham", "aff_unique_dep": "School of Psychology", "aff_unique_url": "https://www.birmingham.ac.uk", "aff_unique_abbr": "UoB", "aff_campus_unique_index": "0;0;0;0", "aff_campus_unique": "Birmingham", "aff_country_unique_index": "0;0;0;0", "aff_country_unique": "United Kingdom" }, { "id": "2021.acl-long.18", "title": "Capturing Event Argument Interaction via A Bi-Directional Entity-Level Recurrent Decoder", "track": "main", "status": "Long", "award": false, "abstract": "Capturing interactions among event arguments is an essential step towards robust event argument extraction (EAE). However, existing efforts in this direction suffer from two limitations: 1) The argument role type information of contextual entities is mainly utilized as training signals, ignoring the potential merits of directly adopting it as semantically rich input features; 2) The argument-level sequential semantics, which implies the overall distribution pattern of argument roles over an event mention, is not well characterized. To tackle the above two bottlenecks, we formalize EAE as a Seq2Seq-like learning problem for the first time, where a sentence with a specific event trigger is mapped to a sequence of event argument roles. A neural architecture with a novel Bi-directional Entity-level Recurrent Decoder (BERD) is proposed to generate argument roles by incorporating contextual entities\u2019 argument role predictions, like a word-by-word text generation process, thereby distinguishing implicit argument distribution patterns within an event more accurately.", "author": "Xi Xiangyu; Wei Ye; Shikun Zhang; Quanxiu Wang; Huixing Jiang; Wei Wu", "authorids": "/x/xi-xiangyu/; /w/wei-ye/; /s/shikun-zhang/; /q/quanxiu-wang/; /h/huixing-jiang/; /w/wei-wu/", "bibtex": "@inproceedings{xiangyu-etal-2021-capturing,\n title = \"Capturing Event Argument Interaction via A Bi-Directional Entity-Level Recurrent Decoder\",\n author = \"Xiangyu, Xi and\n Ye, Wei and\n Zhang, Shikun and\n Wang, Quanxiu and\n Jiang, Huixing and\n Wu, Wei\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.18/\",\n doi = \"10.18653/v1/2021.acl-long.18\",\n pages = \"210--219\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.18.pdf", "site": "https://aclanthology.org/2021.acl-long.18/", "pdf_size": 893271, "gs_citation": 20, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=3395331101948133645&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 2, "aff": "National Engineering Research Center for Software Engineering, Peking University, Beijing, China+Meituan Group, Beijing, China; National Engineering Research Center for Software Engineering, Peking University, Beijing, China+Meituan Group, Beijing, China; National Engineering Research Center for Software Engineering, Peking University, Beijing, China+Meituan Group, Beijing, China; RICH AI, Beijing, China; Meituan Group, Beijing, China; Meituan Group, Beijing, China", "aff_domain": "pku.edu.cn;pku.edu.cn;pku.edu.cn; ; ; ", "email": "pku.edu.cn;pku.edu.cn;pku.edu.cn; ; ; ", "github": "", "project": "", "author_num": 6, "aff_unique_index": "0+1;0+1;0+1;2;1;1", "aff_unique_norm": "Peking University;Meituan Group;RICH AI", "aff_unique_dep": "National Engineering Research Center for Software Engineering;;", "aff_unique_url": "http://www.pku.edu.cn;https://www.meituan.com;", "aff_unique_abbr": "PKU;Meituan;", "aff_campus_unique_index": "0+0;0+0;0+0;0;0", "aff_campus_unique": "Beijing;", "aff_country_unique_index": "0+0;0+0;0+0;0;0;0", "aff_country_unique": "China" }, { "id": "2021.acl-long.473", "title": "Capturing Relations between Scientific Papers: An Abstractive Model for Related Work Section Generation", "track": "main", "status": "Long", "award": false, "abstract": "Given a set of related publications, related work section generation aims to provide researchers with an overview of the specific research area by summarizing these works and introducing them in a logical order. Most of existing related work generation models follow the inflexible extractive style, which directly extract sentences from multiple original papers to form a related work discussion. Hence, in this paper, we propose a Relation-aware Related work Generator (RRG), which generates an abstractive related work from the given multiple scientific papers in the same research area. Concretely, we propose a relation-aware multi-document encoder that relates one document to another according to their content dependency in a relation graph. The relation graph and the document representation are interacted and polished iteratively, complementing each other in the training process. We also contribute two public datasets composed of related work sections and their corresponding papers. Extensive experiments on the two datasets show that the proposed model brings substantial improvements over several strong baselines. We hope that this work will promote advances in related work generation task.", "author": "Xiuying Chen; Hind Alamro; Mingzhe Li; Shen Gao; Xiangliang Zhang; Dongyan Zhao; Rui Yan", "authorids": "/x/xiuying-chen/; /h/hind-alamro/; /m/mingzhe-li/; /s/shen-gao/; /x/xiangliang-zhang/; /d/dongyan-zhao/; /r/rui-yan/", "bibtex": "@inproceedings{chen-etal-2021-capturing,\n title = \"Capturing Relations between Scientific Papers: An Abstractive Model for Related Work Section Generation\",\n author = \"Chen, Xiuying and\n Alamro, Hind and\n Li, Mingzhe and\n Gao, Shen and\n Zhang, Xiangliang and\n Zhao, Dongyan and\n Yan, Rui\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.473/\",\n doi = \"10.18653/v1/2021.acl-long.473\",\n pages = \"6068--6077\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.473.pdf", "site": "https://aclanthology.org/2021.acl-long.473/", "pdf_size": 690580, "gs_citation": 56, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=2498624237807846221&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 6, "aff": "Wangxuan Institute of Computer Technology, Peking University, Beijing, China+Center for Data Science, Peking University, Beijing, China; King Abdullah University of Science and Technology, Thuwal, Saudi Arabia+Computer Science Department, Umm Al-Qura University, Makkah, Saudi Arabia; Center for Data Science, Peking University, Beijing, China+Wangxuan Institute of Computer Technology, Peking University, Beijing, China; Wangxuan Institute of Computer Technology, Peking University, Beijing, China; King Abdullah University of Science and Technology, Thuwal, Saudi Arabia; State Key Laboratory of Media Convergence Production Technology and Systems+Wangxuan Institute of Computer Technology, Peking University, Beijing, China; Gaoling School of Artificial Intelligence, Renmin University of China", "aff_domain": "pku.edu.cn; ; ; ; ; ; ", "email": "pku.edu.cn; ; ; ; ; ; ", "github": "https://github.com/iriscxy/relatedworkgeneration", "project": "", "author_num": 7, "aff_unique_index": "0+0;1+2;0+0;0;1;3+0;4", "aff_unique_norm": "Peking University;King Abdullah University of Science and Technology;Umm Al-Qura University;State Key Laboratory of Media Convergence Production Technology and Systems;Renmin University of China", "aff_unique_dep": "Wangxuan Institute of Computer Technology;;Computer Science Department;;Gaoling School of Artificial Intelligence", "aff_unique_url": "http://www.pku.edu.cn;https://www.kast.kau.edu.sa;;;http://www.ruc.edu.cn", "aff_unique_abbr": "PKU;KAUST;;;RUC", "aff_campus_unique_index": "0+0;1+2;0+0;0;1;0;0", "aff_campus_unique": "Beijing;Thuwal;Makkah;", "aff_country_unique_index": "0+0;1+1;0+0;0;1;0+0;0", "aff_country_unique": "China;Saudi Arabia" }, { "id": "2021.acl-long.224", "title": "Cascade versus Direct Speech Translation: Do the Differences Still Make a Difference?", "track": "main", "status": "Long", "award": false, "abstract": "Five years after the first published proofs of concept, direct approaches to speech translation (ST) are now competing with traditional cascade solutions. In light of this steady progress, can we claim that the performance gap between the two is closed? Starting from this question, we present a systematic comparison between state-of-the-art systems representative of the two paradigms. Focusing on three language directions (English-German/Italian/Spanish), we conduct automatic and manual evaluations, exploiting high-quality professional post-edits and annotations. Our multi-faceted analysis on one of the few publicly available ST benchmarks attests for the first time that: i) the gap between the two paradigms is now closed, and ii) the subtle differences observed in their behavior are not sufficient for humans neither to distinguish them nor to prefer one over the other.", "author": "Luisa Bentivogli; Mauro Cettolo; Marco Gaido; Alina Karakanta; Alberto Martinelli; Matteo Negri; Marco Turchi", "authorids": "/l/luisa-bentivogli/; /m/mauro-cettolo/; /m/marco-gaido/; /a/alina-karakanta/; /a/alberto-martinelli/; /m/matteo-negri/; /m/marco-turchi/", "bibtex": "@inproceedings{bentivogli-etal-2021-cascade,\n title = \"Cascade versus Direct Speech Translation: Do the Differences Still Make a Difference?\",\n author = \"Bentivogli, Luisa and\n Cettolo, Mauro and\n Gaido, Marco and\n Karakanta, Alina and\n Martinelli, Alberto and\n Negri, Matteo and\n Turchi, Marco\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.224/\",\n doi = \"10.18653/v1/2021.acl-long.224\",\n pages = \"2873--2887\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.224.pdf", "site": "https://aclanthology.org/2021.acl-long.224/", "pdf_size": 396718, "gs_citation": 86, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=9982456452795444713&as_sdt=5,33&sciodt=0,33&hl=en", "gs_version_total": 11, "aff": "Fondazione Bruno Kessler; Fondazione Bruno Kessler; Fondazione Bruno Kessler + University of Trento; Fondazione Bruno Kessler + University of Trento; University of Trento; Fondazione Bruno Kessler; Fondazione Bruno Kessler", "aff_domain": "fbk.eu;fbk.eu;fbk.eu;fbk.eu;unitn.it;fbk.eu;fbk.eu", "email": "fbk.eu;fbk.eu;fbk.eu;fbk.eu;unitn.it;fbk.eu;fbk.eu", "github": "", "project": "http://iwslt.org", "author_num": 7, "aff_unique_index": "0;0;0+1;0+1;1;0;0", "aff_unique_norm": "Fondazione Bruno Kessler;University of Trento", "aff_unique_dep": ";", "aff_unique_url": "https://www.fbk.eu;https://www.unitn.it", "aff_unique_abbr": "FBK;UniTN", "aff_campus_unique_index": ";", "aff_campus_unique": "", "aff_country_unique_index": "0;0;0+0;0+0;0;0;0", "aff_country_unique": "Italy" }, { "id": "2021.acl-long.45", "title": "Cascaded Head-colliding Attention", "track": "main", "status": "Long", "award": false, "abstract": "Transformers have advanced the field of natural language processing (NLP) on a variety of important tasks. At the cornerstone of the Transformer architecture is the multi-head attention (MHA) mechanism which models pairwise interactions between the elements of the sequence. Despite its massive success, the current framework ignores interactions among different heads, leading to the problem that many of the heads are redundant in practice, which greatly wastes the capacity of the model. To improve parameter efficiency, we re-formulate the MHA as a latent variable model from a probabilistic perspective. We present cascaded head-colliding attention (CODA) which explicitly models the interactions between attention heads through a hierarchical variational distribution. We conduct extensive experiments and demonstrate that CODA outperforms the transformer baseline, by 0.6 perplexity on Wikitext-103 in language modeling, and by 0.6 BLEU on WMT14 EN-DE in machine translation, due to its improvements on the parameter efficiency.", "author": "Lin Zheng; Zhiyong Wu; Lingpeng Kong", "authorids": "/l/lin-zheng/; /z/zhiyong-wu/; /l/lingpeng-kong/", "bibtex": "@inproceedings{zheng-etal-2021-cascaded,\n title = \"Cascaded Head-colliding Attention\",\n author = \"Zheng, Lin and\n Wu, Zhiyong and\n Kong, Lingpeng\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.45/\",\n doi = \"10.18653/v1/2021.acl-long.45\",\n pages = \"536--549\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.45.pdf", "site": "https://aclanthology.org/2021.acl-long.45/", "pdf_size": 653044, "gs_citation": 2, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=8692507175414186661&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 5, "aff": "Department of Computer Science, The University of Hong Kong + Shanghai Arti\ufb01cial Intelligence Laboratory; Department of Computer Science, The University of Hong Kong; Department of Computer Science, The University of Hong Kong + Shanghai Arti\ufb01cial Intelligence Laboratory", "aff_domain": "mail2.sysu.edu.cn;cs.hku.hk;cs.hku.hk", "email": "mail2.sysu.edu.cn;cs.hku.hk;cs.hku.hk", "github": "https://github.com/LZhengisme/CODA", "project": "", "author_num": 3, "aff_unique_index": "0+1;0;0+1", "aff_unique_norm": "The University of Hong Kong;Shanghai Arti\ufb01cial Intelligence Laboratory", "aff_unique_dep": "Department of Computer Science;", "aff_unique_url": "https://www.hku.hk;https://www.shailab.org", "aff_unique_abbr": "HKU;ShAI Lab", "aff_campus_unique_index": ";", "aff_campus_unique": "", "aff_country_unique_index": "0+0;0;0+0", "aff_country_unique": "China" }, { "id": "2021.acl-short.1", "title": "Catchphrase: Automatic Detection of Cultural References", "track": "main", "status": "Short", "award": false, "abstract": "A snowclone is a customizable phrasal template that can be realized in multiple, instantly recognized variants. For example, \u201c* is the new *\" (Orange is the new black, 40 is the new 30). Snowclones are extensively used in social media. In this paper, we study snowclones originating from pop-culture quotes; our goal is to automatically detect cultural references in text. We introduce a new, publicly available data set of pop-culture quotes and their corresponding snowclone usages and train models on them. We publish code for Catchphrase, an internet browser plugin to automatically detect and mark references in real-time, and examine its performance via a user study. Aside from assisting people to better comprehend cultural references, we hope that detecting snowclones can complement work on paraphrasing and help tackling long-standing questions in social science about the dynamics of information propagation.", "author": "Nir Sweed; Dafna Shahaf", "authorids": "/n/nir-sweed/; /d/dafna-shahaf/", "bibtex": "@inproceedings{sweed-shahaf-2021-catchphrase,\n title = \"Catchphrase: Automatic Detection of Cultural References\",\n author = \"Sweed, Nir and\n Shahaf, Dafna\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-short.1/\",\n doi = \"10.18653/v1/2021.acl-short.1\",\n pages = \"1--7\"\n}", "pdf": "https://aclanthology.org/2021.acl-short.1.pdf", "site": "https://aclanthology.org/2021.acl-short.1/", "pdf_size": 688967, "gs_citation": 3, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=16451093132243538686&as_sdt=5,24&sciodt=0,24&hl=en", "gs_version_total": 6, "aff": "The Hebrew University of Jerusalem; The Hebrew University of Jerusalem", "aff_domain": "mail.huji.ac.il;cs.huji.ac.il", "email": "mail.huji.ac.il;cs.huji.ac.il", "github": "", "project": "", "author_num": 2, "aff_unique_index": "0;0", "aff_unique_norm": "The Hebrew University of Jerusalem", "aff_unique_dep": "", "aff_unique_url": "https://www.huji.ac.il", "aff_unique_abbr": "HUJI", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0;0", "aff_country_unique": "Israel" }, { "id": "2021.acl-long.144", "title": "Causal Analysis of Syntactic Agreement Mechanisms in Neural Language Models", "track": "main", "status": "Long", "award": false, "abstract": "Targeted syntactic evaluations have demonstrated the ability of language models to perform subject-verb agreement given difficult contexts. To elucidate the mechanisms by which the models accomplish this behavior, this study applies causal mediation analysis to pre-trained neural language models. We investigate the magnitude of models\u2019 preferences for grammatical inflections, as well as whether neurons process subject-verb agreement similarly across sentences with different syntactic structures. We uncover similarities and differences across architectures and model sizes\u2014notably, that larger models do not necessarily learn stronger preferences. We also observe two distinct mechanisms for producing subject-verb agreement depending on the syntactic structure of the input sentence. Finally, we find that language models rely on similar sets of neurons when given sentences with similar syntactic structure.", "author": "Matthew Finlayson; Aaron Mueller; Sebastian Gehrmann; Stuart Shieber; Tal Linzen; Yonatan Belinkov", "authorids": "/m/matthew-finlayson/; /a/aaron-mueller/; /s/sebastian-gehrmann/; /s/stuart-m-shieber/; /t/tal-linzen/; /y/yonatan-belinkov/", "bibtex": "@inproceedings{finlayson-etal-2021-causal,\n title = \"Causal Analysis of Syntactic Agreement Mechanisms in Neural Language Models\",\n author = \"Finlayson, Matthew and\n Mueller, Aaron and\n Gehrmann, Sebastian and\n Shieber, Stuart and\n Linzen, Tal and\n Belinkov, Yonatan\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.144/\",\n doi = \"10.18653/v1/2021.acl-long.144\",\n pages = \"1828--1843\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.144.pdf", "site": "https://aclanthology.org/2021.acl-long.144/", "pdf_size": 1185448, "gs_citation": 102, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=17284502218312106656&as_sdt=5,44&sciodt=0,44&hl=en", "gs_version_total": 9, "aff": "Harvard University; Johns Hopkins University; Google Research; Harvard University; New York University; Technion \u2013 IIT", "aff_domain": "gmail.com;jhu.edu;google.com;seas.harvard.edu;nyu.edu;technion.ac.il", "email": "gmail.com;jhu.edu;google.com;seas.harvard.edu;nyu.edu;technion.ac.il", "github": "", "project": "", "author_num": 6, "aff_unique_index": "0;1;2;0;3;4", "aff_unique_norm": "Harvard University;Johns Hopkins University;Google;New York University;Technion \u2013 Israel Institute of Technology", "aff_unique_dep": ";;Google Research;;", "aff_unique_url": "https://www.harvard.edu;https://www.jhu.edu;https://research.google;https://www.nyu.edu;https://www.technion.ac.il/en/", "aff_unique_abbr": "Harvard;JHU;Google Research;NYU;Technion", "aff_campus_unique_index": "1", "aff_campus_unique": ";Mountain View", "aff_country_unique_index": "0;0;0;0;0;1", "aff_country_unique": "United States;Israel" }, { "id": "2021.acl-long.118", "title": "Challenges in Information-Seeking QA: Unanswerable Questions and Paragraph Retrieval", "track": "main", "status": "Long", "award": false, "abstract": "Recent pretrained language models \u201csolved\u201d many reading comprehension benchmarks, where questions are written with access to the evidence document. However, datasets containing information-seeking queries where evidence documents are provided after the queries are written independently remain challenging. We analyze why answering information-seeking queries is more challenging and where their prevalent unanswerabilities arise, on Natural Questions and TyDi QA. Our controlled experiments suggest two headrooms \u2013 paragraph selection and answerability prediction, i.e. whether the paired evidence document contains the answer to the query or not. When provided with a gold paragraph and knowing when to abstain from answering, existing models easily outperform a human annotator. However, predicting answerability itself remains challenging. We manually annotate 800 unanswerable examples across six languages on what makes them challenging to answer. With this new data, we conduct per-category answerability prediction, revealing issues in the current dataset collection as well as task formulation. Together, our study points to avenues for future research in information-seeking question answering, both for dataset creation and model development. Our code and annotated data is publicly available at https://github.com/AkariAsai/unanswerable_qa.", "author": "Akari Asai; Eunsol Choi", "authorids": "/a/akari-asai/; /e/eunsol-choi/", "bibtex": "@inproceedings{asai-choi-2021-challenges,\n title = \"Challenges in Information-Seeking {QA}: Unanswerable Questions and Paragraph Retrieval\",\n author = \"Asai, Akari and\n Choi, Eunsol\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.118/\",\n doi = \"10.18653/v1/2021.acl-long.118\",\n pages = \"1492--1504\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.118.pdf", "site": "https://aclanthology.org/2021.acl-long.118/", "pdf_size": 367178, "gs_citation": 50, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=18407127006591242714&as_sdt=4005&sciodt=0,6&hl=en", "gs_version_total": 5, "aff": "University of Washington; The University of Texas at Austin", "aff_domain": "cs.washington.edu;cs.utexas.edu", "email": "cs.washington.edu;cs.utexas.edu", "github": "https://github.com/AkariAsai/unanswerable_qa", "project": "", "author_num": 2, "aff_unique_index": "0;1", "aff_unique_norm": "University of Washington;University of Texas at Austin", "aff_unique_dep": ";", "aff_unique_url": "https://www.washington.edu;https://www.utexas.edu", "aff_unique_abbr": "UW;UT Austin", "aff_campus_unique_index": "1", "aff_campus_unique": ";Austin", "aff_country_unique_index": "0;0", "aff_country_unique": "United States" }, { "id": "2021.acl-long.129", "title": "Changes in European Solidarity Before and During COVID-19: Evidence from a Large Crowd- and Expert-Annotated Twitter Dataset", "track": "main", "status": "Long", "award": false, "abstract": "We introduce the well-established social scientific concept of social solidarity and its contestation, anti-solidarity, as a new problem setting to supervised machine learning in NLP to assess how European solidarity discourses changed before and after the COVID-19 outbreak was declared a global pandemic. To this end, we annotate 2.3k English and German tweets for (anti-)solidarity expressions, utilizing multiple human annotators and two annotation approaches (experts vs. crowds). We use these annotations to train a BERT model with multiple data augmentation strategies. Our augmented BERT model that combines both expert and crowd annotations outperforms the baseline BERT classifier trained with expert annotations only by over 25 points, from 58% macro-F1 to almost 85%. We use this high-quality model to automatically label over 270k tweets between September 2019 and December 2020. We then assess the automatically labeled data for how statements related to European (anti-)solidarity discourses developed over time and in relation to one another, before and during the COVID-19 crisis. Our results show that solidarity became increasingly salient and contested during the crisis. While the number of solidarity tweets remained on a higher level and dominated the discourse in the scrutinized time frame, anti-solidarity tweets initially spiked, then decreased to (almost) pre-COVID-19 values before rising to a stable higher level until the end of 2020.", "author": "Alexandra Ils; Dan Liu; Daniela Grunow; Steffen Eger", "authorids": "/a/alexandra-ils/; /d/dan-liu/; /d/daniela-grunow/; /s/steffen-eger/", "bibtex": "@inproceedings{ils-etal-2021-changes,\n title = \"Changes in {E}uropean Solidarity Before and During {COVID}-19: Evidence from a Large Crowd- and Expert-Annotated {T}witter Dataset\",\n author = \"Ils, Alexandra and\n Liu, Dan and\n Grunow, Daniela and\n Eger, Steffen\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.129/\",\n doi = \"10.18653/v1/2021.acl-long.129\",\n pages = \"1623--1637\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.129.pdf", "site": "https://aclanthology.org/2021.acl-long.129/", "pdf_size": 463959, "gs_citation": 9, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=17618511970977219747&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 7, "aff": "Department of Social Sciences, Goethe University Frankfurt am Main; Natural Language Learning Group, Technische Universit \u00a8at Darmstadt; Department of Social Sciences, Goethe University Frankfurt am Main; Natural Language Learning Group, Technische Universit \u00a8at Darmstadt", "aff_domain": "soz.uni-frankfurt.de;stud.tu-darmstadt.de;soz.uni-frankfurt.de;aiphes.tu-darmstadt.de", "email": "soz.uni-frankfurt.de;stud.tu-darmstadt.de;soz.uni-frankfurt.de;aiphes.tu-darmstadt.de", "github": "", "project": "", "author_num": 4, "aff_unique_index": "0;1;0;1", "aff_unique_norm": "Goethe University Frankfurt;Technische Universit\u00e4t Darmstadt", "aff_unique_dep": "Department of Social Sciences;Natural Language Learning Group", "aff_unique_url": "https://www.uni-frankfurt.de;https://www.tu-darmstadt.de", "aff_unique_abbr": "Goethe U;TU Darmstadt", "aff_campus_unique_index": "0;0", "aff_campus_unique": "Frankfurt am Main;", "aff_country_unique_index": "0;0;0;0", "aff_country_unique": "Germany" }, { "id": "2021.acl-long.170", "title": "Changing the World by Changing the Data", "track": "main", "status": "Long", "award": false, "abstract": "NLP community is currently investing a lot more research and resources into development of deep learning models than training data. While we have made a lot of progress, it is now clear that our models learn all kinds of spurious patterns, social biases, and annotation artifacts. Algorithmic solutions have so far had limited success. An alternative that is being actively discussed is more careful design of datasets so as to deliver specific signals. This position paper maps out the arguments for and against data curation, and argues that fundamentally the point is moot: curation already is and will be happening, and it is changing the world. The question is only how much thought we want to invest into that process.", "author": "Anna Rogers", "authorids": "/a/anna-rogers/", "bibtex": "@inproceedings{rogers-2021-changing,\n title = \"Changing the World by Changing the Data\",\n author = \"Rogers, Anna\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.170/\",\n doi = \"10.18653/v1/2021.acl-long.170\",\n pages = \"2182--2194\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.170.pdf", "site": "https://aclanthology.org/2021.acl-long.170/", "pdf_size": 312391, "gs_citation": 97, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=1756380900982098714&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 6, "aff": "Center for Social Data Science, University of Copenhagen", "aff_domain": "sodas.ku.dk", "email": "sodas.ku.dk", "github": "", "project": "", "author_num": 1, "aff_unique_index": "0", "aff_unique_norm": "University of Copenhagen", "aff_unique_dep": "Center for Social Data Science", "aff_unique_url": "https://www.ku.dk", "aff_unique_abbr": "UCPH", "aff_country_unique_index": "0", "aff_country_unique": "Denmark" }, { "id": "2021.acl-long.180", "title": "Chase: A Large-Scale and Pragmatic Chinese Dataset for Cross-Database Context-Dependent Text-to-SQL", "track": "main", "status": "Long", "award": false, "abstract": "The cross-database context-dependent Text-to-SQL (XDTS) problem has attracted considerable attention in recent years due to its wide range of potential applications. However, we identify two biases in existing datasets for XDTS: (1) a high proportion of context-independent questions and (2) a high proportion of easy SQL queries. These biases conceal the major challenges in XDTS to some extent. In this work, we present Chase, a large-scale and pragmatic Chinese dataset for XDTS. It consists of 5,459 coherent question sequences (17,940 questions with their SQL queries annotated) over 280 databases, in which only 35% of questions are context-independent, and 28% of SQL queries are easy. We experiment on Chase with three state-of-the-art XDTS approaches. The best approach only achieves an exact match accuracy of 40% over all questions and 16% over all question sequences, indicating that Chase highlights the challenging problems of XDTS. We believe that XDTS can provide fertile soil for addressing the problems.", "author": "Jiaqi Guo; Ziliang Si; Yu Wang; Qian Liu; Ming Fan; Jian-Guang Lou; Zijiang Yang; Ting Liu", "authorids": "/j/jiaqi-guo/; /z/ziliang-si/; /y/yu-wang/; /q/qian-liu/; /m/ming-fan/; /j/jian-guang-lou/; /z/zijiang-yang/; /t/ting-liu/", "bibtex": "@inproceedings{guo-etal-2021-chase,\n title = \"Chase: A Large-Scale and Pragmatic {C}hinese Dataset for Cross-Database Context-Dependent Text-to-{SQL}\",\n author = \"Guo, Jiaqi and\n Si, Ziliang and\n Wang, Yu and\n Liu, Qian and\n Fan, Ming and\n Lou, Jian-Guang and\n Yang, Zijiang and\n Liu, Ting\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.180/\",\n doi = \"10.18653/v1/2021.acl-long.180\",\n pages = \"2316--2331\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.180.pdf", "site": "https://aclanthology.org/2021.acl-long.180/", "pdf_size": 927463, "gs_citation": 17, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=1903182079525005331&as_sdt=80000005&sciodt=0,23&hl=en", "gs_version_total": 3, "aff": "Xi\u2019an Jiaotong University; Xi\u2019an Jiaotong University; Xi\u2019an Jiaotong University; Beihang University; Xi\u2019an Jiaotong University; Microsoft Research; Xi\u2019an Jiaotong University+GuardStrike Inc.; Xi\u2019an Jiaotong University", "aff_domain": "stu.xjtu.edu.cn;stu.xjtu.edu.cn;stu.xjtu.edu.cn;buaa.edu.cn;mail.xjtu.edu.cn;microsoft.com;guardstrike.com;mail.xjtu.edu.cn", "email": "stu.xjtu.edu.cn;stu.xjtu.edu.cn;stu.xjtu.edu.cn;buaa.edu.cn;mail.xjtu.edu.cn;microsoft.com;guardstrike.com;mail.xjtu.edu.cn", "github": "", "project": "", "author_num": 8, "aff_unique_index": "0;0;0;1;0;2;0+3;0", "aff_unique_norm": "Xi'an Jiaotong University;Beihang University;Microsoft Corporation;GuardStrike Inc.", "aff_unique_dep": ";;Microsoft Research;", "aff_unique_url": "https://www.xjtu.edu.cn;http://www.buaa.edu.cn/;https://www.microsoft.com/en-us/research;https://www.guardstrike.com", "aff_unique_abbr": "XJTU;BUAA;MSR;GuardStrike", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0;0;0;0;0;1;0+1;0", "aff_country_unique": "China;United States" }, { "id": "2021.acl-long.317", "title": "Check It Again:Progressive Visual Question Answering via Visual Entailment", "track": "main", "status": "Long", "award": false, "abstract": "While sophisticated neural-based models have achieved remarkable success in Visual Question Answering (VQA), these models tend to answer questions only according to superficial correlations between question and answer. Several recent approaches have been developed to address this language priors problem. However, most of them predict the correct answer according to one best output without checking the authenticity of answers. Besides, they only explore the interaction between image and question, ignoring the semantics of candidate answers. In this paper, we propose a select-and-rerank (SAR) progressive framework based on Visual Entailment. Specifically, we first select the candidate answers relevant to the question or the image, then we rerank the candidate answers by a visual entailment task, which verifies whether the image semantically entails the synthetic statement of the question and each candidate answer. Experimental results show the effectiveness of our proposed framework, which establishes a new state-of-the-art accuracy on VQA-CP v2 with a 7.55% improvement.", "author": "Qingyi Si; Zheng Lin; Ming yu Zheng; Peng Fu; Weiping Wang", "authorids": "/q/qingyi-si/; /z/zheng-lin/; /m/ming-yu-zheng/; /p/peng-fu/; /w/weiping-wang/", "bibtex": "@inproceedings{si-etal-2021-check,\n title = \"Check It Again:Progressive Visual Question Answering via Visual Entailment\",\n author = \"Si, Qingyi and\n Lin, Zheng and\n Zheng, Ming yu and\n Fu, Peng and\n Wang, Weiping\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.317/\",\n doi = \"10.18653/v1/2021.acl-long.317\",\n pages = \"4101--4110\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.317.pdf", "site": "https://aclanthology.org/2021.acl-long.317/", "pdf_size": 1245976, "gs_citation": 48, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=12626429879233668942&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 4, "aff": "Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China+School of Cyber Security, University of Chinese Academy of Sciences, Beijing, China; Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China; Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China+School of Cyber Security, University of Chinese Academy of Sciences, Beijing, China; Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China; Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China", "aff_domain": "iie.ac.cn;iie.ac.cn;iie.ac.cn;iie.ac.cn;iie.ac.cn", "email": "iie.ac.cn;iie.ac.cn;iie.ac.cn;iie.ac.cn;iie.ac.cn", "github": "https://github.com/PhoebusSi/SAR", "project": "", "author_num": 5, "aff_unique_index": "0+1;0;0+1;0;0", "aff_unique_norm": "Chinese Academy of Sciences;University of Chinese Academy of Sciences", "aff_unique_dep": "Institute of Information Engineering;School of Cyber Security", "aff_unique_url": "http://www.cas.cn;http://www.ucas.ac.cn", "aff_unique_abbr": "CAS;UCAS", "aff_campus_unique_index": "0+0;0;0+0;0;0", "aff_campus_unique": "Beijing", "aff_country_unique_index": "0+0;0;0+0;0;0", "aff_country_unique": "China" }, { "id": "2021.acl-long.161", "title": "ChineseBERT: Chinese Pretraining Enhanced by Glyph and Pinyin Information", "track": "main", "status": "Long", "award": false, "abstract": "Recent pretraining models in Chinese neglect two important aspects specific to the Chinese language: glyph and pinyin, which carry significant syntax and semantic information for language understanding. In this work, we propose ChineseBERT, which incorporates both the glyph and pinyin information of Chinese characters into language model pretraining. The glyph embedding is obtained based on different fonts of a Chinese character, being able to capture character semantics from the visual features, and the pinyin embedding characterizes the pronunciation of Chinese characters, which handles the highly prevalent heteronym phenomenon in Chinese (the same character has different pronunciations with different meanings). Pretrained on large-scale unlabeled Chinese corpus, the proposed ChineseBERT model yields significant performance boost over baseline models with fewer training steps. The proposed model achieves new SOTA performances on a wide range of Chinese NLP tasks, including machine reading comprehension, natural language inference, text classification, sentence pair matching, and competitive performances in named entity recognition and word segmentation.", "author": "Zijun Sun; Xiaoya Li; Xiaofei Sun; Yuxian Meng; Xiang Ao; Qing He; Fei Wu; Jiwei Li", "authorids": "/z/zijun-sun/; /x/xiaoya-li/; /x/xiaofei-sun/; /y/yuxian-meng/; /x/xiang-ao/; /q/qing-he/; /f/fei-wu/; /j/jiwei-li/", "bibtex": "@inproceedings{sun-etal-2021-chinesebert,\n title = \"{C}hinese{BERT}: {C}hinese Pretraining Enhanced by Glyph and {P}inyin Information\",\n author = \"Sun, Zijun and\n Li, Xiaoya and\n Sun, Xiaofei and\n Meng, Yuxian and\n Ao, Xiang and\n He, Qing and\n Wu, Fei and\n Li, Jiwei\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.161/\",\n doi = \"10.18653/v1/2021.acl-long.161\",\n pages = \"2065--2075\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.161.pdf", "site": "https://aclanthology.org/2021.acl-long.161/", "pdf_size": 907052, "gs_citation": 233, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=4592189532924131846&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 5, "aff": "Shannon.AI; Shannon.AI; Shannon.AI; Shannon.AI; Key Lab of Intelligent Information Processing of Chinese Academy of Sciences; Key Lab of Intelligent Information Processing of Chinese Academy of Sciences; Zhejiang University; Shannon.AI+Zhejiang University", "aff_domain": "shannonai.com;shannonai.com;shannonai.com;shannonai.com;ict.ac.cn;ict.ac.cn;zju.edu.cn;shannonai.com", "email": "shannonai.com;shannonai.com;shannonai.com;shannonai.com;ict.ac.cn;ict.ac.cn;zju.edu.cn;shannonai.com", "github": "https://github.com/ShannonAI/ChineseBert", "project": "", "author_num": 8, "aff_unique_index": "0;0;0;0;1;1;2;0+2", "aff_unique_norm": "Shannon.AI;Chinese Academy of Sciences;Zhejiang University", "aff_unique_dep": ";Key Lab of Intelligent Information Processing;", "aff_unique_url": "https://www.shannon.ai;http://www.cas.cn/;https://www.zju.edu.cn", "aff_unique_abbr": "Shannon.AI;CAS;ZJU", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0;0;0;0;1;1;1;0+1", "aff_country_unique": "United States;China" }, { "id": "2021.acl-long.59", "title": "CitationIE: Leveraging the Citation Graph for Scientific Information Extraction", "track": "main", "status": "Long", "award": false, "abstract": "Automatically extracting key information from scientific documents has the potential to help scientists work more efficiently and accelerate the pace of scientific progress. Prior work has considered extracting document-level entity clusters and relations end-to-end from raw scientific text, which can improve literature search and help identify methods and materials for a given problem. Despite the importance of this task, most existing works on scientific information extraction (SciIE) consider extraction solely based on the content of an individual paper, without considering the paper\u2019s place in the broader literature. In contrast to prior work, we augment our text representations by leveraging a complementary source of document context: the citation graph of referential links between citing and cited papers. On a test set of English-language scientific documents, we show that simple ways of utilizing the structure and content of the citation graph can each lead to significant gains in different scientific information extraction tasks. When these tasks are combined, we observe a sizable improvement in end-to-end information extraction over the state-of-the-art, suggesting the potential for future work along this direction. We release software tools to facilitate citation-aware SciIE development.", "author": "Vijay Viswanathan; Graham Neubig; Pengfei Liu", "authorids": "/v/vijay-viswanathan/; /g/graham-neubig/; /p/pengfei-liu/", "bibtex": "@inproceedings{viswanathan-etal-2021-citationie,\n title = \"{C}itation{IE}: Leveraging the Citation Graph for Scientific Information Extraction\",\n author = \"Viswanathan, Vijay and\n Neubig, Graham and\n Liu, Pengfei\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.59/\",\n doi = \"10.18653/v1/2021.acl-long.59\",\n pages = \"719--731\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.59.pdf", "site": "https://aclanthology.org/2021.acl-long.59/", "pdf_size": 827447, "gs_citation": 29, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=6821654900050431171&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 4, "aff": "Language Technologies Institute, Carnegie Mellon University; Language Technologies Institute, Carnegie Mellon University; Language Technologies Institute, Carnegie Mellon University", "aff_domain": "cs.cmu.edu;cs.cmu.edu;cs.cmu.edu", "email": "cs.cmu.edu;cs.cmu.edu;cs.cmu.edu", "github": "https://github.com/viswavi/ScigraphIE", "project": "", "author_num": 3, "aff_unique_index": "0;0;0", "aff_unique_norm": "Carnegie Mellon University", "aff_unique_dep": "Language Technologies Institute", "aff_unique_url": "https://www.cmu.edu", "aff_unique_abbr": "CMU", "aff_campus_unique_index": "0;0;0", "aff_campus_unique": "Pittsburgh", "aff_country_unique_index": "0;0;0", "aff_country_unique": "United States" }, { "id": "2021.acl-long.347", "title": "Claim Matching Beyond English to Scale Global Fact-Checking", "track": "main", "status": "Long", "award": false, "abstract": "Manual fact-checking does not scale well to serve the needs of the internet. This issue is further compounded in non-English contexts. In this paper, we discuss claim matching as a possible solution to scale fact-checking. We define claim matching as the task of identifying pairs of textual messages containing claims that can be served with one fact-check. We construct a novel dataset of WhatsApp tipline and public group messages alongside fact-checked claims that are first annotated for containing \u201cclaim-like statements\u201d and then matched with potentially similar items and annotated for claim matching. Our dataset contains content in high-resource (English, Hindi) and lower-resource (Bengali, Malayalam, Tamil) languages. We train our own embedding model using knowledge distillation and a high-quality \u201cteacher\u201d model in order to address the imbalance in embedding quality between the low- and high-resource languages in our dataset. We provide evaluations on the performance of our solution and compare with baselines and existing state-of-the-art multilingual embedding models, namely LASER and LaBSE. We demonstrate that our performance exceeds LASER and LaBSE in all settings. We release our annotated datasets, codebooks, and trained embedding model to allow for further research.", "author": "Ashkan Kazemi; Kiran Garimella; Devin Gaffney; Scott A. Hale", "authorids": "/a/ashkan-kazemi/; /k/kiran-garimella/; /d/devin-gaffney/; /s/scott-a-hale/", "bibtex": "@inproceedings{kazemi-etal-2021-claim,\n title = \"Claim Matching Beyond {E}nglish to Scale Global Fact-Checking\",\n author = \"Kazemi, Ashkan and\n Garimella, Kiran and\n Gaffney, Devin and\n Hale, Scott A.\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.347/\",\n doi = \"10.18653/v1/2021.acl-long.347\",\n pages = \"4504--4517\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.347.pdf", "site": "https://aclanthology.org/2021.acl-long.347/", "pdf_size": 854930, "gs_citation": 69, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=68623691255908852&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 7, "aff": "University of Michigan + Meedan; MIT; Meedan; University of Oxford + Meedan", "aff_domain": "umich.edu;mit.edu;meedan.com;meedan.com", "email": "umich.edu;mit.edu;meedan.com;meedan.com", "github": "", "project": "https://huggingface.co/meedan/indian-xlm-r", "author_num": 4, "aff_unique_index": "0+1;2;1;3+1", "aff_unique_norm": "University of Michigan;Meedan;Massachusetts Institute of Technology;University of Oxford", "aff_unique_dep": ";;;", "aff_unique_url": "https://www.umich.edu;https://meedan.com;https://web.mit.edu;https://www.ox.ac.uk", "aff_unique_abbr": "UM;;MIT;Oxford", "aff_campus_unique_index": ";", "aff_campus_unique": "", "aff_country_unique_index": "0+0;0;0;1+0", "aff_country_unique": "United States;United Kingdom" }, { "id": "2021.acl-long.363", "title": "CoRI: Collective Relation Integration with Data Augmentation for Open Information Extraction", "track": "main", "status": "Long", "award": false, "abstract": "Integrating extracted knowledge from the Web to knowledge graphs (KGs) can facilitate tasks like question answering. We study relation integration that aims to align free-text relations in subject-relation-object extractions to relations in a target KG. To address the challenge that free-text relations are ambiguous, previous methods exploit neighbor entities and relations for additional context. However, the predictions are made independently, which can be mutually inconsistent. We propose a two-stage Collective Relation Integration (CoRI) model, where the first stage independently makes candidate predictions, and the second stage employs a collective model that accesses all candidate predictions to make globally coherent predictions. We further improve the collective model with augmented data from the portion of the target KG that is otherwise unused. Experiment results on two datasets show that CoRI can significantly outperform the baselines, improving AUC from .677 to .748 and from .716 to .780, respectively.", "author": "Zhengbao Jiang; Jialong Han; Bunyamin Sisman; Xin Luna Dong", "authorids": "/z/zhengbao-jiang/; /j/jialong-han/; /b/bunyamin-sisman/; /x/xin-luna-dong/", "bibtex": "@inproceedings{jiang-etal-2021-cori,\n title = \"{C}o{RI}: Collective Relation Integration with Data Augmentation for Open Information Extraction\",\n author = \"Jiang, Zhengbao and\n Han, Jialong and\n Sisman, Bunyamin and\n Dong, Xin Luna\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.363/\",\n doi = \"10.18653/v1/2021.acl-long.363\",\n pages = \"4706--4716\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.363.pdf", "site": "https://aclanthology.org/2021.acl-long.363/", "pdf_size": 868585, "gs_citation": 6, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=277364130293967687&as_sdt=5,33&sciodt=0,33&hl=en", "gs_version_total": 5, "aff": "Language Technologies Institute, Carnegie Mellon University1 + Amazon2; Amazon2; Amazon2; Amazon2", "aff_domain": "cs.cmu.edu;amazon.com;amazon.com;amazon.com", "email": "cs.cmu.edu;amazon.com;amazon.com;amazon.com", "github": "", "project": "", "author_num": 4, "aff_unique_index": "0+1;1;1;1", "aff_unique_norm": "Carnegie Mellon University;Amazon", "aff_unique_dep": "Language Technologies Institute;", "aff_unique_url": "https://www.cmu.edu;https://www.amazon.com", "aff_unique_abbr": "CMU;Amazon", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0+0;0;0;0", "aff_country_unique": "United States" }, { "id": "2021.acl-long.442", "title": "CoSQA: 20,000+ Web Queries for Code Search and Question Answering", "track": "main", "status": "Long", "award": false, "abstract": "Finding codes given natural language query is beneficial to the productivity of software developers. Future progress towards better semantic matching between query and code requires richer supervised training resources. To remedy this, we introduce CoSQA dataset. It includes 20,604 labels for pairs of natural language queries and codes, each annotated by at least 3 human annotators. We further introduce a contrastive learning method dubbed CoCLR to enhance text-code matching, which works as a data augmenter to bring more artificially generated training instances. We show that, evaluated on CodeXGLUE with the same CodeBERT model, training on CoSQA improves the accuracy of code question answering by 5.1% and incorporating CoCLR brings a further improvement of 10.5%.", "author": "Junjie Huang; Duyu Tang; Linjun Shou; Ming Gong; Ke Xu; Daxin Jiang; Ming Zhou; Nan Duan", "authorids": "/j/junjie-huang/; /d/duyu-tang/; /l/linjun-shou/; /m/ming-gong/; /k/ke-xu/; /d/daxin-jiang/; /m/ming-zhou/; /n/nan-duan/", "bibtex": "@inproceedings{huang-etal-2021-cosqa,\n title = \"{C}o{SQA}: 20,000+ Web Queries for Code Search and Question Answering\",\n author = \"Huang, Junjie and\n Tang, Duyu and\n Shou, Linjun and\n Gong, Ming and\n Xu, Ke and\n Jiang, Daxin and\n Zhou, Ming and\n Duan, Nan\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.442/\",\n doi = \"10.18653/v1/2021.acl-long.442\",\n pages = \"5690--5700\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.442.pdf", "site": "https://aclanthology.org/2021.acl-long.442/", "pdf_size": 632659, "gs_citation": 111, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=12835319895298268069&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 4, "aff": "Beihang University; Microsoft Research Asia; Microsoft STC Asia; Microsoft STC Asia; Beihang University; Microsoft STC Asia; Microsoft Research Asia; Microsoft Research Asia", "aff_domain": "buaa.edu.cn; ; ; ;nlsde.buaa.edu.cn; ; ; ", "email": "buaa.edu.cn; ; ; ;nlsde.buaa.edu.cn; ; ; ", "github": "https://github.com/Jun-jie-Huang/CoCLR", "project": "https://github.com/microsoft/CodeXGLUE/tree/main/Text-Code/NL-code-search-WebQuery", "author_num": 8, "aff_unique_index": "0;1;2;2;0;2;1;1", "aff_unique_norm": "Beihang University;Microsoft Research;Microsoft", "aff_unique_dep": ";Research;STC", "aff_unique_url": "http://www.buaa.edu.cn/;https://www.microsoft.com/en-us/research/group/asia;https://www.microsoft.com", "aff_unique_abbr": "BUAA;MSR Asia;MS", "aff_campus_unique_index": "1;1;1;1;1;1", "aff_campus_unique": ";Asia", "aff_country_unique_index": "0;0;0;0;0;0;0;0", "aff_country_unique": "China" }, { "id": "2021.acl-short.98", "title": "Code Generation from Natural Language with Less Prior Knowledge and More Monolingual Data", "track": "main", "status": "Short", "award": false, "abstract": "Training datasets for semantic parsing are typically small due to the higher expertise required for annotation than most other NLP tasks. As a result, models for this application usually need additional prior knowledge to be built into the architecture or algorithm. The increased dependency on human experts hinders automation and raises the development and maintenance costs in practice. This work investigates whether a generic transformer-based seq2seq model can achieve competitive performance with minimal code-generation-specific inductive bias design. By exploiting a relatively sizeable monolingual corpus of the target programming language, which is cheap to mine from the web, we achieved 81.03% exact match accuracy on Django and 32.57 BLEU score on CoNaLa. Both are SOTA to the best of our knowledge. This positive evidence highlights a potentially easier path toward building accurate semantic parsers in practice.", "author": "Sajad Norouzi; Keyi Tang; Yanshuai Cao", "authorids": "/s/sajad-norouzi/; /k/keyi-tang/; /y/yanshuai-cao/", "bibtex": "@inproceedings{norouzi-etal-2021-code,\n title = \"Code Generation from Natural Language with Less Prior Knowledge and More Monolingual Data\",\n author = \"Norouzi, Sajad and\n Tang, Keyi and\n Cao, Yanshuai\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-short.98/\",\n doi = \"10.18653/v1/2021.acl-short.98\",\n pages = \"776--785\"\n}", "pdf": "https://aclanthology.org/2021.acl-short.98.pdf", "site": "https://aclanthology.org/2021.acl-short.98/", "pdf_size": 645367, "gs_citation": 25, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=2328234896300116003&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 2, "aff": "Borealis AI; Borealis AI; Borealis AI", "aff_domain": "cs.toronto.edu;borealisai.com;borealisai.com", "email": "cs.toronto.edu;borealisai.com;borealisai.com", "github": "https://github.com/BorealisAI/code-gen-TAE", "project": "", "author_num": 3, "aff_unique_index": "0;0;0", "aff_unique_norm": "Borealis AI", "aff_unique_dep": "", "aff_unique_url": "https://www.borealisai.com", "aff_unique_abbr": "Borealis AI", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0;0;0", "aff_country_unique": "Canada" }, { "id": "2021.acl-long.291", "title": "CogAlign: Learning to Align Textual Neural Representations to Cognitive Language Processing Signals", "track": "main", "status": "Long", "award": false, "abstract": "Most previous studies integrate cognitive language processing signals (e.g., eye-tracking or EEG data) into neural models of natural language processing (NLP) just by directly concatenating word embeddings with cognitive features, ignoring the gap between the two modalities (i.e., textual vs. cognitive) and noise in cognitive features. In this paper, we propose a CogAlign approach to these issues, which learns to align textual neural representations to cognitive features. In CogAlign, we use a shared encoder equipped with a modality discriminator to alternatively encode textual and cognitive inputs to capture their differences and commonalities. Additionally, a text-aware attention mechanism is proposed to detect task-related information and to avoid using noise in cognitive features. Experimental results on three NLP tasks, namely named entity recognition, sentiment analysis and relation extraction, show that CogAlign achieves significant improvements with multiple cognitive features over state-of-the-art models on public datasets. Moreover, our model is able to transfer cognitive information to other datasets that do not have any cognitive processing signals.", "author": "Yuqi Ren; Deyi Xiong", "authorids": "/y/yuqi-ren/; /d/deyi-xiong/", "bibtex": "@inproceedings{ren-xiong-2021-cogalign,\n title = \"{C}og{A}lign: Learning to Align Textual Neural Representations to Cognitive Language Processing Signals\",\n author = \"Ren, Yuqi and\n Xiong, Deyi\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.291/\",\n doi = \"10.18653/v1/2021.acl-long.291\",\n pages = \"3758--3769\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.291.pdf", "site": "https://aclanthology.org/2021.acl-long.291/", "pdf_size": 3043351, "gs_citation": 18, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=181456165199635840&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 4, "aff": "College of Intelligence and Computing, Tianjin University, Tianjin, China; College of Intelligence and Computing, Tianjin University, Tianjin, China", "aff_domain": "tju.edu.cn;tju.edu.cn", "email": "tju.edu.cn;tju.edu.cn", "github": "https://github.com/tjunlp-lab/CogAlign.git", "project": "", "author_num": 2, "aff_unique_index": "0;0", "aff_unique_norm": "Tianjin University", "aff_unique_dep": "College of Intelligence and Computing", "aff_unique_url": "http://www.tju.edu.cn", "aff_unique_abbr": "Tianjin University", "aff_campus_unique_index": "0;0", "aff_campus_unique": "Tianjin", "aff_country_unique_index": "0;0", "aff_country_unique": "China" }, { "id": "2021.acl-long.102", "title": "Common Sense Beyond English: Evaluating and Improving Multilingual Language Models for Commonsense Reasoning", "track": "main", "status": "Long", "award": false, "abstract": "Commonsense reasoning research has so far been limited to English. We aim to evaluate and improve popular multilingual language models (ML-LMs) to help advance commonsense reasoning (CSR) beyond English. We collect the Mickey corpus, consisting of 561k sentences in 11 different languages, which can be used for analyzing and improving ML-LMs. We propose Mickey Probe, a language-general probing task for fairly evaluating the common sense of popular ML-LMs across different languages. In addition, we also create two new datasets, X-CSQA and X-CODAH, by translating their English versions to 14 other languages, so that we can evaluate popular ML-LMs for cross-lingual commonsense reasoning. To improve the performance beyond English, we propose a simple yet effective method \u2014 multilingual contrastive pretraining (MCP). It significantly enhances sentence representations, yielding a large performance gain on both benchmarks (e.g., +2.7% accuracy for X-CSQA over XLM-R_L).", "author": "Bill Yuchen Lin; Seyeon Lee; Xiaoyang Qiao; Xiang Ren", "authorids": "/b/bill-yuchen-lin/; /s/seyeon-lee/; /x/xiaoyang-qiao/; /x/xiang-ren/", "bibtex": "@inproceedings{lin-etal-2021-common,\n title = \"Common Sense Beyond {E}nglish: Evaluating and Improving Multilingual Language Models for Commonsense Reasoning\",\n author = \"Lin, Bill Yuchen and\n Lee, Seyeon and\n Qiao, Xiaoyang and\n Ren, Xiang\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.102/\",\n doi = \"10.18653/v1/2021.acl-long.102\",\n pages = \"1274--1287\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.102.pdf", "site": "https://aclanthology.org/2021.acl-long.102/", "pdf_size": 915867, "gs_citation": 64, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=13410391615186549353&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 6, "aff": "Department of Computer Science and Information Sciences Institute, University of Southern California; Department of Computer Science and Information Sciences Institute, University of Southern California; Department of Computer Science and Information Sciences Institute, University of Southern California; Department of Computer Science and Information Sciences Institute, University of Southern California", "aff_domain": "usc.edu;usc.edu;usc.edu;usc.edu", "email": "usc.edu;usc.edu;usc.edu;usc.edu", "github": "", "project": "https://inklab.usc.edu/XCSR/", "author_num": 4, "aff_unique_index": "0;0;0;0", "aff_unique_norm": "University of Southern California", "aff_unique_dep": "Department of Computer Science and Information Sciences Institute", "aff_unique_url": "https://www.usc.edu", "aff_unique_abbr": "USC", "aff_campus_unique_index": "0;0;0;0", "aff_campus_unique": "Los Angeles", "aff_country_unique_index": "0;0;0;0", "aff_country_unique": "United States" }, { "id": "2021.acl-long.62", "title": "Compare to The Knowledge: Graph Neural Fake News Detection with External Knowledge", "track": "main", "status": "Long", "award": false, "abstract": "Nowadays, fake news detection, which aims to verify whether a news document is trusted or fake, has become urgent and important. Most existing methods rely heavily on linguistic and semantic features from the news content, and fail to effectively exploit external knowledge which could help determine whether the news document is trusted. In this paper, we propose a novel end-to-end graph neural model called CompareNet, which compares the news to the knowledge base (KB) through entities for fake news detection. Considering that fake news detection is correlated with topics, we also incorporate topics to enrich the news representation. Specifically, we first construct a directed heterogeneous document graph for each news incorporating topics and entities. Based on the graph, we develop a heterogeneous graph attention network for learning the topic-enriched news representation as well as the contextual entity representations that encode the semantics of the news content. The contextual entity representations are then compared to the corresponding KB-based entity representations through a carefully designed entity comparison network, to capture the consistency between the news content and KB. Finally, the topic-enriched news representation combining the entity comparison features is fed into a fake news classifier. Experimental results on two benchmark datasets demonstrate that CompareNet significantly outperforms state-of-the-art methods.", "author": "Linmei Hu; Tianchi Yang; Luhao Zhang; Wanjun Zhong; Duyu Tang; Chuan Shi; Nan Duan; Ming Zhou", "authorids": "/l/linmei-hu/; /t/tianchi-yang/; /l/luhao-zhang/; /w/wanjun-zhong/; /d/duyu-tang/; /c/chuan-shi/; /n/nan-duan/; /m/ming-zhou/", "bibtex": "@inproceedings{hu-etal-2021-compare,\n title = \"Compare to The Knowledge: Graph Neural Fake News Detection with External Knowledge\",\n author = \"Hu, Linmei and\n Yang, Tianchi and\n Zhang, Luhao and\n Zhong, Wanjun and\n Tang, Duyu and\n Shi, Chuan and\n Duan, Nan and\n Zhou, Ming\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.62/\",\n doi = \"10.18653/v1/2021.acl-long.62\",\n pages = \"754--763\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.62.pdf", "site": "https://aclanthology.org/2021.acl-long.62/", "pdf_size": 1755350, "gs_citation": 222, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=18140021721422080479&as_sdt=5,44&sciodt=0,44&hl=en", "gs_version_total": 8, "aff": "School of Computer Science, Beijing University of Posts and Telecommunications; School of Computer Science, Beijing University of Posts and Telecommunications; Meituan; Sun Yat-sen University; Microsoft Research Asia; School of Computer Science, Beijing University of Posts and Telecommunications; Microsoft Research Asia; Microsoft Research Asia", "aff_domain": "bupt.edu.cn;bupt.edu.cn;meituan.com;mail2.sysu.edu.cn;microsoft.com;bupt.edu.cn;microsoft.com;microsoft.com", "email": "bupt.edu.cn;bupt.edu.cn;meituan.com;mail2.sysu.edu.cn;microsoft.com;bupt.edu.cn;microsoft.com;microsoft.com", "github": "", "project": "", "author_num": 8, "aff_unique_index": "0;0;1;2;3;0;3;3", "aff_unique_norm": "Beijing University of Posts and Telecommunications;Meituan;Sun Yat-sen University;Microsoft Research", "aff_unique_dep": "School of Computer Science;;;Research", "aff_unique_url": "http://www.bupt.edu.cn/;https://www.meituan.com;http://www.sysu.edu.cn/;https://www.microsoft.com/en-us/research/group/asia", "aff_unique_abbr": "BUPT;Meituan;SYSU;MSR Asia", "aff_campus_unique_index": "0;0;2;0;2;2", "aff_campus_unique": "Beijing;;Asia", "aff_country_unique_index": "0;0;0;0;0;0;0;0", "aff_country_unique": "China" }, { "id": "2021.acl-long.92", "title": "Comparing Test Sets with Item Response Theory", "track": "main", "status": "Long", "award": false, "abstract": "Recent years have seen numerous NLP datasets introduced to evaluate the performance of fine-tuned models on natural language understanding tasks. Recent results from large pretrained models, though, show that many of these datasets are largely saturated and unlikely to be able to detect further progress. What kind of datasets are still effective at discriminating among strong models, and what kind of datasets should we expect to be able to detect future improvements? To measure this uniformly across datasets, we draw on Item Response Theory and evaluate 29 datasets using predictions from 18 pretrained Transformer models on individual test examples. We find that Quoref, HellaSwag, and MC-TACO are best suited for distinguishing among state-of-the-art models, while SNLI, MNLI, and CommitmentBank seem to be saturated for current strong models. We also observe span selection task format, which is used for QA datasets like QAMR or SQuAD2.0, is effective in differentiating between strong and weak models.", "author": "Clara Vania; Phu Mon Htut; William Huang; Dhara Mungra; Richard Yuanzhe Pang; Jason Phang; Haokun Liu; Kyunghyun Cho; Samuel R. Bowman", "authorids": "/c/clara-vania/; /p/phu-mon-htut/; /w/william-huang/; /d/dhara-mungra/; /r/richard-yuanzhe-pang/; /j/jason-phang/; /h/haokun-liu/; /k/kyunghyun-cho/; /s/samuel-bowman/", "bibtex": "@inproceedings{vania-etal-2021-comparing,\n title = \"Comparing Test Sets with Item Response Theory\",\n author = \"Vania, Clara and\n Htut, Phu Mon and\n Huang, William and\n Mungra, Dhara and\n Pang, Richard Yuanzhe and\n Phang, Jason and\n Liu, Haokun and\n Cho, Kyunghyun and\n Bowman, Samuel R.\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.92/\",\n doi = \"10.18653/v1/2021.acl-long.92\",\n pages = \"1141--1158\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.92.pdf", "site": "https://aclanthology.org/2021.acl-long.92/", "pdf_size": 4456956, "gs_citation": 45, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=6347900644485055885&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 7, "aff": "Amazon; New York University; New York University; New York University; New York University; New York University; Allen Institute for AI; New York University; New York University", "aff_domain": "amazon.co.uk; ; ; ; ; ; ;nyu.edu; ", "email": "amazon.co.uk; ; ; ; ; ; ;nyu.edu; ", "github": "https://github.com/nyu-mll/nlu-test-sets", "project": "", "author_num": 9, "aff_unique_index": "0;1;1;1;1;1;2;1;1", "aff_unique_norm": "Amazon.com, Inc.;New York University;Allen Institute for AI", "aff_unique_dep": ";;", "aff_unique_url": "https://www.amazon.com;https://www.nyu.edu;https://allenai.org", "aff_unique_abbr": "Amazon;NYU;AI2", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0;0;0;0;0;0;0;0;0", "aff_country_unique": "United States" }, { "id": "2021.acl-long.234", "title": "Competence-based Multimodal Curriculum Learning for Medical Report Generation", "track": "main", "status": "Long", "award": false, "abstract": "Medical report generation task, which targets to produce long and coherent descriptions of medical images, has attracted growing research interests recently. Different from the general image captioning tasks, medical report generation is more challenging for data-driven neural models. This is mainly due to 1) the serious data bias and 2) the limited medical data. To alleviate the data bias and make best use of available data, we propose a Competence-based Multimodal Curriculum Learning framework (CMCL). Specifically, CMCL simulates the learning process of radiologists and optimizes the model in a step by step manner. Firstly, CMCL estimates the difficulty of each training instance and evaluates the competence of current model; Secondly, CMCL selects the most suitable batch of training instances considering current model competence. By iterating above two steps, CMCL can gradually improve the model\u2019s performance. The experiments on the public IU-Xray and MIMIC-CXR datasets show that CMCL can be incorporated into existing models to improve their performance.", "author": "Fenglin Liu; Shen Ge; Xian Wu", "authorids": "/f/fenglin-liu/; /s/shen-ge/; /x/xian-wu/", "bibtex": "@inproceedings{liu-etal-2021-competence,\n title = \"Competence-based Multimodal Curriculum Learning for Medical Report Generation\",\n author = \"Liu, Fenglin and\n Ge, Shen and\n Wu, Xian\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.234/\",\n doi = \"10.18653/v1/2021.acl-long.234\",\n pages = \"3001--3012\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.234.pdf", "site": "https://aclanthology.org/2021.acl-long.234/", "pdf_size": 904511, "gs_citation": 204, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=4719914519747899110&as_sdt=40005&sciodt=0,10&hl=en", "gs_version_total": 6, "aff": "ADSPLAB, School of ECE, Peking University; Tencent Medical AI Lab, Beijing, China; Tencent Medical AI Lab, Beijing, China", "aff_domain": "pku.edu.cn;tencent.com;tencent.com", "email": "pku.edu.cn;tencent.com;tencent.com", "github": "", "project": "", "author_num": 3, "aff_unique_index": "0;1;1", "aff_unique_norm": "Peking University;Tencent", "aff_unique_dep": "School of ECE;Tencent Medical AI Lab", "aff_unique_url": "http://www.pku.edu.cn;https://www.tencent.com", "aff_unique_abbr": "PKU;Tencent", "aff_campus_unique_index": "1;1", "aff_campus_unique": ";Beijing", "aff_country_unique_index": "0;0;0", "aff_country_unique": "China" }, { "id": "2021.acl-long.75", "title": "Compositional Generalization and Natural Language Variation: Can a Semantic Parsing Approach Handle Both?", "track": "main", "status": "Long", "award": false, "abstract": "Sequence-to-sequence models excel at handling natural language variation, but have been shown to struggle with out-of-distribution compositional generalization. This has motivated new specialized architectures with stronger compositional biases, but most of these approaches have only been evaluated on synthetically-generated datasets, which are not representative of natural language variation. In this work we ask: can we develop a semantic parsing approach that handles both natural language variation and compositional generalization? To better assess this capability, we propose new train and test splits of non-synthetic datasets. We demonstrate that strong existing approaches do not perform well across a broad set of evaluations. We also propose NQG-T5, a hybrid model that combines a high-precision grammar-based approach with a pre-trained sequence-to-sequence model. It outperforms existing approaches across several compositional generalization challenges on non-synthetic data, while also being competitive with the state-of-the-art on standard evaluations. While still far from solving this problem, our study highlights the importance of diverse evaluations and the open challenge of handling both compositional generalization and natural language variation in semantic parsing.", "author": "Peter Shaw; Ming-Wei Chang; Panupong Pasupat; Kristina Toutanova", "authorids": "/p/peter-shaw/; /m/ming-wei-chang/; /p/panupong-pasupat/; /k/kristina-toutanova/", "bibtex": "@inproceedings{shaw-etal-2021-compositional,\n title = \"Compositional Generalization and Natural Language Variation: Can a Semantic Parsing Approach Handle Both?\",\n author = \"Shaw, Peter and\n Chang, Ming-Wei and\n Pasupat, Panupong and\n Toutanova, Kristina\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.75/\",\n doi = \"10.18653/v1/2021.acl-long.75\",\n pages = \"922--938\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.75.pdf", "site": "https://aclanthology.org/2021.acl-long.75/", "pdf_size": 501674, "gs_citation": 201, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=13240644332083046622&as_sdt=5,33&sciodt=0,33&hl=en", "gs_version_total": 5, "aff": "Google Research; Google Research; Google Research; Google Research", "aff_domain": "google.com;google.com;google.com;google.com", "email": "google.com;google.com;google.com;google.com", "github": "", "project": "", "author_num": 4, "aff_unique_index": "0;0;0;0", "aff_unique_norm": "Google", "aff_unique_dep": "Google Research", "aff_unique_url": "https://research.google", "aff_unique_abbr": "Google Research", "aff_campus_unique_index": "0;0;0;0", "aff_campus_unique": "Mountain View", "aff_country_unique_index": "0;0;0;0", "aff_country_unique": "United States" }, { "id": "2021.acl-long.193", "title": "Comprehensive Study: How the Context Information of Different Granularity Affects Dialogue State Tracking?", "track": "main", "status": "Long", "award": false, "abstract": "Dialogue state tracking (DST) plays a key role in task-oriented dialogue systems to monitor the user\u2019s goal. In general, there are two strategies to track a dialogue state: predicting it from scratch and updating it from previous state. The scratch-based strategy obtains each slot value by inquiring all the dialogue history, and the previous-based strategy relies on the current turn dialogue to update the previous dialogue state. However, it is hard for the scratch-based strategy to correctly track short-dependency dialogue state because of noise; meanwhile, the previous-based strategy is not very useful for long-dependency dialogue state tracking. Obviously, it plays different roles for the context information of different granularity to track different kinds of dialogue states. Thus, in this paper, we will study and discuss how the context information of different granularity affects dialogue state tracking. First, we explore how greatly different granularities affect dialogue state tracking. Then, we further discuss how to combine multiple granularities for dialogue state tracking. Finally, we apply the findings about context granularity to few-shot learning scenario. Besides, we have publicly released all codes.", "author": "Puhai Yang; Heyan Huang; Xian-Ling Mao", "authorids": "/p/puhai-yang/; /h/he-yan-huang/; /x/xian-ling-mao/", "bibtex": "@inproceedings{yang-etal-2021-comprehensive,\n title = \"Comprehensive Study: How the Context Information of Different Granularity Affects Dialogue State Tracking?\",\n author = \"Yang, Puhai and\n Huang, Heyan and\n Mao, Xian-Ling\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.193/\",\n doi = \"10.18653/v1/2021.acl-long.193\",\n pages = \"2481--2491\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.193.pdf", "site": "https://aclanthology.org/2021.acl-long.193/", "pdf_size": 757986, "gs_citation": 13, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=4582870629708804964&as_sdt=5,44&sciodt=0,44&hl=en", "gs_version_total": 6, "aff": "School of Computer Science and Technology, Beijing Institute of Technology, Beijing, China; Beijing Engineering Research Center of High Volume Language Information Processing and Cloud Computing Applications, Beijing, China; Southeast Academy of Information Technology, Beijing Institute of Technology, Fujian, China", "aff_domain": "bit.edu.cn;bit.edu.cn;bit.edu.cn", "email": "bit.edu.cn;bit.edu.cn;bit.edu.cn", "github": "", "project": "", "author_num": 3, "aff_unique_index": "0;1;0", "aff_unique_norm": "Beijing Institute of Technology;Beijing Engineering Research Center of High Volume Language Information Processing and Cloud Computing Applications", "aff_unique_dep": "School of Computer Science and Technology;", "aff_unique_url": "http://www.bit.edu.cn;", "aff_unique_abbr": "BIT;", "aff_campus_unique_index": "0", "aff_campus_unique": "Beijing;", "aff_country_unique_index": "0;0;0", "aff_country_unique": "China" }, { "id": "2021.acl-long.393", "title": "ConSERT: A Contrastive Framework for Self-Supervised Sentence Representation Transfer", "track": "main", "status": "Long", "award": false, "abstract": "Learning high-quality sentence representations benefits a wide range of natural language processing tasks. Though BERT-based pre-trained language models achieve high performance on many downstream tasks, the native derived sentence representations are proved to be collapsed and thus produce a poor performance on the semantic textual similarity (STS) tasks. In this paper, we present ConSERT, a Contrastive Framework for Self-Supervised SEntence Representation Transfer, that adopts contrastive learning to fine-tune BERT in an unsupervised and effective way. By making use of unlabeled texts, ConSERT solves the collapse issue of BERT-derived sentence representations and make them more applicable for downstream tasks. Experiments on STS datasets demonstrate that ConSERT achieves an 8% relative improvement over the previous state-of-the-art, even comparable to the supervised SBERT-NLI. And when further incorporating NLI supervision, we achieve new state-of-the-art performance on STS tasks. Moreover, ConSERT obtains comparable results with only 1000 samples available, showing its robustness in data scarcity scenarios.", "author": "Yuanmeng Yan; Rumei Li; Sirui Wang; Fuzheng Zhang; Wei Wu; Weiran Xu", "authorids": "/y/yuanmeng-yan/; /r/rumei-li/; /s/sirui-wang/; /f/fuzheng-zhang/; /w/wei-wu/; /w/weiran-xu/", "bibtex": "@inproceedings{yan-etal-2021-consert,\n title = \"{C}on{SERT}: A Contrastive Framework for Self-Supervised Sentence Representation Transfer\",\n author = \"Yan, Yuanmeng and\n Li, Rumei and\n Wang, Sirui and\n Zhang, Fuzheng and\n Wu, Wei and\n Xu, Weiran\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.393/\",\n doi = \"10.18653/v1/2021.acl-long.393\",\n pages = \"5065--5075\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.393.pdf", "site": "https://aclanthology.org/2021.acl-long.393/", "pdf_size": 671447, "gs_citation": 634, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=18136843005178032984&as_sdt=5,44&sciodt=0,44&hl=en", "gs_version_total": 5, "aff": "Beijing University of Posts and Telecommunications; Meituan Inc. + Beijing University of Posts and Telecommunications; Meituan Inc.; Meituan Inc.; Meituan Inc.; Beijing University of Posts and Telecommunications", "aff_domain": "bupt.edu.cn;meituan.com;meituan.com;meituan.com;meituan.com;bupt.edu.cn", "email": "bupt.edu.cn;meituan.com;meituan.com;meituan.com;meituan.com;bupt.edu.cn", "github": "", "project": "", "author_num": 6, "aff_unique_index": "0;1+0;1;1;1;0", "aff_unique_norm": "Beijing University of Posts and Telecommunications;Meituan Inc.", "aff_unique_dep": ";", "aff_unique_url": "http://www.bupt.edu.cn/;https://www.meituan.com", "aff_unique_abbr": "BUPT;Meituan", "aff_campus_unique_index": "0;0;0", "aff_campus_unique": "Beijing;", "aff_country_unique_index": "0;0+0;0;0;0;0", "aff_country_unique": "China" }, { "id": "2021.acl-long.388", "title": "Concept-Based Label Embedding via Dynamic Routing for Hierarchical Text Classification", "track": "main", "status": "Long", "award": false, "abstract": "Hierarchical Text Classification (HTC) is a challenging task that categorizes a textual description within a taxonomic hierarchy. Most of the existing methods focus on modeling the text. Recently, researchers attempt to model the class representations with some resources (e.g., external dictionaries). However, the concept shared among classes which is a kind of domain-specific and fine-grained information has been ignored in previous work. In this paper, we propose a novel concept-based label embedding method that can explicitly represent the concept and model the sharing mechanism among classes for the hierarchical text classification. Experimental results on two widely used datasets prove that the proposed model outperforms several state-of-the-art methods. We release our complementary resources (concepts and definitions of classes) for these two datasets to benefit the research on HTC.", "author": "Xuepeng Wang; Li Zhao; Bing Liu; Tao Chen; Feng Zhang; Di Wang", "authorids": "/x/xuepeng-wang/; /l/li-zhao/; /b/bing-liu/; /t/tao-chen/; /f/feng-zhang/; /d/di-wang/", "bibtex": "@inproceedings{wang-etal-2021-concept,\n title = \"Concept-Based Label Embedding via Dynamic Routing for Hierarchical Text Classification\",\n author = \"Wang, Xuepeng and\n Zhao, Li and\n Liu, Bing and\n Chen, Tao and\n Zhang, Feng and\n Wang, Di\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.388/\",\n doi = \"10.18653/v1/2021.acl-long.388\",\n pages = \"5010--5019\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.388.pdf", "site": "https://aclanthology.org/2021.acl-long.388/", "pdf_size": 1063267, "gs_citation": 22, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=3861234262134016167&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 2, "aff": "Tencent AI Platform Department, China; Tencent AI Platform Department, China; Tencent AI Platform Department, China; Tencent AI Platform Department, China; Tencent AI Platform Department, China; Tencent AI Platform Department, China", "aff_domain": "tencent.com;tencent.com;tencent.com;tencent.com;tencent.com;tencent.com", "email": "tencent.com;tencent.com;tencent.com;tencent.com;tencent.com;tencent.com", "github": "", "project": "", "author_num": 6, "aff_unique_index": "0;0;0;0;0;0", "aff_unique_norm": "Tencent", "aff_unique_dep": "AI Platform Department", "aff_unique_url": "https://www.tencent.com", "aff_unique_abbr": "Tencent", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0;0;0;0;0;0", "aff_country_unique": "China" }, { "id": "2021.acl-long.555", "title": "Conditional Generation of Temporally-ordered Event Sequences", "track": "main", "status": "Long", "award": false, "abstract": "Models of narrative schema knowledge have proven useful for a range of event-related tasks, but they typically do not capture the temporal relationships between events. We propose a single model that addresses both temporal ordering, sorting given events into the order they occurred, and event infilling, predicting new events which fit into an existing temporally-ordered sequence. We use a BART-based conditional generation model that can capture both temporality and common event co-occurrence, meaning it can be flexibly applied to different tasks in this space. Our model is trained as a denoising autoencoder: we take temporally-ordered event sequences, shuffle them, delete some events, and then attempt to recover the original event sequence. This task teaches the model to make inferences given incomplete knowledge about the events in an underlying scenario. On the temporal ordering task, we show that our model is able to unscramble event sequences from existing datasets without access to explicitly labeled temporal training data, outperforming both a BERT-based pairwise model and a BERT-based pointer network. On event infilling, human evaluation shows that our model is able to generate events that fit better temporally into the input events when compared to GPT-2 story completion models.", "author": "Shih-Ting Lin; Nathanael Chambers; Greg Durrett", "authorids": "/s/shih-ting-lin/; /n/nathanael-chambers/; /g/greg-durrett/", "bibtex": "@inproceedings{lin-etal-2021-conditional,\n title = \"Conditional Generation of Temporally-ordered Event Sequences\",\n author = \"Lin, Shih-Ting and\n Chambers, Nathanael and\n Durrett, Greg\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.555/\",\n doi = \"10.18653/v1/2021.acl-long.555\",\n pages = \"7142--7157\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.555.pdf", "site": "https://aclanthology.org/2021.acl-long.555/", "pdf_size": 746644, "gs_citation": 25, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=3832237821643551963&as_sdt=5,33&sciodt=0,33&hl=en", "gs_version_total": 4, "aff": "The University of Texas at Austin\u2660; United States Naval Academy\u2666; The University of Texas at Austin\u2660", "aff_domain": "cs.utexas.edu;usna.edu;cs.utexas.edu", "email": "cs.utexas.edu;usna.edu;cs.utexas.edu", "github": "", "project": "", "author_num": 3, "aff_unique_index": "0;1;0", "aff_unique_norm": "The University of Texas at Austin;United States Naval Academy", "aff_unique_dep": ";", "aff_unique_url": "https://www.utexas.edu;https://www.usna.edu", "aff_unique_abbr": "UT Austin;USNA", "aff_campus_unique_index": "0;0", "aff_campus_unique": "Austin;", "aff_country_unique_index": "0;0;0", "aff_country_unique": "United States" }, { "id": "2021.acl-long.264", "title": "Consistency Regularization for Cross-Lingual Fine-Tuning", "track": "main", "status": "Long", "award": false, "abstract": "Fine-tuning pre-trained cross-lingual language models can transfer task-specific supervision from one language to the others. In this work, we propose to improve cross-lingual fine-tuning with consistency regularization. Specifically, we use example consistency regularization to penalize the prediction sensitivity to four types of data augmentations, i.e., subword sampling, Gaussian noise, code-switch substitution, and machine translation. In addition, we employ model consistency to regularize the models trained with two augmented versions of the same training set. Experimental results on the XTREME benchmark show that our method significantly improves cross-lingual fine-tuning across various tasks, including text classification, question answering, and sequence labeling.", "author": "Bo Zheng; Li Dong; Shaohan Huang; Wenhui Wang; Zewen Chi; Saksham Singhal; Wanxiang Che; Ting Liu; Xia Song; Furu Wei", "authorids": "/b/bo-zheng/; /l/li-dong/; /s/shaohan-huang/; /w/wenhui-wang/; /z/zewen-chi/; /s/saksham-singhal/; /w/wanxiang-che/; /t/ting-liu/; /x/xia-song/; /f/furu-wei/", "bibtex": "@inproceedings{zheng-etal-2021-consistency,\n title = \"Consistency Regularization for Cross-Lingual Fine-Tuning\",\n author = \"Zheng, Bo and\n Dong, Li and\n Huang, Shaohan and\n Wang, Wenhui and\n Chi, Zewen and\n Singhal, Saksham and\n Che, Wanxiang and\n Liu, Ting and\n Song, Xia and\n Wei, Furu\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.264/\",\n doi = \"10.18653/v1/2021.acl-long.264\",\n pages = \"3403--3417\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.264.pdf", "site": "https://aclanthology.org/2021.acl-long.264/", "pdf_size": 988132, "gs_citation": 50, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=15777129613907103728&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 6, "aff": "Harbin Institute of Technology\u2020; Microsoft Corporation\u2021; Microsoft Corporation\u2021; Microsoft Corporation\u2021; Microsoft Corporation\u2021; Microsoft Corporation\u2021; Harbin Institute of Technology\u2020; Harbin Institute of Technology\u2020; Microsoft Corporation\u2021; Microsoft Corporation\u2021", "aff_domain": "ir.hit.edu.cn;microsoft.com;microsoft.com;microsoft.com;microsoft.com;microsoft.com;ir.hit.edu.cn;ir.hit.edu.cn;microsoft.com;microsoft.com", "email": "ir.hit.edu.cn;microsoft.com;microsoft.com;microsoft.com;microsoft.com;microsoft.com;ir.hit.edu.cn;ir.hit.edu.cn;microsoft.com;microsoft.com", "github": "https://github.com/bozheng-hit/xTune", "project": "", "author_num": 10, "aff_unique_index": "0;1;1;1;1;1;0;0;1;1", "aff_unique_norm": "Harbin Institute of Technology;Microsoft Corporation", "aff_unique_dep": ";", "aff_unique_url": "http://www.hit.edu.cn/;https://www.microsoft.com", "aff_unique_abbr": "HIT;Microsoft", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0;1;1;1;1;1;0;0;1;1", "aff_country_unique": "China;United States" }, { "id": "2021.acl-short.113", "title": "Constructing Multi-Modal Dialogue Dataset by Replacing Text with Semantically Relevant Images", "track": "main", "status": "Short", "award": false, "abstract": "In multi-modal dialogue systems, it is important to allow the use of images as part of a multi-turn conversation. Training such dialogue systems generally requires a large-scale dataset consisting of multi-turn dialogues that involve images, but such datasets rarely exist. In response, this paper proposes a 45k multi-modal dialogue dataset created with minimal human intervention. Our method to create such a dataset consists of (1) preparing and pre-processing text dialogue datasets, (2) creating image-mixed dialogues by using a text-to-image replacement technique, and (3) employing a contextual-similarity-based filtering step to ensure the contextual coherence of the dataset. To evaluate the validity of our dataset, we devise a simple retrieval model for dialogue sentence prediction tasks. Automatic metrics and human evaluation results on such tasks show that our dataset can be effectively used as training data for multi-modal dialogue systems which require an understanding of images and text in a context-aware manner. Our dataset and generation code is available at https://github.com/shh1574/multi-modal-dialogue-dataset.", "author": "Nyoungwoo Lee; Suwon Shin; Jaegul Choo; Ho-Jin Choi; Sung-Hyon Myaeng", "authorids": "/n/nyoungwoo-lee/; /s/suwon-shin/; /j/jaegul-choo/; /h/ho-jin-choi/; /s/sung-hyon-myaeng/", "bibtex": "@inproceedings{lee-etal-2021-constructing,\n title = \"Constructing Multi-Modal Dialogue Dataset by Replacing Text with Semantically Relevant Images\",\n author = \"Lee, Nyoungwoo and\n Shin, Suwon and\n Choo, Jaegul and\n Choi, Ho-Jin and\n Myaeng, Sung-Hyon\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-short.113/\",\n doi = \"10.18653/v1/2021.acl-short.113\",\n pages = \"897--906\"\n}", "pdf": "https://aclanthology.org/2021.acl-short.113.pdf", "site": "https://aclanthology.org/2021.acl-short.113/", "pdf_size": 1175068, "gs_citation": 26, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=9881761600008386763&as_sdt=5,33&sciodt=0,33&hl=en", "gs_version_total": 7, "aff": "KAIST, Daejeon, South Korea; KAIST, Daejeon, South Korea; KAIST, Daejeon, South Korea; KAIST, Daejeon, South Korea; KAIST, Daejeon, South Korea", "aff_domain": "kaist.ac.kr;kaist.ac.kr;kaist.ac.kr;kaist.ac.kr;kaist.ac.kr", "email": "kaist.ac.kr;kaist.ac.kr;kaist.ac.kr;kaist.ac.kr;kaist.ac.kr", "github": "https://github.com/shh1574/multi-modal-dialogue-dataset", "project": "", "author_num": 5, "aff_unique_index": "0;0;0;0;0", "aff_unique_norm": "Korea Advanced Institute of Science and Technology", "aff_unique_dep": "", "aff_unique_url": "https://www.kaist.ac.kr", "aff_unique_abbr": "KAIST", "aff_campus_unique_index": "0;0;0;0;0", "aff_campus_unique": "Daejeon", "aff_country_unique_index": "0;0;0;0;0", "aff_country_unique": "South Korea" }, { "id": "2021.acl-short.66", "title": "Continual Learning for Task-oriented Dialogue System with Iterative Network Pruning, Expanding and Masking", "track": "main", "status": "Short", "award": false, "abstract": "This ability to learn consecutive tasks without forgetting how to perform previously trained problems is essential for developing an online dialogue system. This paper proposes an effective continual learning method for the task-oriented dialogue system with iterative network pruning, expanding, and masking (TPEM), which preserves performance on previously encountered tasks while accelerating learning progress on subsequent tasks. Specifically, TPEM (i) leverages network pruning to keep the knowledge for old tasks, (ii) adopts network expanding to create free weights for new tasks, and (iii) introduces task-specific network masking to alleviate the negative impact of fixed weights of old tasks on new tasks. We conduct extensive experiments on seven different tasks from three benchmark datasets and show empirically that TPEM leads to significantly improved results over the strong competitors.", "author": "Binzong Geng; Fajie Yuan; Qiancheng Xu; Ying Shen; Ruifeng Xu; Min Yang", "authorids": "/b/binzong-geng/; /f/fajie-yuan/; /q/qiancheng-xu/; /y/ying-shen/; /r/ruifeng-xu/; /m/min-yang/", "bibtex": "@inproceedings{geng-etal-2021-continual,\n title = \"Continual Learning for Task-oriented Dialogue System with Iterative Network Pruning, Expanding and Masking\",\n author = \"Geng, Binzong and\n Yuan, Fajie and\n Xu, Qiancheng and\n Shen, Ying and\n Xu, Ruifeng and\n Yang, Min\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-short.66/\",\n doi = \"10.18653/v1/2021.acl-short.66\",\n pages = \"517--523\"\n}", "pdf": "https://aclanthology.org/2021.acl-short.66.pdf", "site": "https://aclanthology.org/2021.acl-short.66/", "pdf_size": 425838, "gs_citation": 36, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=65427776271725136&as_sdt=400005&sciodt=0,14&hl=en", "gs_version_total": 4, "aff": "University of Science and Technology of China+Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences; Westlake University; Georgia Institute of Technology; Sun Yat-sen University; Harbin Institute of Technology (Shenzhen); Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences", "aff_domain": "siat.ac.cn;westlake.edu.cn;gatech.edu;mail.sysu.edu.cn;hit.edu.cn;siat.ac.cn", "email": "siat.ac.cn;westlake.edu.cn;gatech.edu;mail.sysu.edu.cn;hit.edu.cn;siat.ac.cn", "github": "https://github.com/siat-nlp/TPEM", "project": "", "author_num": 6, "aff_unique_index": "0+1;2;3;4;5;1", "aff_unique_norm": "University of Science and Technology of China;Shenzhen Institute of Advanced Technology;Westlake University;Georgia Institute of Technology;Sun Yat-sen University;Harbin Institute of Technology", "aff_unique_dep": ";;;;;", "aff_unique_url": "http://www.ustc.edu.cn;http://www.siat.cas.cn;https://www.westlake.edu.cn;https://www.gatech.edu;http://www.sysu.edu.cn/;http://en.hhit.edu.cn/", "aff_unique_abbr": "USTC;SIAT;WU;Georgia Tech;SYSU;HIT", "aff_campus_unique_index": "1;1;1", "aff_campus_unique": ";Shenzhen", "aff_country_unique_index": "0+0;0;1;0;0;0", "aff_country_unique": "China;United States" }, { "id": "2021.acl-short.25", "title": "Continual Quality Estimation with Online Bayesian Meta-Learning", "track": "main", "status": "Short", "award": false, "abstract": "Most current quality estimation (QE) models for machine translation are trained and evaluated in a static setting where training and test data are assumed to be from a fixed distribution. However, in real-life settings, the test data that a deployed QE model would be exposed to may differ from its training data. In particular, training samples are often labelled by one or a small set of annotators, whose perceptions of translation quality and needs may differ substantially from those of end-users, who will employ predictions in practice. To address this challenge, we propose an online Bayesian meta-learning framework for the continuous training of QE models that is able to adapt them to the needs of different users, while being robust to distributional shifts in training and test data. Experiments on data with varying number of users and language characteristics validate the effectiveness of the proposed approach.", "author": "Abiola Obamuyide; Marina Fomicheva; Lucia Specia", "authorids": "/a/abiola-obamuyide/; /m/marina-fomicheva/; /l/lucia-specia/", "bibtex": "@inproceedings{obamuyide-etal-2021-continual,\n title = \"Continual Quality Estimation with Online {B}ayesian Meta-Learning\",\n author = \"Obamuyide, Abiola and\n Fomicheva, Marina and\n Specia, Lucia\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-short.25/\",\n doi = \"10.18653/v1/2021.acl-short.25\",\n pages = \"190--197\"\n}", "pdf": "https://aclanthology.org/2021.acl-short.25.pdf", "site": "https://aclanthology.org/2021.acl-short.25/", "pdf_size": 358965, "gs_citation": 0, "gs_cited_by_link": "https://scholar.google.com/scholar?q=related:2eUCXd-awVgJ:scholar.google.com/&scioq=Continual+Quality+Estimation+with+Online+Bayesian+Meta-Learning&hl=en&as_sdt=0,47", "gs_version_total": 2, "aff": "Department of Computer Science, University of Sheffield + Department of Computing, Imperial College London; Department of Computer Science, University of Sheffield + Department of Computing, Imperial College London; Department of Computer Science, University of Sheffield + Department of Computing, Imperial College London", "aff_domain": "sheffield.ac.uk;sheffield.ac.uk;sheffield.ac.uk", "email": "sheffield.ac.uk;sheffield.ac.uk;sheffield.ac.uk", "github": "", "project": "", "author_num": 3, "aff_unique_index": "0+1;0+1;0+1", "aff_unique_norm": "University of Sheffield;Imperial College London", "aff_unique_dep": "Department of Computer Science;Department of Computing", "aff_unique_url": "https://www.sheffield.ac.uk;https://www.imperial.ac.uk", "aff_unique_abbr": "Sheffield;Imperial", "aff_campus_unique_index": "1;1;1", "aff_campus_unique": ";London", "aff_country_unique_index": "0+0;0+0;0+0", "aff_country_unique": "United Kingdom" }, { "id": "2021.acl-long.355", "title": "Continuous Language Generative Flow", "track": "main", "status": "Long", "award": false, "abstract": "Recent years have witnessed various types of generative models for natural language generation (NLG), especially RNNs or transformer based sequence-to-sequence models, as well as variational autoencoder (VAE) and generative adversarial network (GAN) based models. However, flow-based generative models, which achieve strong performance in image generation due to their invertibility and exact density estimation properties, have been less explored for NLG. In this paper, we propose a flow-based language generation model by adapting previous flow generative models to language generation via continuous input embeddings, adapted affine coupling structures, and a novel architecture for autoregressive text generation. We also apply our framework to Sequence-to-Sequence generation, including text- and video-based Question Generation (QG) and Neural Machine Translation (NMT), and data augmentation for Question Answering (QA). We use our language flow model to provide extra input features for QG and NMT, which achieves improvements over the strong QG baselines on SQuAD and TVQA and NMT baseline on WMT16. We also augment QA data with new context by injecting noise to the latent features of the language flow and show this augmentation leads to a large performance improvement from strong baselines on SQuAD and TVQA.", "author": "Zineng Tang; Shiyue Zhang; Hyounghun Kim; Mohit Bansal", "authorids": "/z/zineng-tang/; /s/shiyue-zhang/; /h/hyounghun-kim/; /m/mohit-bansal/", "bibtex": "@inproceedings{tang-etal-2021-continuous,\n title = \"Continuous Language Generative Flow\",\n author = \"Tang, Zineng and\n Zhang, Shiyue and\n Kim, Hyounghun and\n Bansal, Mohit\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.355/\",\n doi = \"10.18653/v1/2021.acl-long.355\",\n pages = \"4609--4622\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.355.pdf", "site": "https://aclanthology.org/2021.acl-long.355/", "pdf_size": 509717, "gs_citation": 8, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=10371250527865715505&as_sdt=5,44&sciodt=0,44&hl=en", "gs_version_total": 2, "aff": "UNC Chapel Hill; UNC Chapel Hill; UNC Chapel Hill; UNC Chapel Hill", "aff_domain": "cs.unc.edu;cs.unc.edu;cs.unc.edu;cs.unc.edu", "email": "cs.unc.edu;cs.unc.edu;cs.unc.edu;cs.unc.edu", "github": "https://github.com/zinengtang/ContinuousFlowNLG", "project": "", "author_num": 4, "aff_unique_index": "0;0;0;0", "aff_unique_norm": "University of North Carolina at Chapel Hill", "aff_unique_dep": "", "aff_unique_url": "https://www.unc.edu", "aff_unique_abbr": "UNC", "aff_campus_unique_index": "0;0;0;0", "aff_campus_unique": "Chapel Hill", "aff_country_unique_index": "0;0;0;0", "aff_country_unique": "United States" }, { "id": "2021.acl-long.21", "title": "Contrastive Learning for Many-to-many Multilingual Neural Machine Translation", "track": "main", "status": "Long", "award": false, "abstract": "Existing multilingual machine translation approaches mainly focus on English-centric directions, while the non-English directions still lag behind. In this work, we aim to build a many-to-many translation system with an emphasis on the quality of non-English language directions. Our intuition is based on the hypothesis that a universal cross-language representation leads to better multilingual translation performance. To this end, we propose mRASP2, a training method to obtain a single unified multilingual translation model. mRASP2 is empowered by two techniques: a) a contrastive learning scheme to close the gap among representations of different languages, and b) data augmentation on both multiple parallel and monolingual data to further align token representations. For English-centric directions, mRASP2 achieves competitive or even better performance than a strong pre-trained model mBART on tens of WMT benchmarks. For non-English directions, mRASP2 achieves an improvement of average 10+ BLEU compared with the multilingual baseline", "author": "Xiao Pan; Mingxuan Wang; Liwei Wu; Lei Li", "authorids": "/x/xiao-pan/; /m/mingxuan-wang/; /l/liwei-wu/; /l/lei-li/", "bibtex": "@inproceedings{pan-etal-2021-contrastive,\n title = \"Contrastive Learning for Many-to-many Multilingual Neural Machine Translation\",\n author = \"Pan, Xiao and\n Wang, Mingxuan and\n Wu, Liwei and\n Li, Lei\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.21/\",\n doi = \"10.18653/v1/2021.acl-long.21\",\n pages = \"244--258\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.21.pdf", "site": "https://aclanthology.org/2021.acl-long.21/", "pdf_size": 1115257, "gs_citation": 205, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=17611686364916190861&as_sdt=5,44&sciodt=0,44&hl=en", "gs_version_total": 11, "aff": "ByteDance AI Lab; ByteDance AI Lab; ByteDance AI Lab; ByteDance AI Lab", "aff_domain": "bytedance.com;bytedance.com;bytedance.com;bytedance.com", "email": "bytedance.com;bytedance.com;bytedance.com;bytedance.com", "github": "https://github.com/PANXiao1994/mRASP2", "project": "", "author_num": 4, "aff_unique_index": "0;0;0;0", "aff_unique_norm": "ByteDance", "aff_unique_dep": "AI Lab", "aff_unique_url": "https://www.bytedance.com", "aff_unique_abbr": "ByteDance", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0;0;0;0", "aff_country_unique": "China" }, { "id": "2021.acl-long.152", "title": "Contributions of Transformer Attention Heads in Multi- and Cross-lingual Tasks", "track": "main", "status": "Long", "award": false, "abstract": "This paper studies the relative importance of attention heads in Transformer-based models to aid their interpretability in cross-lingual and multi-lingual tasks. Prior research has found that only a few attention heads are important in each mono-lingual Natural Language Processing (NLP) task and pruning the remaining heads leads to comparable or improved performance of the model. However, the impact of pruning attention heads is not yet clear in cross-lingual and multi-lingual tasks. Through extensive experiments, we show that (1) pruning a number of attention heads in a multi-lingual Transformer-based model has, in general, positive effects on its performance in cross-lingual and multi-lingual tasks and (2) the attention heads to be pruned can be ranked using gradients and identified with a few trial experiments. Our experiments focus on sequence labeling tasks, with potential applicability on other cross-lingual and multi-lingual tasks. For comprehensiveness, we examine two pre-trained multi-lingual models, namely multi-lingual BERT (mBERT) and XLM-R, on three tasks across 9 languages each. We also discuss the validity of our findings and their extensibility to truly resource-scarce languages and other task settings.", "author": "Weicheng Ma; Kai Zhang; Renze Lou; Lili Wang; Soroush Vosoughi", "authorids": "/w/weicheng-ma/; /k/kai-zhang/; /r/renze-lou/; /l/lili-wang/; /s/soroush-vosoughi/", "bibtex": "@inproceedings{ma-etal-2021-contributions,\n title = \"Contributions of Transformer Attention Heads in Multi- and Cross-lingual Tasks\",\n author = \"Ma, Weicheng and\n Zhang, Kai and\n Lou, Renze and\n Wang, Lili and\n Vosoughi, Soroush\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.152/\",\n doi = \"10.18653/v1/2021.acl-long.152\",\n pages = \"1956--1966\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.152.pdf", "site": "https://aclanthology.org/2021.acl-long.152/", "pdf_size": 471223, "gs_citation": 16, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=16665631549622331518&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 3, "aff": "Department of Computer Science, Dartmouth College; Department of Computer Science and Technology, Tsinghua University; Department of Computer Science, Zhejiang University City College; Department of Computer Science, Dartmouth College; Department of Computer Science, Dartmouth College", "aff_domain": "dartmouth.edu;gmail.com;gmail.com; ;dartmouth.edu", "email": "dartmouth.edu;gmail.com;gmail.com; ;dartmouth.edu", "github": "", "project": "", "author_num": 5, "aff_unique_index": "0;1;2;0;0", "aff_unique_norm": "Dartmouth College;Tsinghua University;Zhejiang University City College", "aff_unique_dep": "Department of Computer Science;Department of Computer Science and Technology;Department of Computer Science", "aff_unique_url": "https://dartmouth.edu;https://www.tsinghua.edu.cn;", "aff_unique_abbr": "Dartmouth;THU;", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0;1;1;0;0", "aff_country_unique": "United States;China" }, { "id": "2021.acl-long.157", "title": "Control Image Captioning Spatially and Temporally", "track": "main", "status": "Long", "award": false, "abstract": "Generating image captions with user intention is an emerging need. The recently published Localized Narratives dataset takes mouse traces as another input to the image captioning task, which is an intuitive and efficient way for a user to control what to describe in the image. However, how to effectively employ traces to improve generation quality and controllability is still under exploration. This paper aims to solve this problem by proposing a novel model called LoopCAG, which connects Contrastive constraints and Attention Guidance in a Loop manner, engaged explicit spatial and temporal constraints to the generating process. Precisely, each generated sentence is temporally aligned to the corresponding trace sequence through a contrastive learning strategy. Besides, each generated text token is supervised to attend to the correct visual objects under heuristic spatial attention guidance. Comprehensive experimental results demonstrate that our LoopCAG model learns better correspondence among the three modalities (vision, language, and traces) and achieves SOTA performance on trace-controlled image captioning task. Moreover, the controllability and explainability of LoopCAG are validated by analyzing spatial and temporal sensitivity during the generation process.", "author": "Kun Yan; Lei Ji; Huaishao Luo; Ming Zhou; Nan Duan; Shuai Ma", "authorids": "/k/kun-yan/; /l/lei-ji/; /h/huaishao-luo/; /m/ming-zhou/; /n/nan-duan/; /s/shuai-ma/", "bibtex": "@inproceedings{yan-etal-2021-control,\n title = \"Control Image Captioning Spatially and Temporally\",\n author = \"Yan, Kun and\n Ji, Lei and\n Luo, Huaishao and\n Zhou, Ming and\n Duan, Nan and\n Ma, Shuai\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.157/\",\n doi = \"10.18653/v1/2021.acl-long.157\",\n pages = \"2014--2025\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.157.pdf", "site": "https://aclanthology.org/2021.acl-long.157/", "pdf_size": 5128895, "gs_citation": 28, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=12111425469230929589&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 3, "aff": "SKLSDE Lab, Beihang University, Beijing, China; Institute of Computing Technology, CAS, Beijing, China+University of Chinese Academy of Sciences, Beijing, China+Microsoft Research Asia, Beijing, China; Southwest Jiaotong University, Chengdu, China; Microsoft Research Asia, Beijing, China; Microsoft Research Asia, Beijing, China; SKLSDE Lab, Beihang University, Beijing, China", "aff_domain": "buaa.edu.cn;microsoft.com;gmail.com;microsoft.com;microsoft.com;buaa.edu.cn", "email": "buaa.edu.cn;microsoft.com;gmail.com;microsoft.com;microsoft.com;buaa.edu.cn", "github": "", "project": "", "author_num": 6, "aff_unique_index": "0;1+2+3;4;3;3;0", "aff_unique_norm": "Beihang University;Chinese Academy of Sciences;University of Chinese Academy of Sciences;Microsoft Research Asia;Southwest Jiaotong University", "aff_unique_dep": "SKLSDE Lab;Institute of Computing Technology;;Research;", "aff_unique_url": "http://www.buaa.edu.cn;http://www.ict.cas.cn;http://www.ucas.ac.cn;https://www.microsoft.com/en-us/research/group/asia;https://www.swjtu.edu.cn", "aff_unique_abbr": ";CAS;UCAS;MSRA;SWJTU", "aff_campus_unique_index": "0;0+0+0;1;0;0;0", "aff_campus_unique": "Beijing;Chengdu", "aff_country_unique_index": "0;0+0+0;0;0;0;0", "aff_country_unique": "China" }, { "id": "2021.acl-long.502", "title": "Controllable Open-ended Question Generation with A New Question Type Ontology", "track": "main", "status": "Long", "award": false, "abstract": "We investigate the less-explored task of generating open-ended questions that are typically answered by multiple sentences. We first define a new question type ontology which differentiates the nuanced nature of questions better than widely used question words. A new dataset with 4,959 questions is labeled based on the new ontology. We then propose a novel question type-aware question generation framework, augmented by a semantic graph representation, to jointly predict question focuses and produce the question. Based on this framework, we further use both exemplars and automatically generated templates to improve controllability and diversity. Experiments on two newly collected large-scale datasets show that our model improves question quality over competitive comparisons based on automatic metrics. Human judges also rate our model outputs highly in answerability, coverage of scope, and overall quality. Finally, our model variants with templates can produce questions with enhanced controllability and diversity.", "author": "Shuyang Cao; Lu Wang", "authorids": "/s/shuyang-cao/; /l/lu-wang/", "bibtex": "@inproceedings{cao-wang-2021-controllable,\n title = \"Controllable Open-ended Question Generation with A New Question Type Ontology\",\n author = \"Cao, Shuyang and\n Wang, Lu\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.502/\",\n doi = \"10.18653/v1/2021.acl-long.502\",\n pages = \"6424--6439\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.502.pdf", "site": "https://aclanthology.org/2021.acl-long.502/", "pdf_size": 586421, "gs_citation": 56, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=773265134841452161&as_sdt=5,44&sciodt=0,44&hl=en", "gs_version_total": 5, "aff": "Computer Science and Engineering, University of Michigan; Computer Science and Engineering, University of Michigan", "aff_domain": "umich.edu;umich.edu", "email": "umich.edu;umich.edu", "github": "", "project": "", "author_num": 2, "aff_unique_index": "0;0", "aff_unique_norm": "University of Michigan", "aff_unique_dep": "Computer Science and Engineering", "aff_unique_url": "https://www.umich.edu", "aff_unique_abbr": "UM", "aff_campus_unique_index": "0;0", "aff_campus_unique": "Ann Arbor", "aff_country_unique_index": "0;0", "aff_country_unique": "United States" }, { "id": "2021.acl-long.460", "title": "Controversy and Conformity: from Generalized to Personalized Aggressiveness Detection", "track": "main", "status": "Long", "award": false, "abstract": "There is content such as hate speech, offensive, toxic or aggressive documents, which are perceived differently by their consumers. They are commonly identified using classifiers solely based on textual content that generalize pre-agreed meanings of difficult problems. Such models provide the same results for each user, which leads to high misclassification rate observable especially for contentious, aggressive documents. Both document controversy and user nonconformity require new solutions. Therefore, we propose novel personalized approaches that respect individual beliefs expressed by either user conformity-based measures or various embeddings of their previous text annotations. We found that only a few annotations of most controversial documents are enough for all our personalization methods to significantly outperform classic, generalized solutions. The more controversial the content, the greater the gain. The personalized solutions may be used to efficiently filter unwanted aggressive content in the way adjusted to a given person.", "author": "Kamil Kanclerz; Alicja Figas; Marcin Gruza; Tomasz Kajdanowicz; Jan Kocon; Daria Puchalska; Przemyslaw Kazienko", "authorids": "/k/kamil-kanclerz/; /a/alicja-figas/; /m/marcin-gruza/; /t/tomasz-kajdanowicz/; /j/jan-kocon/; /d/daria-puchalska/; /p/przemyslaw-kazienko/", "bibtex": "@inproceedings{kanclerz-etal-2021-controversy,\n title = \"Controversy and Conformity: from Generalized to Personalized Aggressiveness Detection\",\n author = \"Kanclerz, Kamil and\n Figas, Alicja and\n Gruza, Marcin and\n Kajdanowicz, Tomasz and\n Kocon, Jan and\n Puchalska, Daria and\n Kazienko, Przemyslaw\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.460/\",\n doi = \"10.18653/v1/2021.acl-long.460\",\n pages = \"5915--5926\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.460.pdf", "site": "https://aclanthology.org/2021.acl-long.460/", "pdf_size": 946679, "gs_citation": 39, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=9808557350129794440&as_sdt=5,33&sciodt=0,33&hl=en", "gs_version_total": 5, "aff": "Wroc\u0142aw University of Science and Technology; Wroc\u0142aw University of Science and Technology; Wroc\u0142aw University of Science and Technology; Wroc\u0142aw University of Science and Technology; Wroc\u0142aw University of Science and Technology; Wroc\u0142aw University of Science and Technology; Wroc\u0142aw University of Science and Technology", "aff_domain": "pwr.edu.pl;student.pwr.edu.pl;pwr.edu.pl;pwr.edu.pl;pwr.edu.pl;student.pwr.edu.pl;pwr.edu.pl", "email": "pwr.edu.pl;student.pwr.edu.pl;pwr.edu.pl;pwr.edu.pl;pwr.edu.pl;student.pwr.edu.pl;pwr.edu.pl", "github": "", "project": "", "author_num": 7, "aff_unique_index": "0;0;0;0;0;0;0", "aff_unique_norm": "Wroc\u0142aw University of Science and Technology", "aff_unique_dep": "", "aff_unique_url": "https://www.pwr.edu.pl", "aff_unique_abbr": "WUST", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0;0;0;0;0;0;0", "aff_country_unique": "Poland" }, { "id": "2021.acl-long.11", "title": "Conversations Are Not Flat: Modeling the Dynamic Information Flow across Dialogue Utterances", "track": "main", "status": "Long", "award": false, "abstract": "Nowadays, open-domain dialogue models can generate acceptable responses according to the historical context based on the large-scale pre-trained language models. However, they generally concatenate the dialogue history directly as the model input to predict the response, which we named as the flat pattern and ignores the dynamic information flow across dialogue utterances. In this work, we propose the DialoFlow model, in which we introduce a dynamic flow mechanism to model the context flow, and design three training objectives to capture the information dynamics across dialogue utterances by addressing the semantic influence brought about by each utterance in large-scale pre-training. Experiments on the multi-reference Reddit Dataset and DailyDialog Dataset demonstrate that our DialoFlow significantly outperforms the DialoGPT on the dialogue generation task. Besides, we propose the Flow score, an effective automatic metric for evaluating interactive human-bot conversation quality based on the pre-trained DialoFlow, which presents high chatbot-level correlation (r=0.9) with human ratings among 11 chatbots. Code and pre-trained models will be public.", "author": "Zekang Li; Jinchao Zhang; Zhengcong Fei; Yang Feng; Jie Zhou", "authorids": "/z/zekang-li/; /j/jinchao-zhang/; /z/zhengcong-fei/; /y/yang-feng/; /j/jie-zhou/", "bibtex": "@inproceedings{li-etal-2021-conversations,\n title = \"Conversations Are Not Flat: Modeling the Dynamic Information Flow across Dialogue Utterances\",\n author = \"Li, Zekang and\n Zhang, Jinchao and\n Fei, Zhengcong and\n Feng, Yang and\n Zhou, Jie\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.11/\",\n doi = \"10.18653/v1/2021.acl-long.11\",\n pages = \"128--138\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.11.pdf", "site": "https://aclanthology.org/2021.acl-long.11/", "pdf_size": 1886294, "gs_citation": 66, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=549010311526011579&as_sdt=5,33&sciodt=0,33&hl=en", "gs_version_total": 4, "aff": "Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences (ICT/CAS)+University of Chinese Academy of Sciences; Pattern Recognition Center, WeChat AI, Tencent Inc, China; Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences (ICT/CAS)+University of Chinese Academy of Sciences; Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences (ICT/CAS)+University of Chinese Academy of Sciences+Pattern Recognition Center, WeChat AI, Tencent Inc, China; Pattern Recognition Center, WeChat AI, Tencent Inc, China", "aff_domain": "ict.ac.cn;tencent.com;ict.ac.cn;ict.ac.cn;tencent.com", "email": "ict.ac.cn;tencent.com;ict.ac.cn;ict.ac.cn;tencent.com", "github": "https://github.com/ictnlp/DialoFlow", "project": "", "author_num": 5, "aff_unique_index": "0+1;2;0+1;0+1+2;2", "aff_unique_norm": "Chinese Academy of Sciences;University of Chinese Academy of Sciences;Tencent Inc", "aff_unique_dep": "Institute of Computing Technology;;Pattern Recognition Center, WeChat AI", "aff_unique_url": "http://www.cas.cn;http://www.ucas.ac.cn;https://www.tencent.com", "aff_unique_abbr": "CAS;UCAS;Tencent", "aff_campus_unique_index": ";;", "aff_campus_unique": "", "aff_country_unique_index": "0+0;0;0+0;0+0+0;0", "aff_country_unique": "China" }, { "id": "2021.acl-long.535", "title": "ConvoSumm: Conversation Summarization Benchmark and Improved Abstractive Summarization with Argument Mining", "track": "main", "status": "Long", "award": false, "abstract": "While online conversations can cover a vast amount of information in many different formats, abstractive text summarization has primarily focused on modeling solely news articles. This research gap is due, in part, to the lack of standardized datasets for summarizing online discussions. To address this gap, we design annotation protocols motivated by an issues\u2013viewpoints\u2013assertions framework to crowdsource four new datasets on diverse online conversation forms of news comments, discussion forums, community question answering forums, and email threads. We benchmark state-of-the-art models on our datasets and analyze characteristics associated with the data. To create a comprehensive benchmark, we also evaluate these models on widely-used conversation summarization datasets to establish strong baselines in this domain. Furthermore, we incorporate argument mining through graph construction to directly model the issues, viewpoints, and assertions present in a conversation and filter noisy input, showing comparable or improved results according to automatic and human evaluations.", "author": "Alexander Fabbri; Faiaz Rahman; Imad Rizvi; Borui Wang; Haoran Li; Yashar Mehdad; Dragomir Radev", "authorids": "/a/alexander-richard-fabbri/; /f/faiaz-rahman/; /i/imad-rizvi/; /b/borui-wang/; /h/haoran-li/; /y/yashar-mehdad/; /d/dragomir-radev/", "bibtex": "@inproceedings{fabbri-etal-2021-convosumm,\n title = \"{C}onvo{S}umm: Conversation Summarization Benchmark and Improved Abstractive Summarization with Argument Mining\",\n author = \"Fabbri, Alexander and\n Rahman, Faiaz and\n Rizvi, Imad and\n Wang, Borui and\n Li, Haoran and\n Mehdad, Yashar and\n Radev, Dragomir\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.535/\",\n doi = \"10.18653/v1/2021.acl-long.535\",\n pages = \"6866--6880\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.535.pdf", "site": "https://aclanthology.org/2021.acl-long.535/", "pdf_size": 558404, "gs_citation": 74, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=10906187942396721567&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 7, "aff": "Yale University; Yale University; Yale University; Yale University; Facebook AI; Facebook AI; Yale University", "aff_domain": "yale.edu;yale.edu;yale.edu;yale.edu;fb.com;fb.com;yale.edu", "email": "yale.edu;yale.edu;yale.edu;yale.edu;fb.com;fb.com;yale.edu", "github": "", "project": "", "author_num": 7, "aff_unique_index": "0;0;0;0;1;1;0", "aff_unique_norm": "Yale University;Facebook", "aff_unique_dep": ";Facebook AI", "aff_unique_url": "https://www.yale.edu;https://www.facebook.com", "aff_unique_abbr": "Yale;Facebook AI", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0;0;0;0;0;0;0", "aff_country_unique": "United States" }, { "id": "2021.acl-long.333", "title": "Convolutions and Self-Attention: Re-interpreting Relative Positions in Pre-trained Language Models", "track": "main", "status": "Long", "award": false, "abstract": "In this paper, we detail the relationship between convolutions and self-attention in natural language tasks. We show that relative position embeddings in self-attention layers are equivalent to recently-proposed dynamic lightweight convolutions, and we consider multiple new ways of integrating convolutions into Transformer self-attention. Specifically, we propose composite attention, which unites previous relative position encoding methods under a convolutional framework. We conduct experiments by training BERT with composite attention, finding that convolutions consistently improve performance on multiple downstream tasks, replacing absolute position embeddings. To inform future work, we present results comparing lightweight convolutions, dynamic convolutions, and depthwise-separable convolutions in language model pre-training, considering multiple injection points for convolutions in self-attention layers.", "author": "Tyler Chang; Yifan Xu; Weijian Xu; Zhuowen Tu", "authorids": "/t/tyler-chang/; /y/yifan-xu/; /w/weijian-xu/; /z/zhuowen-tu/", "bibtex": "@inproceedings{chang-etal-2021-convolutions,\n title = \"Convolutions and Self-Attention: {R}e-interpreting Relative Positions in Pre-trained Language Models\",\n author = \"Chang, Tyler and\n Xu, Yifan and\n Xu, Weijian and\n Tu, Zhuowen\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.333/\",\n doi = \"10.18653/v1/2021.acl-long.333\",\n pages = \"4322--4333\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.333.pdf", "site": "https://aclanthology.org/2021.acl-long.333/", "pdf_size": 466910, "gs_citation": 16, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=6513351230677003923&as_sdt=8000005&sciodt=0,19&hl=en", "gs_version_total": 6, "aff": "University of California San Diego + Hal\u0131c\u0131o\u011flu Data Science Institute; University of California San Diego; University of California San Diego; University of California San Diego", "aff_domain": "ucsd.edu;ucsd.edu;ucsd.edu;ucsd.edu", "email": "ucsd.edu;ucsd.edu;ucsd.edu;ucsd.edu", "github": "", "project": "", "author_num": 4, "aff_unique_index": "0+0;0;0;0", "aff_unique_norm": "University of California, San Diego", "aff_unique_dep": "", "aff_unique_url": "https://ucsd.edu", "aff_unique_abbr": "UCSD", "aff_campus_unique_index": "0+1;0;0;0", "aff_campus_unique": "San Diego;La Jolla", "aff_country_unique_index": "0+0;0;0;0", "aff_country_unique": "United States" }, { "id": "2021.acl-long.448", "title": "Coreference Reasoning in Machine Reading Comprehension", "track": "main", "status": "Long", "award": false, "abstract": "Coreference resolution is essential for natural language understanding and has been long studied in NLP. In recent years, as the format of Question Answering (QA) became a standard for machine reading comprehension (MRC), there have been data collection efforts, e.g., Dasigi et al. (2019), that attempt to evaluate the ability of MRC models to reason about coreference. However, as we show, coreference reasoning in MRC is a greater challenge than earlier thought; MRC datasets do not reflect the natural distribution and, consequently, the challenges of coreference reasoning. Specifically, success on these datasets does not reflect a model\u2019s proficiency in coreference reasoning. We propose a methodology for creating MRC datasets that better reflect the challenges of coreference reasoning and use it to create a sample evaluation set. The results on our dataset show that state-of-the-art models still struggle with these phenomena. Furthermore, we develop an effective way to use naturally occurring coreference phenomena from existing coreference resolution datasets when training MRC models. This allows us to show an improvement in the coreference reasoning abilities of state-of-the-art models.", "author": "Mingzhu Wu; Nafise Sadat Moosavi; Dan Roth; Iryna Gurevych", "authorids": "/m/mingzhu-wu/; /n/nafise-sadat-moosavi/; /d/dan-roth/; /i/iryna-gurevych/", "bibtex": "@inproceedings{wu-etal-2021-coreference,\n title = \"Coreference Reasoning in Machine Reading Comprehension\",\n author = \"Wu, Mingzhu and\n Moosavi, Nafise Sadat and\n Roth, Dan and\n Gurevych, Iryna\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.448/\",\n doi = \"10.18653/v1/2021.acl-long.448\",\n pages = \"5768--5781\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.448.pdf", "site": "https://aclanthology.org/2021.acl-long.448/", "pdf_size": 461728, "gs_citation": 7, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=10793539347464256287&as_sdt=5,44&sciodt=0,44&hl=en", "gs_version_total": 6, "aff": "UKP Lab, Technische Universitat Darmstadt; UKP Lab, Technische Universitat Darmstadt; Department of Computer and Information Science, UPenn; UKP Lab, Technische Universitat Darmstadt", "aff_domain": "; ; ; ", "email": "; ; ; ", "github": "https://github.com/UKPLab/coref-reasoning-in-qa", "project": "", "author_num": 4, "aff_unique_index": "0;0;1;0", "aff_unique_norm": "Technische Universitat Darmstadt;University of Pennsylvania", "aff_unique_dep": "UKP Lab;Department of Computer and Information Science", "aff_unique_url": "https://www.tu-darmstadt.de;https://www.upenn.edu", "aff_unique_abbr": "TU Darmstadt;UPenn", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0;0;1;0", "aff_country_unique": "Germany;United States" }, { "id": "2021.acl-short.3", "title": "Coreference Resolution without Span Representations", "track": "main", "status": "Short", "award": false, "abstract": "The introduction of pretrained language models has reduced many complex task-specific NLP models to simple lightweight layers. An exception to this trend is coreference resolution, where a sophisticated task-specific model is appended to a pretrained transformer encoder. While highly effective, the model has a very large memory footprint \u2013 primarily due to dynamically-constructed span and span-pair representations \u2013 which hinders the processing of complete documents and the ability to train on multiple instances in a single batch. We introduce a lightweight end-to-end coreference model that removes the dependency on span representations, handcrafted features, and heuristics. Our model performs competitively with the current standard model, while being simpler and more efficient.", "author": "Yuval Kirstain; Ori Ram; Omer Levy", "authorids": "/y/yuval-kirstain/; /o/ori-ram/; /o/omer-levy/", "bibtex": "@inproceedings{kirstain-etal-2021-coreference,\n title = \"Coreference Resolution without Span Representations\",\n author = \"Kirstain, Yuval and\n Ram, Ori and\n Levy, Omer\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-short.3/\",\n doi = \"10.18653/v1/2021.acl-short.3\",\n pages = \"14--19\"\n}", "pdf": "https://aclanthology.org/2021.acl-short.3.pdf", "site": "https://aclanthology.org/2021.acl-short.3/", "pdf_size": 288753, "gs_citation": 97, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=14596182734272819973&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 6, "aff": "Blavatnik School of Computer Science, Tel Aviv University; Blavatnik School of Computer Science, Tel Aviv University; Blavatnik School of Computer Science, Tel Aviv University", "aff_domain": "cs.tau.ac.il;cs.tau.ac.il;cs.tau.ac.il", "email": "cs.tau.ac.il;cs.tau.ac.il;cs.tau.ac.il", "github": "https://github.com/yuvalkirstain/s2e-coref", "project": "", "author_num": 3, "aff_unique_index": "0;0;0", "aff_unique_norm": "Tel Aviv University", "aff_unique_dep": "Blavatnik School of Computer Science", "aff_unique_url": "https://www.tau.ac.il", "aff_unique_abbr": "TAU", "aff_campus_unique_index": "0;0;0", "aff_campus_unique": "Tel Aviv", "aff_country_unique_index": "0;0;0", "aff_country_unique": "Israel" }, { "id": "2021.acl-long.422", "title": "Counterfactual Inference for Text Classification Debiasing", "track": "main", "status": "Long", "award": false, "abstract": "Today\u2019s text classifiers inevitably suffer from unintended dataset biases, especially the document-level label bias and word-level keyword bias, which may hurt models\u2019 generalization. Many previous studies employed data-level manipulations or model-level balancing mechanisms to recover unbiased distributions and thus prevent models from capturing the two types of biases. Unfortunately, they either suffer from the extra cost of data collection/selection/annotation or need an elaborate design of balancing strategies. Different from traditional factual inference in which debiasing occurs before or during training, counterfactual inference mitigates the influence brought by unintended confounders after training, which can make unbiased decisions with biased observations. Inspired by this, we propose a model-agnostic text classification debiasing framework \u2013 Corsair, which can effectively avoid employing data manipulations or designing balancing mechanisms. Concretely, Corsair first trains a base model on a training set directly, allowing the dataset biases \u2018poison\u2019 the trained model. In inference, given a factual input document, Corsair imagines its two counterfactual counterparts to distill and mitigate the two biases captured by the poisonous model. Extensive experiments demonstrate Corsair\u2019s effectiveness, generalizability and fairness.", "author": "Chen Qian; Fuli Feng; Lijie Wen; Chunping Ma; Pengjun Xie", "authorids": "/c/chen-qian/; /f/fuli-feng/; /l/lijie-wen/; /c/chunping-ma/; /p/pengjun-xie/", "bibtex": "@inproceedings{qian-etal-2021-counterfactual,\n title = \"Counterfactual Inference for Text Classification Debiasing\",\n author = \"Qian, Chen and\n Feng, Fuli and\n Wen, Lijie and\n Ma, Chunping and\n Xie, Pengjun\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.422/\",\n doi = \"10.18653/v1/2021.acl-long.422\",\n pages = \"5434--5445\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.422.pdf", "site": "https://aclanthology.org/2021.acl-long.422/", "pdf_size": 701580, "gs_citation": 98, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=17646939387474385597&as_sdt=40005&sciodt=0,10&hl=en", "gs_version_total": 2, "aff": "Tsinghua University; National University of Singapore + Tsinghua University; Tsinghua University; Alibaba DAMO Academy; Alibaba DAMO Academy", "aff_domain": "mails.tsinghua.edu.cn;gmail.com;tsinghua.edu.cn;alibaba-inc.com;taobao.com", "email": "mails.tsinghua.edu.cn;gmail.com;tsinghua.edu.cn;alibaba-inc.com;taobao.com", "github": "https://github.com/qianc62/Corsair", "project": "", "author_num": 5, "aff_unique_index": "0;1+0;0;2;2", "aff_unique_norm": "Tsinghua University;National University of Singapore;Alibaba Group", "aff_unique_dep": ";;DAMO Academy", "aff_unique_url": "https://www.tsinghua.edu.cn;https://www.nus.edu.sg;https://www.alibaba-group.com", "aff_unique_abbr": "THU;NUS;Alibaba DAMO", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0;1+0;0;0;0", "aff_country_unique": "China;Singapore" }, { "id": "2021.acl-short.7", "title": "Counterfactuals to Control Latent Disentangled Text Representations for Style Transfer", "track": "main", "status": "Short", "award": false, "abstract": "Disentanglement of latent representations into content and style spaces has been a commonly employed method for unsupervised text style transfer. These techniques aim to learn the disentangled representations and tweak them to modify the style of a sentence. In this paper, we propose a counterfactual-based method to modify the latent representation, by posing a \u2018what-if\u2019 scenario. This simple and disciplined approach also enables a fine-grained control on the transfer strength. We conduct experiments with the proposed methodology on multiple attribute transfer tasks like Sentiment, Formality and Excitement to support our hypothesis.", "author": "Sharmila Reddy Nangi; Niyati Chhaya; Sopan Khosla; Nikhil Kaushik; Harshit Nyati", "authorids": "/s/sharmila-reddy-nangi/; /n/niyati-chhaya/; /s/sopan-khosla/; /n/nikhil-kaushik/; /h/harshit-nyati/", "bibtex": "@inproceedings{nangi-etal-2021-counterfactuals,\n title = \"Counterfactuals to Control Latent Disentangled Text Representations for Style Transfer\",\n author = \"Nangi, Sharmila Reddy and\n Chhaya, Niyati and\n Khosla, Sopan and\n Kaushik, Nikhil and\n Nyati, Harshit\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-short.7/\",\n doi = \"10.18653/v1/2021.acl-short.7\",\n pages = \"40--48\"\n}", "pdf": "https://aclanthology.org/2021.acl-short.7.pdf", "site": "https://aclanthology.org/2021.acl-short.7/", "pdf_size": 1144140, "gs_citation": 24, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=9169850539224777636&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 2, "aff": "Adobe Research, India; Adobe Research, India; Carnegie Mellon University, USA; Cohesity Storage Solutions, India; Adobe Systems, India", "aff_domain": "adobe.com;adobe.com;andrew.cmu.edu;cohesity.com;adobe.com", "email": "adobe.com;adobe.com;andrew.cmu.edu;cohesity.com;adobe.com", "github": "", "project": "", "author_num": 5, "aff_unique_index": "0;0;1;2;3", "aff_unique_norm": "Adobe Research;Carnegie Mellon University;Cohesity Storage Solutions;Adobe Systems", "aff_unique_dep": ";;;", "aff_unique_url": "https://research.adobe.com;https://www.cmu.edu;;https://www.adobe.com/in/", "aff_unique_abbr": "Adobe;CMU;;Adobe", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0;0;1;0;0", "aff_country_unique": "India;United States" }, { "id": "2021.acl-long.153", "title": "Crafting Adversarial Examples for Neural Machine Translation", "track": "main", "status": "Long", "award": false, "abstract": "Effective adversary generation for neural machine translation (NMT) is a crucial prerequisite for building robust machine translation systems. In this work, we investigate veritable evaluations of NMT adversarial attacks, and propose a novel method to craft NMT adversarial examples. We first show the current NMT adversarial attacks may be improperly estimated by the commonly used mono-directional translation, and we propose to leverage the round-trip translation technique to build valid metrics for evaluating NMT adversarial attacks. Our intuition is that an effective NMT adversarial example, which imposes minor shifting on the source and degrades the translation dramatically, would naturally lead to a semantic-destroyed round-trip translation result. We then propose a promising black-box attack method called Word Saliency speedup Local Search (WSLS) that could effectively attack the mainstream NMT architectures. Comprehensive experiments demonstrate that the proposed metrics could accurately evaluate the attack effectiveness, and the proposed WSLS could significantly break the state-of-art NMT models with small perturbation. Besides, WSLS exhibits strong transferability on attacking Baidu and Bing online translators.", "author": "Xinze Zhang; Junzhe Zhang; Zhenhua Chen; Kun He", "authorids": "/x/xinze-zhang/; /j/junzhe-zhang/; /z/zhenhua-chen/; /k/kun-he/", "bibtex": "@inproceedings{zhang-etal-2021-crafting,\n title = \"Crafting Adversarial Examples for Neural Machine Translation\",\n author = \"Zhang, Xinze and\n Zhang, Junzhe and\n Chen, Zhenhua and\n He, Kun\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.153/\",\n doi = \"10.18653/v1/2021.acl-long.153\",\n pages = \"1967--1977\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.153.pdf", "site": "https://aclanthology.org/2021.acl-long.153/", "pdf_size": 1787476, "gs_citation": 57, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=1724450243396437458&as_sdt=400005&sciodt=0,14&hl=en", "gs_version_total": 2, "aff": "School of Computer Science and Technology; School of Computer Science and Technology; School of Computer Science and Technology; School of Computer Science and Technology + School of Management", "aff_domain": "hust.edu.cn;hust.edu.cn;hust.edu.cn;hust.edu.cn", "email": "hust.edu.cn;hust.edu.cn;hust.edu.cn;hust.edu.cn", "github": "", "project": "", "author_num": 4, "aff_unique_index": "0;0;0;0+1", "aff_unique_norm": "School of Computer Science and Technology;School of Management", "aff_unique_dep": "Computer Science and Technology;Management", "aff_unique_url": ";", "aff_unique_abbr": ";", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "", "aff_country_unique": "" }, { "id": "2021.acl-long.538", "title": "Cross-Lingual Abstractive Summarization with Limited Parallel Resources", "track": "main", "status": "Long", "award": false, "abstract": "Parallel cross-lingual summarization data is scarce, requiring models to better use the limited available cross-lingual resources. Existing methods to do so often adopt sequence-to-sequence networks with multi-task frameworks. Such approaches apply multiple decoders, each of which is utilized for a specific task. However, these independent decoders share no parameters, hence fail to capture the relationships between the discrete phrases of summaries in different languages, breaking the connections in order to transfer the knowledge of the high-resource languages to low-resource languages. To bridge these connections, we propose a novel Multi-Task framework for Cross-Lingual Abstractive Summarization (MCLAS) in a low-resource setting. Employing one unified decoder to generate the sequential concatenation of monolingual and cross-lingual summaries, MCLAS makes the monolingual summarization task a prerequisite of the CLS task. In this way, the shared decoder learns interactions involving alignments and summary patterns across languages, which encourages attaining knowledge transfer. Experiments on two CLS datasets demonstrate that our model significantly outperforms three baseline models in both low-resource and full-dataset scenarios. Moreover, in-depth analysis on the generated summaries and attention heads verifies that interactions are learned well using MCLAS, which benefits the CLS task under limited parallel resources.", "author": "Yu Bai; Yang Gao; Heyan Huang", "authorids": "/y/yu-bai/; /y/yang-gao/; /h/he-yan-huang/", "bibtex": "@inproceedings{bai-etal-2021-cross,\n title = \"Cross-Lingual Abstractive Summarization with Limited Parallel Resources\",\n author = \"Bai, Yu and\n Gao, Yang and\n Huang, Heyan\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.538/\",\n doi = \"10.18653/v1/2021.acl-long.538\",\n pages = \"6910--6924\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.538.pdf", "site": "https://aclanthology.org/2021.acl-long.538/", "pdf_size": 7390612, "gs_citation": 67, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=13271881726305365761&as_sdt=5,44&sciodt=0,44&hl=en", "gs_version_total": 7, "aff": "School of Computer Science and Technology, Beijing Institute of Technology, Beijing, China + Southeast Academy of Information Technology, Fujian, China + Beijing Engineering Research Center of High Volume Language Information Processing and Cloud Computing Applications, Beijing, China; School of Computer Science and Technology, Beijing Institute of Technology, Beijing, China + Southeast Academy of Information Technology, Fujian, China + Beijing Engineering Research Center of High Volume Language Information Processing and Cloud Computing Applications, Beijing, China; School of Computer Science and Technology, Beijing Institute of Technology, Beijing, China + Southeast Academy of Information Technology, Fujian, China + Beijing Engineering Research Center of High Volume Language Information Processing and Cloud Computing Applications, Beijing, China", "aff_domain": "bit.edu.cn;bit.edu.cn;bit.edu.cn", "email": "bit.edu.cn;bit.edu.cn;bit.edu.cn", "github": "", "project": "", "author_num": 3, "aff_unique_index": "0+1+2;0+1+2;0+1+2", "aff_unique_norm": "Beijing Institute of Technology;Southeast Academy of Information Technology;Beijing Engineering Research Center of High Volume Language Information Processing and Cloud Computing Applications", "aff_unique_dep": "School of Computer Science and Technology;;", "aff_unique_url": "http://www.bit.edu.cn;;", "aff_unique_abbr": "BIT;;", "aff_campus_unique_index": "0;0;0", "aff_campus_unique": "Beijing;", "aff_country_unique_index": "0+0+0;0+0+0;0+0+0", "aff_country_unique": "China" }, { "id": "2021.acl-long.300", "title": "Cross-language Sentence Selection via Data Augmentation and Rationale Training", "track": "main", "status": "Long", "award": false, "abstract": "This paper proposes an approach to cross-language sentence selection in a low-resource setting. It uses data augmentation and negative sampling techniques on noisy parallel sentence data to directly learn a cross-lingual embedding-based query relevance model. Results show that this approach performs as well as or better than multiple state-of-the-art machine translation + monolingual retrieval systems trained on the same parallel data. Moreover, when a rationale training secondary objective is applied to encourage the model to match word alignment hints from a phrase-based statistical machine translation model, consistent improvements are seen across three language pairs (English-Somali, English-Swahili and English-Tagalog) over a variety of state-of-the-art baselines.", "author": "Yanda Chen; Chris Kedzie; Suraj Nair; Petra Galuscakova; Rui Zhang; Douglas Oard; Kathleen McKeown", "authorids": "/y/yanda-chen/; /c/chris-kedzie/; /s/suraj-nair/; /p/petra-galuscakova/; /r/rui-zhang/; /d/douglas-w-oard/; /k/kathleen-mckeown/", "bibtex": "@inproceedings{chen-etal-2021-cross-language,\n title = \"Cross-language Sentence Selection via Data Augmentation and Rationale Training\",\n author = \"Chen, Yanda and\n Kedzie, Chris and\n Nair, Suraj and\n Galuscakova, Petra and\n Zhang, Rui and\n Oard, Douglas and\n McKeown, Kathleen\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.300/\",\n doi = \"10.18653/v1/2021.acl-long.300\",\n pages = \"3881--3895\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.300.pdf", "site": "https://aclanthology.org/2021.acl-long.300/", "pdf_size": 459307, "gs_citation": 11, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=5753776373116844649&as_sdt=400005&sciodt=0,14&hl=en", "gs_version_total": 8, "aff": "Columbia University; Columbia University; University of Maryland; University of Maryland; Penn State University; University of Maryland; Columbia University", "aff_domain": "columbia.edu;cs.columbia.edu;cs.columbia.edu;umd.edu;umd.edu;umd.edu;psu.edu", "email": "columbia.edu;cs.columbia.edu;cs.columbia.edu;umd.edu;umd.edu;umd.edu;psu.edu", "github": "", "project": "", "author_num": 7, "aff_unique_index": "0;0;1;1;2;1;0", "aff_unique_norm": "Columbia University;University of Maryland;Penn State University", "aff_unique_dep": ";;", "aff_unique_url": "https://www.columbia.edu;https://www/umd.edu;https://www.psu.edu", "aff_unique_abbr": "Columbia;UMD;PSU", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0;0;0;0;0;0;0", "aff_country_unique": "United States" }, { "id": "2021.acl-short.78", "title": "Cross-lingual Text Classification with Heterogeneous Graph Neural Network", "track": "main", "status": "Short", "award": false, "abstract": "Cross-lingual text classification aims at training a classifier on the source language and transferring the knowledge to target languages, which is very useful for low-resource languages. Recent multilingual pretrained language models (mPLM) achieve impressive results in cross-lingual classification tasks, but rarely consider factors beyond semantic similarity, causing performance degradation between some language pairs. In this paper we propose a simple yet effective method to incorporate heterogeneous information within and across languages for cross-lingual text classification using graph convolutional networks (GCN). In particular, we construct a heterogeneous graph by treating documents and words as nodes, and linking nodes with different relations, which include part-of-speech roles, semantic similarity, and document translations. Extensive experiments show that our graph-based method significantly outperforms state-of-the-art models on all tasks, and also achieves consistent performance gain over baselines in low-resource settings where external tools like translators are unavailable.", "author": "Ziyun Wang; Xuan Liu; Peiji Yang; Shixing Liu; Zhisheng Wang", "authorids": "/z/ziyun-wang/; /x/xuan-liu/; /p/peiji-yang/; /s/shixing-liu/; /z/zhisheng-wang/", "bibtex": "@inproceedings{wang-etal-2021-cross-lingual,\n title = \"Cross-lingual Text Classification with Heterogeneous Graph Neural Network\",\n author = \"Wang, Ziyun and\n Liu, Xuan and\n Yang, Peiji and\n Liu, Shixing and\n Wang, Zhisheng\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-short.78/\",\n doi = \"10.18653/v1/2021.acl-short.78\",\n pages = \"612--620\"\n}", "pdf": "https://aclanthology.org/2021.acl-short.78.pdf", "site": "https://aclanthology.org/2021.acl-short.78/", "pdf_size": 606301, "gs_citation": 36, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=3167981952492621436&as_sdt=5,47&sciodt=0,47&hl=en", "gs_version_total": 4, "aff": "Tencent, Shenzhen, China; Tencent, Shenzhen, China; Tencent, Shenzhen, China; Tencent, Shenzhen, China; Tencent, Shenzhen, China", "aff_domain": "tencent.com;gmail.com;tencent.com;tencent.com;tencent.com", "email": "tencent.com;gmail.com;tencent.com;tencent.com;tencent.com", "github": "", "project": "", "author_num": 5, "aff_unique_index": "0;0;0;0;0", "aff_unique_norm": "Tencent", "aff_unique_dep": "", "aff_unique_url": "https://www.tencent.com", "aff_unique_abbr": "Tencent", "aff_campus_unique_index": "0;0;0;0;0", "aff_campus_unique": "Shenzhen", "aff_country_unique_index": "0;0;0;0;0", "aff_country_unique": "China" }, { "id": "2021.acl-long.459", "title": "Cross-modal Memory Networks for Radiology Report Generation", "track": "main", "status": "Long", "award": false, "abstract": "Medical imaging plays a significant role in clinical practice of medical diagnosis, where the text reports of the images are essential in understanding them and facilitating later treatments. By generating the reports automatically, it is beneficial to help lighten the burden of radiologists and significantly promote clinical automation, which already attracts much attention in applying artificial intelligence to medical domain. Previous studies mainly follow the encoder-decoder paradigm and focus on the aspect of text generation, with few studies considering the importance of cross-modal mappings and explicitly exploit such mappings to facilitate radiology report generation. In this paper, we propose a cross-modal memory networks (CMN) to enhance the encoder-decoder framework for radiology report generation, where a shared memory is designed to record the alignment between images and texts so as to facilitate the interaction and generation across modalities. Experimental results illustrate the effectiveness of our proposed model, where state-of-the-art performance is achieved on two widely used benchmark datasets, i.e., IU X-Ray and MIMIC-CXR. Further analyses also prove that our model is able to better align information from radiology images and texts so as to help generating more accurate reports in terms of clinical indicators.", "author": "Zhihong Chen; Yaling Shen; Yan Song; Xiang Wan", "authorids": "/z/zhihong-chen/; /y/yaling-shen/; /y/yan-song/; /x/xiang-wan/", "bibtex": "@inproceedings{chen-etal-2021-cross-modal,\n title = \"Cross-modal Memory Networks for Radiology Report Generation\",\n author = \"Chen, Zhihong and\n Shen, Yaling and\n Song, Yan and\n Wan, Xiang\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.459/\",\n doi = \"10.18653/v1/2021.acl-long.459\",\n pages = \"5904--5914\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.459.pdf", "site": "https://aclanthology.org/2021.acl-long.459/", "pdf_size": 4087042, "gs_citation": 333, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=694868759187463951&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 4, "aff": "The Chinese University of Hong Kong (Shenzhen)+Shenzhen Research Institute of Big Data; The Chinese University of Hong Kong (Shenzhen); The Chinese University of Hong Kong (Shenzhen)+Shenzhen Research Institute of Big Data; Shenzhen Research Institute of Big Data", "aff_domain": "link.cuhk.edu.cn;link.cuhk.edu.cn;cuhk.edu.cn;sribd.cn", "email": "link.cuhk.edu.cn;link.cuhk.edu.cn;cuhk.edu.cn;sribd.cn", "github": "https://github.com/cuhksz-nlp/R2GenCMN", "project": "", "author_num": 4, "aff_unique_index": "0+1;0;0+1;1", "aff_unique_norm": "The Chinese University of Hong Kong;Shenzhen Research Institute of Big Data", "aff_unique_dep": ";", "aff_unique_url": "https://www.cuhk.edu.cn;http://www.sribd.cn", "aff_unique_abbr": "CUHK;", "aff_campus_unique_index": "0;0;0", "aff_campus_unique": "Shenzhen;", "aff_country_unique_index": "0+0;0;0+0;0", "aff_country_unique": "China" }, { "id": "2021.acl-long.548", "title": "Cross-replication Reliability - An Empirical Approach to Interpreting Inter-rater Reliability", "track": "main", "status": "Long", "award": false, "abstract": "When collecting annotations and labeled data from humans, a standard practice is to use inter-rater reliability (IRR) as a measure of data goodness (Hallgren, 2012). Metrics such as Krippendorff\u2019s alpha or Cohen\u2019s kappa are typically required to be above a threshold of 0.6 (Landis and Koch, 1977). These absolute thresholds are unreasonable for crowdsourced data from annotators with high cultural and training variances, especially on subjective topics. We present a new alternative to interpreting IRR that is more empirical and contextualized. It is based upon benchmarking IRR against baseline measures in a replication, one of which is a novel cross-replication reliability (xRR) measure based on Cohen\u2019s (1960) kappa. We call this approach the xRR framework. We opensource a replication dataset of 4 million human judgements of facial expressions and analyze it with the proposed framework. We argue this framework can be used to measure the quality of crowdsourced datasets.", "author": "Ka Wong; Praveen Paritosh; Lora Aroyo", "authorids": "/k/ka-wong/; /p/praveen-paritosh/; /l/lora-aroyo/", "bibtex": "@inproceedings{wong-etal-2021-cross,\n title = \"Cross-replication Reliability - An Empirical Approach to Interpreting Inter-rater Reliability\",\n author = \"Wong, Ka and\n Paritosh, Praveen and\n Aroyo, Lora\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.548/\",\n doi = \"10.18653/v1/2021.acl-long.548\",\n pages = \"7053--7065\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.548.pdf", "site": "https://aclanthology.org/2021.acl-long.548/", "pdf_size": 752690, "gs_citation": 28, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=17182656630610759785&as_sdt=5,33&sciodt=0,33&hl=en", "gs_version_total": 6, "aff": "Google Research; Google Research; Google Research", "aff_domain": "gmail.com;google.com;gmail.com", "email": "gmail.com;google.com;gmail.com", "github": "", "project": "", "author_num": 3, "aff_unique_index": "0;0;0", "aff_unique_norm": "Google", "aff_unique_dep": "Google Research", "aff_unique_url": "https://research.google", "aff_unique_abbr": "Google Research", "aff_campus_unique_index": "0;0;0", "aff_campus_unique": "Mountain View", "aff_country_unique_index": "0;0;0", "aff_country_unique": "United States" }, { "id": "2021.acl-long.432", "title": "Crowdsourcing Learning as Domain Adaptation: A Case Study on Named Entity Recognition", "track": "main", "status": "Long", "award": false, "abstract": "Crowdsourcing is regarded as one prospective solution for effective supervised learning, aiming to build large-scale annotated training data by crowd workers. Previous studies focus on reducing the influences from the noises of the crowdsourced annotations for supervised models. We take a different point in this work, regarding all crowdsourced annotations as gold-standard with respect to the individual annotators. In this way, we find that crowdsourcing could be highly similar to domain adaptation, and then the recent advances of cross-domain methods can be almost directly applied to crowdsourcing. Here we take named entity recognition (NER) as a study case, suggesting an annotator-aware representation learning model that inspired by the domain adaptation methods which attempt to capture effective domain-aware features. We investigate both unsupervised and supervised crowdsourcing learning, assuming that no or only small-scale expert annotations are available. Experimental results on a benchmark crowdsourced NER dataset show that our method is highly effective, leading to a new state-of-the-art performance. In addition, under the supervised setting, we can achieve impressive performance gains with only a very small scale of expert annotations.", "author": "Xin Zhang; Guangwei Xu; Yueheng Sun; Meishan Zhang; Pengjun Xie", "authorids": "/x/xin-zhang/; /g/guangwei-xu/; /y/yueheng-sun/; /m/meishan-zhang/; /p/pengjun-xie/", "bibtex": "@inproceedings{zhang-etal-2021-crowdsourcing,\n title = \"Crowdsourcing Learning as Domain Adaptation: {A} Case Study on Named Entity Recognition\",\n author = \"Zhang, Xin and\n Xu, Guangwei and\n Sun, Yueheng and\n Zhang, Meishan and\n Xie, Pengjun\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.432/\",\n doi = \"10.18653/v1/2021.acl-long.432\",\n pages = \"5558--5570\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.432.pdf", "site": "https://aclanthology.org/2021.acl-long.432/", "pdf_size": 626952, "gs_citation": 22, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=11076831502633026109&as_sdt=5,33&sciodt=0,33&hl=en", "gs_version_total": 4, "aff": "School of New Media and Communication, Tianjin University, China; School of New Media and Communication, Tianjin University, China; College of Intelligence and Computing, Tianjin University, China; School of New Media and Communication, Tianjin University, China; School of New Media and Communication, Tianjin University, China", "aff_domain": "tju.edu.cn;gmail.com;tju.edu.cn;tju.edu.cn;gmail.com", "email": "tju.edu.cn;gmail.com;tju.edu.cn;tju.edu.cn;gmail.com", "github": "", "project": "", "author_num": 5, "aff_unique_index": "0;0;0;0;0", "aff_unique_norm": "Tianjin University", "aff_unique_dep": "School of New Media and Communication", "aff_unique_url": "http://www.tju.edu.cn", "aff_unique_abbr": "Tianjin University", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0;0;0;0;0", "aff_country_unique": "China" }, { "id": "2021.acl-long.35", "title": "DESCGEN: A Distantly Supervised Datasetfor Generating Entity Descriptions", "track": "main", "status": "Long", "award": false, "abstract": "Short textual descriptions of entities provide summaries of their key attributes and have been shown to be useful sources of background knowledge for tasks such as entity linking and question answering. However, generating entity descriptions, especially for new and long-tail entities, can be challenging since relevant information is often scattered across multiple sources with varied content and style. We introduce DESCGEN: given mentions spread over multiple documents, the goal is to generate an entity summary description. DESCGEN consists of 37K entity descriptions from Wikipedia and Fandom, each paired with nine evidence documents on average. The documents were collected using a combination of entity linking and hyperlinks into the entity pages, which together provide high-quality distant supervision. Compared to other multi-document summarization tasks, our task is entity-centric, more abstractive, and covers a wide range of domains. We also propose a two-stage extract-then-generate baseline and show that there exists a large gap (19.9% in ROUGE-L) between state-of-art models and human performance, suggesting that the data will support significant future work.", "author": "Weijia Shi; Mandar Joshi; Luke Zettlemoyer", "authorids": "/w/weijia-shi/; /m/mandar-joshi/; /l/luke-zettlemoyer/", "bibtex": "@inproceedings{shi-etal-2021-descgen,\n title = \"{DESCGEN}: A Distantly Supervised Datasetfor Generating Entity Descriptions\",\n author = \"Shi, Weijia and\n Joshi, Mandar and\n Zettlemoyer, Luke\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.35/\",\n doi = \"10.18653/v1/2021.acl-long.35\",\n pages = \"415--427\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.35.pdf", "site": "https://aclanthology.org/2021.acl-long.35/", "pdf_size": 519731, "gs_citation": 5, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=13572323522615956328&as_sdt=5,33&sciodt=0,33&hl=en", "gs_version_total": 0, "aff": "Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, WA; Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, WA; Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, WA", "aff_domain": "cs.washington.edu;cs.washington.edu;cs.washington.edu", "email": "cs.washington.edu;cs.washington.edu;cs.washington.edu", "github": "github.com/swj0419/DESCGENDoc", "project": "", "author_num": 3, "aff_unique_index": "0;0;0", "aff_unique_norm": "University of Washington", "aff_unique_dep": "Paul G. Allen School of Computer Science and Engineering", "aff_unique_url": "https://www.washington.edu", "aff_unique_abbr": "UW", "aff_campus_unique_index": "0;0;0", "aff_campus_unique": "Seattle", "aff_country_unique_index": "0;0;0", "aff_country_unique": "United States" }, { "id": "2021.acl-long.522", "title": "DExperts: Decoding-Time Controlled Text Generation with Experts and Anti-Experts", "track": "main", "status": "Long", "award": false, "abstract": "Despite recent advances in natural language generation, it remains challenging to control attributes of generated text. We propose DExperts: Decoding-time Experts, a decoding-time method for controlled text generation that combines a pretrained language model with \u201cexpert\u201d LMs and/or \u201canti-expert\u201d LMs in a product of experts. Intuitively, under the ensemble, tokens only get high probability if they are considered likely by the experts, and unlikely by the anti-experts. We apply DExperts to language detoxification and sentiment-controlled generation, where we outperform existing controllable generation methods on both automatic and human evaluations. Moreover, because DExperts operates only on the output of the pretrained LM, it is effective with (anti-)experts of smaller size, including when operating on GPT-3. Our work highlights the promise of tuning small LMs on text with (un)desirable attributes for efficient decoding-time steering.", "author": "Alisa Liu; Maarten Sap; Ximing Lu; Swabha Swayamdipta; Chandra Bhagavatula; Noah A. Smith; Yejin Choi", "authorids": "/a/alisa-liu/; /m/maarten-sap/; /x/ximing-lu/; /s/swabha-swayamdipta/; /c/chandra-bhagavatula/; /n/noah-a-smith/; /y/yejin-choi/", "bibtex": "@inproceedings{liu-etal-2021-dexperts,\n title = \"{DE}xperts: Decoding-Time Controlled Text Generation with Experts and Anti-Experts\",\n author = \"Liu, Alisa and\n Sap, Maarten and\n Lu, Ximing and\n Swayamdipta, Swabha and\n Bhagavatula, Chandra and\n Smith, Noah A. and\n Choi, Yejin\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.522/\",\n doi = \"10.18653/v1/2021.acl-long.522\",\n pages = \"6691--6706\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.522.pdf", "site": "https://aclanthology.org/2021.acl-long.522/", "pdf_size": 766228, "gs_citation": 376, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=1809705998967314284&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 7, "aff": "Paul G. Allen School of Computer Science & Engineering, University of Washington; Paul G. Allen School of Computer Science & Engineering, University of Washington; Paul G. Allen School of Computer Science & Engineering, University of Washington+Allen Institute for Artificial Intelligence; Allen Institute for Artificial Intelligence; Allen Institute for Artificial Intelligence; Paul G. Allen School of Computer Science & Engineering, University of Washington+Allen Institute for Artificial Intelligence; Paul G. Allen School of Computer Science & Engineering, University of Washington+Allen Institute for Artificial Intelligence", "aff_domain": "cs.washington.edu; ; ; ; ; ; ", "email": "cs.washington.edu; ; ; ; ; ; ", "github": "https://github.com/alisawuffles/DExperts", "project": "", "author_num": 7, "aff_unique_index": "0;0;0+1;1;1;0+1;0+1", "aff_unique_norm": "University of Washington;Allen Institute for Artificial Intelligence", "aff_unique_dep": "Paul G. Allen School of Computer Science & Engineering;", "aff_unique_url": "https://www.washington.edu;https://allenai.org", "aff_unique_abbr": "UW;AI2", "aff_campus_unique_index": "0;0;0;0;0", "aff_campus_unique": "Seattle;", "aff_country_unique_index": "0;0;0+0;0;0;0+0;0+0", "aff_country_unique": "United States" }, { "id": "2021.acl-long.439", "title": "DVD: A Diagnostic Dataset for Multi-step Reasoning in Video Grounded Dialogue", "track": "main", "status": "Long", "award": false, "abstract": "A video-grounded dialogue system is required to understand both dialogue, which contains semantic dependencies from turn to turn, and video, which contains visual cues of spatial and temporal scene variations. Building such dialogue systems is a challenging problem, involving various reasoning types on both visual and language inputs. Existing benchmarks do not have enough annotations to thoroughly analyze dialogue systems and understand their capabilities and limitations in isolation. These benchmarks are also not explicitly designed to minimise biases that models can exploit without actual reasoning. To address these limitations, in this paper, we present DVD, a Diagnostic Dataset for Video-grounded Dialogue. The dataset is designed to contain minimal biases and has detailed annotations for the different types of reasoning over the spatio-temporal space of video. Dialogues are synthesized over multiple question turns, each of which is injected with a set of cross-turn semantic relationships. We use DVD to analyze existing approaches, providing interesting insights into their abilities and limitations. In total, DVD is built from 11k CATER synthetic videos and contains 10 instances of 10-round dialogues for each video, resulting in more than 100k dialogues and 1M question-answer pairs. Our code and dataset are publicly available.", "author": "Hung Le; Chinnadhurai Sankar; Seungwhan Moon; Ahmad Beirami; Alborz Geramifard; Satwik Kottur", "authorids": "/h/hung-le/; /c/chinnadhurai-sankar/; /s/seungwhan-moon/; /a/ahmad-beirami/; /a/alborz-geramifard/; /s/satwik-kottur/", "bibtex": "@inproceedings{le-etal-2021-dvd,\n title = \"{DVD}: A Diagnostic Dataset for Multi-step Reasoning in Video Grounded Dialogue\",\n author = \"Le, Hung and\n Sankar, Chinnadhurai and\n Moon, Seungwhan and\n Beirami, Ahmad and\n Geramifard, Alborz and\n Kottur, Satwik\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.439/\",\n doi = \"10.18653/v1/2021.acl-long.439\",\n pages = \"5651--5665\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.439.pdf", "site": "https://aclanthology.org/2021.acl-long.439/", "pdf_size": 2083930, "gs_citation": 18, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=1736923059634281102&as_sdt=5,47&sciodt=0,47&hl=en", "gs_version_total": 7, "aff": "Singapore Management University+Institute for Infocomm Research, A*STAR; Facebook; Facebook; Facebook; Facebook; Facebook", "aff_domain": "smu.edu.sg;fb.com;fb.com;fb.com;fb.com;fb.com", "email": "smu.edu.sg;fb.com;fb.com;fb.com;fb.com;fb.com", "github": "github.com/facebookresearch/DVDialogues", "project": "", "author_num": 6, "aff_unique_index": "0+1;2;2;2;2;2", "aff_unique_norm": "Singapore Management University;Institute for Infocomm Research;Facebook, Inc.", "aff_unique_dep": ";;", "aff_unique_url": "https://www.smu.edu.sg;https://www.i2r.a-star.edu.sg;https://www.facebook.com", "aff_unique_abbr": "SMU;I2R;FB", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0+0;1;1;1;1;1", "aff_country_unique": "Singapore;United States" }, { "id": "2021.acl-long.501", "title": "DYPLOC: Dynamic Planning of Content Using Mixed Language Models for Text Generation", "track": "main", "status": "Long", "award": false, "abstract": "We study the task of long-form opinion text generation, which faces at least two distinct challenges. First, existing neural generation models fall short of coherence, thus requiring efficient content planning. Second, diverse types of information are needed to guide the generator to cover both subjective and objective content. To this end, we propose DYPLOC, a generation framework that conducts dynamic planning of content while generating the output based on a novel design of mixed language models. To enrich the generation with diverse content, we further propose to use large pre-trained models to predict relevant concepts and to generate claims. We experiment with two challenging tasks on newly collected datasets: (1) argument generation with Reddit ChangeMyView, and (2) writing articles using New York Times\u2019 Opinion section. Automatic evaluation shows that our model significantly outperforms competitive comparisons. Human judges further confirm that our generations are more coherent with richer content.", "author": "Xinyu Hua; Ashwin Sreevatsa; Lu Wang", "authorids": "/x/xinyu-hua/; /a/ashwin-sreevatsa/; /l/lu-wang/", "bibtex": "@inproceedings{hua-etal-2021-dyploc,\n title = \"{DYPLOC}: Dynamic Planning of Content Using Mixed Language Models for Text Generation\",\n author = \"Hua, Xinyu and\n Sreevatsa, Ashwin and\n Wang, Lu\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.501/\",\n doi = \"10.18653/v1/2021.acl-long.501\",\n pages = \"6408--6423\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.501.pdf", "site": "https://aclanthology.org/2021.acl-long.501/", "pdf_size": 1080405, "gs_citation": 31, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=8023901186124554677&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 7, "aff": "Khoury College of Computer Sciences, Northeastern University, Boston, MA; Computer Science and Engineering, University of Michigan, Ann Arbor, MI; Computer Science and Engineering, University of Michigan, Ann Arbor, MI", "aff_domain": "northeastern.edu;umich.edu;umich.edu", "email": "northeastern.edu;umich.edu;umich.edu", "github": "", "project": "", "author_num": 3, "aff_unique_index": "0;1;1", "aff_unique_norm": "Northeastern University;University of Michigan", "aff_unique_dep": "Khoury College of Computer Sciences;Computer Science and Engineering", "aff_unique_url": "https://www.northeastern.edu;https://www.umich.edu", "aff_unique_abbr": "NU;UM", "aff_campus_unique_index": "0;1;1", "aff_campus_unique": "Boston;Ann Arbor", "aff_country_unique_index": "0;0;0", "aff_country_unique": "United States" }, { "id": "2021.acl-long.173", "title": "Data Augmentation for Text Generation Without Any Augmented Data", "track": "main", "status": "Long", "award": false, "abstract": "Data augmentation is an effective way to improve the performance of many neural text generation models. However, current data augmentation methods need to define or choose proper data mapping functions that map the original samples into the augmented samples. In this work, we derive an objective to formulate the problem of data augmentation on text generation tasks without any use of augmented data constructed by specific mapping functions. Our proposed objective can be efficiently optimized and applied to popular loss functions on text generation tasks with a convergence rate guarantee. Experiments on five datasets of two text generation tasks show that our approach can approximate or even surpass popular data augmentation methods.", "author": "Wei Bi; Huayang Li; Jiacheng Huang", "authorids": "/w/wei-bi/; /h/huayang-li/; /j/jiacheng-huang/", "bibtex": "@inproceedings{bi-etal-2021-data,\n title = \"Data Augmentation for Text Generation Without Any Augmented Data\",\n author = \"Bi, Wei and\n Li, Huayang and\n Huang, Jiacheng\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.173/\",\n doi = \"10.18653/v1/2021.acl-long.173\",\n pages = \"2223--2237\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.173.pdf", "site": "https://aclanthology.org/2021.acl-long.173/", "pdf_size": 1022214, "gs_citation": 8, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=18001925304436311831&as_sdt=5,38&sciodt=0,38&hl=en", "gs_version_total": 4, "aff": "Tencent AI Lab, Shenzhen, China; Tencent AI Lab, Shenzhen, China; Tencent AI Lab, Shenzhen, China", "aff_domain": "tencent.com;tencent.com;tencent.com", "email": "tencent.com;tencent.com;tencent.com", "github": "", "project": "", "author_num": 3, "aff_unique_index": "0;0;0", "aff_unique_norm": "Tencent AI Lab", "aff_unique_dep": "AI Lab", "aff_unique_url": "https://ai.tencent.com", "aff_unique_abbr": "Tencent AI Lab", "aff_campus_unique_index": "0;0;0", "aff_campus_unique": "Shenzhen", "aff_country_unique_index": "0;0;0", "aff_country_unique": "China" }, { "id": "2021.acl-long.401", "title": "Data Augmentation with Adversarial Training for Cross-Lingual NLI", "track": "main", "status": "Long", "award": false, "abstract": "Due to recent pretrained multilingual representation models, it has become feasible to exploit labeled data from one language to train a cross-lingual model that can then be applied to multiple new languages. In practice, however, we still face the problem of scarce labeled data, leading to subpar results. In this paper, we propose a novel data augmentation strategy for better cross-lingual natural language inference by enriching the data to reflect more diversity in a semantically faithful way. To this end, we propose two methods of training a generative model to induce synthesized examples, and then leverage the resulting data using an adversarial training regimen for more robustness. In a series of detailed experiments, we show that this fruitful combination leads to substantial gains in cross-lingual inference.", "author": "Xin Dong; Yaxin Zhu; Zuohui Fu; Dongkuan Xu; Gerard de Melo", "authorids": "/x/xin-luna-dong/; /y/yaxin-zhu/; /z/zuohui-fu/; /d/dongkuan-xu/; /g/gerard-de-melo/", "bibtex": "@inproceedings{dong-etal-2021-data,\n title = \"Data Augmentation with Adversarial Training for Cross-Lingual {NLI}\",\n author = \"Dong, Xin and\n Zhu, Yaxin and\n Fu, Zuohui and\n Xu, Dongkuan and\n de Melo, Gerard\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.401/\",\n doi = \"10.18653/v1/2021.acl-long.401\",\n pages = \"5158--5167\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.401.pdf", "site": "https://aclanthology.org/2021.acl-long.401/", "pdf_size": 345170, "gs_citation": 22, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=6410997279717337177&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 4, "aff": "Rutgers University; Rutgers University; Rutgers University; The Pennsylvania State University; Hasso Plattner Institute / University of Potsdam", "aff_domain": "rutgers.edu;rutgers.edu;rutgers.edu;psu.edu;demelo.org", "email": "rutgers.edu;rutgers.edu;rutgers.edu;psu.edu;demelo.org", "github": "", "project": "", "author_num": 5, "aff_unique_index": "0;0;0;1;2", "aff_unique_norm": "Rutgers University;The Pennsylvania State University;Hasso Plattner Institute", "aff_unique_dep": ";;", "aff_unique_url": "https://www.rutgers.edu;https://www.psu.edu;https://www.hpi.de", "aff_unique_abbr": "Rutgers;PSU;HPI", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0;0;0;0;1", "aff_country_unique": "United States;Germany" }, { "id": "2021.acl-long.241", "title": "Database reasoning over text", "track": "main", "status": "Long", "award": false, "abstract": "Neural models have shown impressive performance gains in answering queries from natural language text. However, existing works are unable to support database queries, such as \u201cList/Count all female athletes who were born in 20th century\u201d, which require reasoning over sets of relevant facts with operations such as join, filtering and aggregation. We show that while state-of-the-art transformer models perform very well for small databases, they exhibit limitations in processing noisy data, numerical operations, and queries that aggregate facts. We propose a modular architecture to answer these database-style queries over multiple spans from text and aggregating these at scale. We evaluate the architecture using WikiNLDB, a novel dataset for exploring such queries. Our architecture scales to databases containing thousands of facts whereas contemporary models are limited by how many facts can be encoded. In direct comparison on small databases, our approach increases overall answer accuracy from 85% to 90%. On larger databases, our approach retains its accuracy whereas transformer baselines could not encode the context.", "author": "James Thorne; Majid Yazdani; Marzieh Saeidi; Fabrizio Silvestri; Sebastian Riedel; Alon Halevy", "authorids": "/j/james-thorne/; /m/majid-yazdani/; /m/marzieh-saeidi/; /f/fabrizio-silvestri/; /s/sebastian-riedel/; /a/alon-halevy/", "bibtex": "@inproceedings{thorne-etal-2021-database,\n title = \"Database reasoning over text\",\n author = \"Thorne, James and\n Yazdani, Majid and\n Saeidi, Marzieh and\n Silvestri, Fabrizio and\n Riedel, Sebastian and\n Halevy, Alon\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.241/\",\n doi = \"10.18653/v1/2021.acl-long.241\",\n pages = \"3091--3104\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.241.pdf", "site": "https://aclanthology.org/2021.acl-long.241/", "pdf_size": 1011489, "gs_citation": 41, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=15375409130751083179&as_sdt=5,31&sciodt=0,31&hl=en", "gs_version_total": 11, "aff": "University of Cambridge; Facebook AI; Facebook AI; Sapienza University, Rome + Facebook AI; Facebook AI + University College London; Facebook AI", "aff_domain": "cam.ac.uk;fb.com;fb.com;diag.uniroma1.it;fb.com;fb.com", "email": "cam.ac.uk;fb.com;fb.com;diag.uniroma1.it;fb.com;fb.com", "github": "https://github.com/facebookresearch/NeuralDB", "project": "", "author_num": 6, "aff_unique_index": "0;1;1;2+1;1+3;1", "aff_unique_norm": "University of Cambridge;Facebook;Sapienza University;University College London", "aff_unique_dep": ";Facebook AI;;", "aff_unique_url": "https://www.cam.ac.uk;https://www.facebook.com;https://www.uniroma1.it;https://www.ucl.ac.uk", "aff_unique_abbr": "Cambridge;Facebook AI;Sapienza;UCL", "aff_campus_unique_index": "0;2;", "aff_campus_unique": "Cambridge;;Rome", "aff_country_unique_index": "0;1;1;2+1;1+0;1", "aff_country_unique": "United Kingdom;United States;Italy" }, { "id": "2021.acl-long.430", "title": "De-Confounded Variational Encoder-Decoder for Logical Table-to-Text Generation", "track": "main", "status": "Long", "award": false, "abstract": "Logical table-to-text generation aims to automatically generate fluent and logically faithful text from tables. The task remains challenging where deep learning models often generated linguistically fluent but logically inconsistent text. The underlying reason may be that deep learning models often capture surface-level spurious correlations rather than the causal relationships between the table x and the sentence y. Specifically, in the training stage, a model can get a low empirical loss without understanding x and use spurious statistical cues instead. In this paper, we propose a de-confounded variational encoder-decoder (DCVED) based on causal intervention, learning the objective p(y|do(x)). Firstly, we propose to use variational inference to estimate the confounders in the latent space and cooperate with the causal intervention based on Pearl\u2019s do-calculus to alleviate the spurious correlations. Secondly, to make the latent confounder meaningful, we propose a back-prediction process to predict the not-used entities but linguistically similar to the exactly selected ones. Finally, since our variational model can generate multiple candidates, we train a table-text selector to find out the best candidate sentence for the given table. An extensive set of experiments show that our model outperforms the baselines and achieves new state-of-the-art performance on two logical table-to-text datasets in terms of logical fidelity.", "author": "Wenqing Chen; Jidong Tian; Yitian Li; Hao He; Yaohui Jin", "authorids": "/w/wenqing-chen/; /j/jidong-tian/; /y/yitian-li/; /h/hao-he/; /y/yaohui-jin/", "bibtex": "@inproceedings{chen-etal-2021-de,\n title = \"De-Confounded Variational Encoder-Decoder for Logical Table-to-Text Generation\",\n author = \"Chen, Wenqing and\n Tian, Jidong and\n Li, Yitian and\n He, Hao and\n Jin, Yaohui\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.430/\",\n doi = \"10.18653/v1/2021.acl-long.430\",\n pages = \"5532--5542\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.430.pdf", "site": "https://aclanthology.org/2021.acl-long.430/", "pdf_size": 331428, "gs_citation": 29, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=12922301147552159866&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 4, "aff": "MoE Key Lab of Artificial Intelligence, AI Institute, Shanghai Jiao Tong University+State Key Lab of Advanced Optical Communication System and Network, Shanghai Jiao Tong University; MoE Key Lab of Artificial Intelligence, AI Institute, Shanghai Jiao Tong University+State Key Lab of Advanced Optical Communication System and Network, Shanghai Jiao Tong University; MoE Key Lab of Artificial Intelligence, AI Institute, Shanghai Jiao Tong University+State Key Lab of Advanced Optical Communication System and Network, Shanghai Jiao Tong University; MoE Key Lab of Artificial Intelligence, AI Institute, Shanghai Jiao Tong University+State Key Lab of Advanced Optical Communication System and Network, Shanghai Jiao Tong University; MoE Key Lab of Artificial Intelligence, AI Institute, Shanghai Jiao Tong University+State Key Lab of Advanced Optical Communication System and Network, Shanghai Jiao Tong University", "aff_domain": "sjtu.edu.cn;sjtu.edu.cn;sjtu.edu.cn;sjtu.edu.cn;sjtu.edu.cn", "email": "sjtu.edu.cn;sjtu.edu.cn;sjtu.edu.cn;sjtu.edu.cn;sjtu.edu.cn", "github": "", "project": "", "author_num": 5, "aff_unique_index": "0+0;0+0;0+0;0+0;0+0", "aff_unique_norm": "Shanghai Jiao Tong University", "aff_unique_dep": "AI Institute", "aff_unique_url": "https://www.sjtu.edu.cn", "aff_unique_abbr": "SJTU", "aff_campus_unique_index": "0+0;0+0;0+0;0+0;0+0", "aff_campus_unique": "Shanghai", "aff_country_unique_index": "0+0;0+0;0+0;0+0;0+0", "aff_country_unique": "China" }, { "id": "2021.acl-long.371", "title": "De-biasing Distantly Supervised Named Entity Recognition via Causal Intervention", "track": "main", "status": "Long", "award": false, "abstract": "Distant supervision tackles the data bottleneck in NER by automatically generating training instances via dictionary matching. Unfortunately, the learning of DS-NER is severely dictionary-biased, which suffers from spurious correlations and therefore undermines the effectiveness and the robustness of the learned models. In this paper, we fundamentally explain the dictionary bias via a Structural Causal Model (SCM), categorize the bias into intra-dictionary and inter-dictionary biases, and identify their causes. Based on the SCM, we learn de-biased DS-NER via causal interventions. For intra-dictionary bias, we conduct backdoor adjustment to remove the spurious correlations introduced by the dictionary confounder. For inter-dictionary bias, we propose a causal invariance regularizer which will make DS-NER models more robust to the perturbation of dictionaries. Experiments on four datasets and three DS-NER models show that our method can significantly improve the performance of DS-NER.", "author": "Wenkai Zhang; Hongyu Lin; Xianpei Han; Le Sun", "authorids": "/w/wenkai-zhang/; /h/hongyu-lin/; /x/xianpei-han/; /l/le-sun/", "bibtex": "@inproceedings{zhang-etal-2021-de,\n title = \"De-biasing Distantly Supervised Named Entity Recognition via Causal Intervention\",\n author = \"Zhang, Wenkai and\n Lin, Hongyu and\n Han, Xianpei and\n Sun, Le\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.371/\",\n doi = \"10.18653/v1/2021.acl-long.371\",\n pages = \"4803--4813\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.371.pdf", "site": "https://aclanthology.org/2021.acl-long.371/", "pdf_size": 581773, "gs_citation": 51, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=765620667531777439&as_sdt=5,31&sciodt=0,31&hl=en", "gs_version_total": 4, "aff": "Chinese Information Processing Laboratory + University of Chinese Academy of Sciences; Chinese Information Processing Laboratory; Chinese Information Processing Laboratory + State Key Laboratory of Computer Science; Chinese Information Processing Laboratory + State Key Laboratory of Computer Science", "aff_domain": "iscas.ac.cn;iscas.ac.cn;iscas.ac.cn;iscas.ac.cn", "email": "iscas.ac.cn;iscas.ac.cn;iscas.ac.cn;iscas.ac.cn", "github": "", "project": "", "author_num": 4, "aff_unique_index": "0+1;0;0+2;0+2", "aff_unique_norm": "Chinese Information Processing Laboratory;University of Chinese Academy of Sciences;State Key Laboratory of Computer Science", "aff_unique_dep": "Information Processing;;", "aff_unique_url": ";http://www.ucas.ac.cn;", "aff_unique_abbr": ";UCAS;", "aff_campus_unique_index": ";;", "aff_campus_unique": "", "aff_country_unique_index": "0+0;0;0+0;0+0", "aff_country_unique": "China" }, { "id": "2021.acl-long.72", "title": "DeCLUTR: Deep Contrastive Learning for Unsupervised Textual Representations", "track": "main", "status": "Long", "award": false, "abstract": "Sentence embeddings are an important component of many natural language processing (NLP) systems. Like word embeddings, sentence embeddings are typically learned on large text corpora and then transferred to various downstream tasks, such as clustering and retrieval. Unlike word embeddings, the highest performing solutions for learning sentence embeddings require labelled data, limiting their usefulness to languages and domains where labelled data is abundant. In this paper, we present DeCLUTR: Deep Contrastive Learning for Unsupervised Textual Representations. Inspired by recent advances in deep metric learning (DML), we carefully design a self-supervised objective for learning universal sentence embeddings that does not require labelled training data. When used to extend the pretraining of transformer-based language models, our approach closes the performance gap between unsupervised and supervised pretraining for universal sentence encoders. Importantly, our experiments suggest that the quality of the learned embeddings scale with both the number of trainable parameters and the amount of unlabelled training data. Our code and pretrained models are publicly available and can be easily adapted to new domains or used to embed unseen text.", "author": "John Giorgi; Osvald Nitski; Bo Wang; Gary Bader", "authorids": "/j/john-giorgi/; /o/osvald-nitski/; /b/bo-wang/; /g/gary-bader/", "bibtex": "@inproceedings{giorgi-etal-2021-declutr,\n title = \"{D}e{CLUTR}: Deep Contrastive Learning for Unsupervised Textual Representations\",\n author = \"Giorgi, John and\n Nitski, Osvald and\n Wang, Bo and\n Bader, Gary\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.72/\",\n doi = \"10.18653/v1/2021.acl-long.72\",\n pages = \"879--895\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.72.pdf", "site": "https://aclanthology.org/2021.acl-long.72/", "pdf_size": 1286608, "gs_citation": 570, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=12179202598837548213&as_sdt=5,44&sciodt=0,44&hl=en", "gs_version_total": 7, "aff": "Department of Computer Science, University of Toronto + Terrence Donnelly Centre for Cellular & Biomolecular Research + Vector Institute for Artificial Intelligence; Faculty of Applied Science and Engineering, University of Toronto + Peter Munk Cardiac Center, University Health Network; Department of Computer Science, University of Toronto + Department of Laboratory Medicine and Pathobiology, University of Toronto + Vector Institute for Artificial Intelligence; Department of Computer Science, University of Toronto + Department of Molecular Genetics, University of Toronto + Terrence Donnelly Centre for Cellular & Biomolecular Research", "aff_domain": "mail.utoronto.ca;mail.utoronto.ca;vectorinstitute.ai;mail.utoronto.ca", "email": "mail.utoronto.ca;mail.utoronto.ca;vectorinstitute.ai;mail.utoronto.ca", "github": "https://github.com/JohnGiorgi/DeCLUTR", "project": "", "author_num": 4, "aff_unique_index": "0+0+1;0+2;0+0+1;0+0+0", "aff_unique_norm": "University of Toronto;Vector Institute for Artificial Intelligence;University Health Network", "aff_unique_dep": "Department of Computer Science;;Peter Munk Cardiac Center", "aff_unique_url": "https://www.utoronto.ca;https://vectorinstitute.ai/;https://www.uhn.ca", "aff_unique_abbr": "U of T;Vector Institute;", "aff_campus_unique_index": "0;0;0;0+0", "aff_campus_unique": "Toronto;", "aff_country_unique_index": "0+0+0;0+0;0+0+0;0+0+0", "aff_country_unique": "Canada" }, { "id": "2021.acl-short.63", "title": "Deep Context- and Relation-Aware Learning for Aspect-based Sentiment Analysis", "track": "main", "status": "Short", "award": false, "abstract": "Existing works for aspect-based sentiment analysis (ABSA) have adopted a unified approach, which allows the interactive relations among subtasks. However, we observe that these methods tend to predict polarities based on the literal meaning of aspect and opinion terms and mainly consider relations implicitly among subtasks at the word level. In addition, identifying multiple aspect\u2013opinion pairs with their polarities is much more challenging. Therefore, a comprehensive understanding of contextual information w.r.t. the aspect and opinion are further required in ABSA. In this paper, we propose Deep Contextualized Relation-Aware Network (DCRAN), which allows interactive relations among subtasks with deep contextual information based on two modules (i.e., Aspect and Opinion Propagation and Explicit Self-Supervised Strategies). Especially, we design novel self-supervised strategies for ABSA, which have strengths in dealing with multiple aspects. Experimental results show that DCRAN significantly outperforms previous state-of-the-art methods by large margins on three widely used benchmarks.", "author": "Shinhyeok Oh; Dongyub Lee; Taesun Whang; IlNam Park; Seo Gaeun; EungGyun Kim; Harksoo Kim", "authorids": "/s/shinhyeok-oh/; /d/dongyub-lee/; /t/taesun-whang/; /i/ilnam-park/; /s/seo-gaeun/; /e/eunggyun-kim/; /h/harksoo-kim/", "bibtex": "@inproceedings{oh-etal-2021-deep,\n title = \"Deep Context- and Relation-Aware Learning for Aspect-based Sentiment Analysis\",\n author = \"Oh, Shinhyeok and\n Lee, Dongyub and\n Whang, Taesun and\n Park, IlNam and\n Gaeun, Seo and\n Kim, EungGyun and\n Kim, Harksoo\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-short.63/\",\n doi = \"10.18653/v1/2021.acl-short.63\",\n pages = \"495--503\"\n}", "pdf": "https://aclanthology.org/2021.acl-short.63.pdf", "site": "https://aclanthology.org/2021.acl-short.63/", "pdf_size": 385677, "gs_citation": 30, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=1871029119770745942&as_sdt=5,33&sciodt=0,33&hl=en", "gs_version_total": 5, "aff": "Netmarble AI Center; Kakao Corp.; Wisenut Inc.; Kakao Enterprise; Kakao Enterprise; Kakao Enterprise; Konkuk University", "aff_domain": "; ; ; ; ; ; ", "email": "; ; ; ; ; ; ", "github": "", "project": "", "author_num": 7, "aff_unique_index": "0;1;2;3;3;3;4", "aff_unique_norm": "Netmarble;Kakao Corp.;Wisenut Inc.;Kakao Enterprise;Konkuk University", "aff_unique_dep": "AI Center;;;;", "aff_unique_url": "https://www.netmarble.com;https://www.kakao.com;https://www.wisenut.com;https://enterprise.kakao.com;http://www.konkuk.edu", "aff_unique_abbr": "Netmarble;Kakao;;Kakao Enterprise;KU", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0;0;1;0;0;0;0", "aff_country_unique": "South Korea;United States" }, { "id": "2021.acl-long.31", "title": "Deep Differential Amplifier for Extractive Summarization", "track": "main", "status": "Long", "award": false, "abstract": "For sentence-level extractive summarization, there is a disproportionate ratio of selected and unselected sentences, leading to flatting the summary features when maximizing the accuracy. The imbalanced classification of summarization is inherent, which can\u2019t be addressed by common algorithms easily. In this paper, we conceptualize the single-document extractive summarization as a rebalance problem and present a deep differential amplifier framework. Specifically, we first calculate and amplify the semantic difference between each sentence and all other sentences, and then apply the residual unit as the second item of the differential amplifier to deepen the architecture. Finally, to compensate for the imbalance, the corresponding objective loss of minority class is boosted by a weighted cross-entropy. In contrast to previous approaches, this model pays more attention to the pivotal information of one sentence, instead of all the informative context modeling by recurrent or Transformer architecture. We demonstrate experimentally on two benchmark datasets that our summarizer performs competitively against state-of-the-art methods. Our source code will be available on Github.", "author": "Ruipeng Jia; Yanan Cao; Fang Fang; Yuchen Zhou; Zheng Fang; Yanbing Liu; Shi Wang", "authorids": "/r/ruipeng-jia/; /y/yanan-cao/; /f/fang-fang/; /y/yuchen-zhou/; /z/zheng-fang/; /y/yanbing-liu/; /s/shi-wang/", "bibtex": "@inproceedings{jia-etal-2021-deep,\n title = \"Deep Differential Amplifier for Extractive Summarization\",\n author = \"Jia, Ruipeng and\n Cao, Yanan and\n Fang, Fang and\n Zhou, Yuchen and\n Fang, Zheng and\n Liu, Yanbing and\n Wang, Shi\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.31/\",\n doi = \"10.18653/v1/2021.acl-long.31\",\n pages = \"366--376\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.31.pdf", "site": "https://aclanthology.org/2021.acl-long.31/", "pdf_size": 745595, "gs_citation": 19, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=1141301750174602074&as_sdt=5,31&sciodt=0,31&hl=en", "gs_version_total": 2, "aff": "Institute of Information Engineering, Chinese Academy of Sciences + School of Cyber Security, University of Chinese Academy of Sciences; Institute of Information Engineering, Chinese Academy of Sciences + School of Cyber Security, University of Chinese Academy of Sciences; Institute of Information Engineering, Chinese Academy of Sciences + School of Cyber Security, University of Chinese Academy of Sciences; Institute of Information Engineering, Chinese Academy of Sciences; Institute of Information Engineering, Chinese Academy of Sciences; Institute of Information Engineering, Chinese Academy of Sciences + School of Cyber Security, University of Chinese Academy of Sciences; Institute of Computing Technology, Chinese Academy of Sciences", "aff_domain": "iie.ac.cn;iie.ac.cn;iie.ac.cn;iie.ac.cn;iie.ac.cn;iie.ac.cn;ict.ac.cn", "email": "iie.ac.cn;iie.ac.cn;iie.ac.cn;iie.ac.cn;iie.ac.cn;iie.ac.cn;ict.ac.cn", "github": "", "project": "", "author_num": 7, "aff_unique_index": "0+1;0+1;0+1;0;0;0+1;0", "aff_unique_norm": "Chinese Academy of Sciences;University of Chinese Academy of Sciences", "aff_unique_dep": "Institute of Information Engineering;School of Cyber Security", "aff_unique_url": "http://www.cas.cn;http://www.ucas.ac.cn", "aff_unique_abbr": "CAS;UCAS", "aff_campus_unique_index": ";;;", "aff_campus_unique": "", "aff_country_unique_index": "0+0;0+0;0+0;0;0;0+0;0", "aff_country_unique": "China" }, { "id": "2021.acl-long.6", "title": "DeepRapper: Neural Rap Generation with Rhyme and Rhythm Modeling", "track": "main", "status": "Long", "award": false, "abstract": "Rap generation, which aims to produce lyrics and corresponding singing beats, needs to model both rhymes and rhythms. Previous works for rap generation focused on rhyming lyrics, but ignored rhythmic beats, which are important for rap performance. In this paper, we develop DeepRapper, a Transformer-based rap generation system that can model both rhymes and rhythms. Since there is no available rap datasets with rhythmic beats, we develop a data mining pipeline to collect a large-scale rap dataset, which includes a large number of rap songs with aligned lyrics and rhythmic beats. Second, we design a Transformer-based autoregressive language model which carefully models rhymes and rhythms. Specifically, we generate lyrics in the reverse order with rhyme representation and constraint for rhyme enhancement, and insert a beat symbol into lyrics for rhythm/beat modeling. To our knowledge, DeepRapper is the first system to generate rap with both rhymes and rhythms. Both objective and subjective evaluations demonstrate that DeepRapper generates creative and high-quality raps with rhymes and rhythms.", "author": "Lanqing Xue; Kaitao Song; Duocai Wu; Xu Tan; Nevin L. Zhang; Tao Qin; Wei-Qiang Zhang; Tie-Yan Liu", "authorids": "/l/lanqing-xue/; /k/kaitao-song/; /d/duocai-wu/; /x/xu-tan/; /n/nevin-l-zhang/; /t/tao-qin/; /w/wei-qiang-zhang/; /t/tie-yan-liu/", "bibtex": "@inproceedings{xue-etal-2021-deeprapper,\n title = \"{D}eep{R}apper: Neural Rap Generation with Rhyme and Rhythm Modeling\",\n author = \"Xue, Lanqing and\n Song, Kaitao and\n Wu, Duocai and\n Tan, Xu and\n Zhang, Nevin L. and\n Qin, Tao and\n Zhang, Wei-Qiang and\n Liu, Tie-Yan\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.6/\",\n doi = \"10.18653/v1/2021.acl-long.6\",\n pages = \"69--81\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.6.pdf", "site": "https://aclanthology.org/2021.acl-long.6/", "pdf_size": 1129430, "gs_citation": 45, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=16633357336699511438&as_sdt=5,33&sciodt=0,33&hl=en", "gs_version_total": 7, "aff": "The Hong Kong University of Science and Technology; Nanjing University of Science and Technology; Fudan University; Microsoft Research Asia; Tsinghua University; The Hong Kong University of Science and Technology; Microsoft Research Asia; Microsoft Research Asia", "aff_domain": "cse.ust.hk;njust.edu.cn;fudan.edu.cn;microsoft.com;cse.ust.hk;microsoft.com;tsinghua.edu.cn;microsoft.com", "email": "cse.ust.hk;njust.edu.cn;fudan.edu.cn;microsoft.com;cse.ust.hk;microsoft.com;tsinghua.edu.cn;microsoft.com", "github": "", "project": "", "author_num": 8, "aff_unique_index": "0;1;2;3;4;0;3;3", "aff_unique_norm": "Hong Kong University of Science and Technology;Nanjing University of Science and Technology;Fudan University;Microsoft Research;Tsinghua University", "aff_unique_dep": ";;;Research;", "aff_unique_url": "https://www.ust.hk;http://www.nust.edu.cn/;https://www.fudan.edu.cn;https://www.microsoft.com/en-us/research/group/asia;https://www.tsinghua.edu.cn", "aff_unique_abbr": "HKUST;NUST;Fudan;MSR Asia;THU", "aff_campus_unique_index": "1;1;1", "aff_campus_unique": ";Asia", "aff_country_unique_index": "0;0;0;0;0;0;0;0", "aff_country_unique": "China" }, { "id": "2021.acl-short.52", "title": "DefSent: Sentence Embeddings using Definition Sentences", "track": "main", "status": "Short", "award": false, "abstract": "Sentence embedding methods using natural language inference (NLI) datasets have been successfully applied to various tasks. However, these methods are only available for limited languages due to relying heavily on the large NLI datasets. In this paper, we propose DefSent, a sentence embedding method that uses definition sentences from a word dictionary, which performs comparably on unsupervised semantics textual similarity (STS) tasks and slightly better on SentEval tasks than conventional methods. Since dictionaries are available for many languages, DefSent is more broadly applicable than methods using NLI datasets without constructing additional datasets. We demonstrate that DefSent performs comparably on unsupervised semantics textual similarity (STS) tasks and slightly better on SentEval tasks to the methods using large NLI datasets. Our code is publicly available at https://github.com/hpprc/defsent.", "author": "Hayato Tsukagoshi; Ryohei Sasano; Koichi Takeda", "authorids": "/h/hayato-tsukagoshi/; /r/ryohei-sasano/; /k/koichi-takeda/", "bibtex": "@inproceedings{tsukagoshi-etal-2021-defsent,\n title = \"{D}ef{S}ent: Sentence Embeddings using Definition Sentences\",\n author = \"Tsukagoshi, Hayato and\n Sasano, Ryohei and\n Takeda, Koichi\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-short.52/\",\n doi = \"10.18653/v1/2021.acl-short.52\",\n pages = \"411--418\"\n}", "pdf": "https://aclanthology.org/2021.acl-short.52.pdf", "site": "https://aclanthology.org/2021.acl-short.52/", "pdf_size": 753867, "gs_citation": 30, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=6497294506527635538&as_sdt=5,24&sciodt=0,24&hl=en", "gs_version_total": 5, "aff": "Graduate School of Informatics, Nagoya University; Graduate School of Informatics, Nagoya University; Graduate School of Informatics, Nagoya University", "aff_domain": "e.mbox.nagoya-u.ac.jp;i.nagoya-u.ac.jp;i.nagoya-u.ac.jp", "email": "e.mbox.nagoya-u.ac.jp;i.nagoya-u.ac.jp;i.nagoya-u.ac.jp", "github": "https://github.com/hpprc/defsent", "project": "", "author_num": 3, "aff_unique_index": "0;0;0", "aff_unique_norm": "Nagoya University", "aff_unique_dep": "Graduate School of Informatics", "aff_unique_url": "https://www.nagoya-u.ac.jp", "aff_unique_abbr": "Nagoya U", "aff_campus_unique_index": "0;0;0", "aff_campus_unique": "Nagoya", "aff_country_unique_index": "0;0;0", "aff_country_unique": "Japan" }, { "id": "2021.acl-long.426", "title": "Defense against Synonym Substitution-based Adversarial Attacks via Dirichlet Neighborhood Ensemble", "track": "main", "status": "Long", "award": false, "abstract": "Although deep neural networks have achieved prominent performance on many NLP tasks, they are vulnerable to adversarial examples. We propose Dirichlet Neighborhood Ensemble (DNE), a randomized method for training a robust model to defense synonym substitution-based attacks. During training, DNE forms virtual sentences by sampling embedding vectors for each word in an input sentence from a convex hull spanned by the word and its synonyms, and it augments them with the training data. In such a way, the model is robust to adversarial attacks while maintaining the performance on the original clean data. DNE is agnostic to the network architectures and scales to large models (e.g., BERT) for NLP applications. Through extensive experimentation, we demonstrate that our method consistently outperforms recently proposed defense methods by a significant margin across different network architectures and multiple data sets.", "author": "Yi Zhou; Xiaoqing Zheng; Cho-Jui Hsieh; Kai-Wei Chang; Xuanjing Huang", "authorids": "/y/yi-zhou/; /x/xiaoqing-zheng/; /c/cho-jui-hsieh/; /k/kai-wei-chang/; /x/xuan-jing-huang/", "bibtex": "@inproceedings{zhou-etal-2021-defense,\n title = \"Defense against Synonym Substitution-based Adversarial Attacks via {D}irichlet Neighborhood Ensemble\",\n author = \"Zhou, Yi and\n Zheng, Xiaoqing and\n Hsieh, Cho-Jui and\n Chang, Kai-Wei and\n Huang, Xuanjing\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.426/\",\n doi = \"10.18653/v1/2021.acl-long.426\",\n pages = \"5482--5492\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.426.pdf", "site": "https://aclanthology.org/2021.acl-long.426/", "pdf_size": 659355, "gs_citation": 82, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=18187562486745527333&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 3, "aff": "School of Computer Science, Fudan University, Shanghai, China+Shanghai Key Laboratory of Intelligent Information Processing; School of Computer Science, Fudan University, Shanghai, China+Shanghai Key Laboratory of Intelligent Information Processing; Department of Computer Science, University of California, Los Angeles, USA; Department of Computer Science, University of California, Los Angeles, USA; School of Computer Science, Fudan University, Shanghai, China+Shanghai Key Laboratory of Intelligent Information Processing", "aff_domain": "fudan.edu.cn;fudan.edu.cn;cs.ucla.edu;cs.ucla.edu;fudan.edu.cn", "email": "fudan.edu.cn;fudan.edu.cn;cs.ucla.edu;cs.ucla.edu;fudan.edu.cn", "github": "", "project": "", "author_num": 5, "aff_unique_index": "0+1;0+1;2;2;0+1", "aff_unique_norm": "Fudan University;Shanghai Key Laboratory of Intelligent Information Processing;University of California, Los Angeles", "aff_unique_dep": "School of Computer Science;Intelligent Information Processing;Department of Computer Science", "aff_unique_url": "https://www.fudan.edu.cn;;https://www.ucla.edu", "aff_unique_abbr": "Fudan;;UCLA", "aff_campus_unique_index": "0;0;2;2;0", "aff_campus_unique": "Shanghai;;Los Angeles", "aff_country_unique_index": "0+0;0+0;1;1;0+0", "aff_country_unique": "China;United States" }, { "id": "2021.acl-short.119", "title": "Demoting the Lead Bias in News Summarization via Alternating Adversarial Learning", "track": "main", "status": "Short", "award": false, "abstract": "In news articles the lead bias is a common phenomenon that usually dominates the learning signals for neural extractive summarizers, severely limiting their performance on data with different or even no bias. In this paper, we introduce a novel technique to demote lead bias and make the summarizer focus more on the content semantics. Experiments on two news corpora with different degrees of lead bias show that our method can effectively demote the model\u2019s learned lead bias and improve its generality on out-of-distribution data, with little to no performance loss on in-distribution data.", "author": "Linzi Xing; Wen Xiao; Giuseppe Carenini", "authorids": "/l/linzi-xing/; /w/wen-xiao/; /g/giuseppe-carenini/", "bibtex": "@inproceedings{xing-etal-2021-demoting,\n title = \"Demoting the Lead Bias in News Summarization via Alternating Adversarial Learning\",\n author = \"Xing, Linzi and\n Xiao, Wen and\n Carenini, Giuseppe\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-short.119/\",\n doi = \"10.18653/v1/2021.acl-short.119\",\n pages = \"948--954\"\n}", "pdf": "https://aclanthology.org/2021.acl-short.119.pdf", "site": "https://aclanthology.org/2021.acl-short.119/", "pdf_size": 898605, "gs_citation": 21, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=9159238319033760858&as_sdt=5,44&sciodt=0,44&hl=en", "gs_version_total": 6, "aff": "Department of Computer Science, University of British Columbia; Department of Computer Science, University of British Columbia; Department of Computer Science, University of British Columbia", "aff_domain": "cs.ubc.ca;cs.ubc.ca;cs.ubc.ca", "email": "cs.ubc.ca;cs.ubc.ca;cs.ubc.ca", "github": "https://github.com/lxing532/Debiasing", "project": "", "author_num": 3, "aff_unique_index": "0;0;0", "aff_unique_norm": "University of British Columbia", "aff_unique_dep": "Department of Computer Science", "aff_unique_url": "https://www.ubc.ca", "aff_unique_abbr": "UBC", "aff_campus_unique_index": "0;0;0", "aff_campus_unique": "Vancouver", "aff_country_unique_index": "0;0;0", "aff_country_unique": "Canada" }, { "id": "2021.acl-long.344", "title": "Dependency-driven Relation Extraction with Attentive Graph Convolutional Networks", "track": "main", "status": "Long", "award": false, "abstract": "Syntactic information, especially dependency trees, has been widely used by existing studies to improve relation extraction with better semantic guidance for analyzing the context information associated with the given entities. However, most existing studies suffer from the noise in the dependency trees, especially when they are automatically generated, so that intensively leveraging dependency information may introduce confusions to relation classification and necessary pruning is of great importance in this task. In this paper, we propose a dependency-driven approach for relation extraction with attentive graph convolutional networks (A-GCN). In this approach, an attention mechanism upon graph convolutional networks is applied to different contextual words in the dependency tree obtained from an off-the-shelf dependency parser, to distinguish the importance of different word dependencies. Consider that dependency types among words also contain important contextual guidance, which is potentially helpful for relation extraction, we also include the type information in A-GCN modeling. Experimental results on two English benchmark datasets demonstrate the effectiveness of our A-GCN, which outperforms previous studies and achieves state-of-the-art performance on both datasets.", "author": "Yuanhe Tian; Guimin Chen; Yan Song; Xiang Wan", "authorids": "/y/yuanhe-tian/; /g/guimin-chen/; /y/yan-song/; /x/xiang-wan/", "bibtex": "@inproceedings{tian-etal-2021-dependency,\n title = \"Dependency-driven Relation Extraction with Attentive Graph Convolutional Networks\",\n author = \"Tian, Yuanhe and\n Chen, Guimin and\n Song, Yan and\n Wan, Xiang\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.344/\",\n doi = \"10.18653/v1/2021.acl-long.344\",\n pages = \"4458--4471\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.344.pdf", "site": "https://aclanthology.org/2021.acl-long.344/", "pdf_size": 2594219, "gs_citation": 149, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=15709499363513859165&as_sdt=80000005&sciodt=0,23&hl=en", "gs_version_total": 5, "aff": "University of Washington; QTrade; The Chinese University of Hong Kong (Shenzhen) + Shenzhen Research Institute of Big Data; Shenzhen Research Institute of Big Data", "aff_domain": "uw.edu;foxmail.com;cuhk.edu.cn;sribd.cn", "email": "uw.edu;foxmail.com;cuhk.edu.cn;sribd.cn", "github": "https://github.com/cuhksz-nlp/RE-AGCN", "project": "", "author_num": 4, "aff_unique_index": "0;1;2+3;3", "aff_unique_norm": "University of Washington;QTrade;The Chinese University of Hong Kong;Shenzhen Research Institute of Big Data", "aff_unique_dep": ";;;", "aff_unique_url": "https://www.washington.edu;;https://www.cuhk.edu.cn;http://www.sribd.cn", "aff_unique_abbr": "UW;;CUHK;", "aff_campus_unique_index": "1", "aff_campus_unique": ";Shenzhen", "aff_country_unique_index": "0;2+2;2", "aff_country_unique": "United States;;China" }, { "id": "2021.acl-long.516", "title": "Detecting Propaganda Techniques in Memes", "track": "main", "status": "Long", "award": false, "abstract": "Propaganda can be defined as a form of communication that aims to influence the opinions or the actions of people towards a specific goal; this is achieved by means of well-defined rhetorical and psychological devices. Propaganda, in the form we know it today, can be dated back to the beginning of the 17th century. However, it is with the advent of the Internet and the social media that propaganda has started to spread on a much larger scale than before, thus becoming major societal and political issue. Nowadays, a large fraction of propaganda in social media is multimodal, mixing textual with visual content. With this in mind, here we propose a new multi-label multimodal task: detecting the type of propaganda techniques used in memes. We further create and release a new corpus of 950 memes, carefully annotated with 22 propaganda techniques, which can appear in the text, in the image, or in both. Our analysis of the corpus shows that understanding both modalities together is essential for detecting these techniques. This is further confirmed in our experiments with several state-of-the-art multimodal models.", "author": "Dimitar Dimitrov; Bishr Bin Ali; Shaden Shaar; Firoj Alam; Fabrizio Silvestri; Hamed Firooz; Preslav Nakov; Giovanni Da San Martino", "authorids": "/d/dimitar-dimitrov/; /b/bishr-bin-ali/; /s/shaden-shaar/; /f/firoj-alam/; /f/fabrizio-silvestri/; /h/hamed-firooz/; /p/preslav-nakov/; /g/giovanni-da-san-martino/", "bibtex": "@inproceedings{dimitrov-etal-2021-detecting,\n title = \"Detecting Propaganda Techniques in Memes\",\n author = \"Dimitrov, Dimitar and\n Bin Ali, Bishr and\n Shaar, Shaden and\n Alam, Firoj and\n Silvestri, Fabrizio and\n Firooz, Hamed and\n Nakov, Preslav and\n Da San Martino, Giovanni\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.516/\",\n doi = \"10.18653/v1/2021.acl-long.516\",\n pages = \"6603--6617\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.516.pdf", "site": "https://aclanthology.org/2021.acl-long.516/", "pdf_size": 4800688, "gs_citation": 102, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=15845988941476634398&as_sdt=80000005&sciodt=0,23&hl=en", "gs_version_total": 13, "aff": "Sofia University \"St. Kliment Ohridski\"; King's College London; Qatar Computing Research Institute, HBKU; Qatar Computing Research Institute, HBKU; Sapienza University of Rome; Facebook AI; Qatar Computing Research Institute, HBKU; University of Padova", "aff_domain": "gmail.com;gmail.com;hbku.edu.qa;hbku.edu.qa;diag.uniroma1.it;fb.com;hbku.edu.qa;math.unipd.it", "email": "gmail.com;gmail.com;hbku.edu.qa;hbku.edu.qa;diag.uniroma1.it;fb.com;hbku.edu.qa;math.unipd.it", "github": "", "project": "", "author_num": 8, "aff_unique_index": "0;1;2;2;3;4;2;5", "aff_unique_norm": "Sofia University St. Kliment Ohridski;King's College London;Qatar Computing Research Institute;Sapienza University of Rome;Facebook;University of Padova", "aff_unique_dep": ";;;;Facebook AI;", "aff_unique_url": "https://www.uni-sofia.bg;https://www.kcl.ac.uk;https://www.qcri.org;https://www.uniroma1.it;https://www.facebook.com;https://www.unipd.it", "aff_unique_abbr": "SU;KCL;QCRI;Sapienza;Facebook AI;UNIPD", "aff_campus_unique_index": "1", "aff_campus_unique": ";Rome", "aff_country_unique_index": "0;1;2;2;3;4;2;3", "aff_country_unique": "Bulgaria;United Kingdom;Qatar;Italy;United States" }, { "id": "2021.acl-long.512", "title": "Determinantal Beam Search", "track": "main", "status": "Long", "award": false, "abstract": "Beam search is a go-to strategy for decoding neural sequence models. The algorithm can naturally be viewed as a subset optimization problem, albeit one where the corresponding set function does not reflect interactions between candidates. Empirically, this leads to sets often exhibiting high overlap, e.g., strings may differ by only a single word. Yet in use-cases that call for multiple solutions, a diverse or representative set is often desired. To address this issue, we propose a reformulation of beam search, which we call determinantal beam search. Determinantal beam search has a natural relationship to determinantal point processes (DPPs), models over sets that inherently encode intra-set interactions. By posing iterations in beam search as a series of subdeterminant maximization problems, we can turn the algorithm into a diverse subset selection process. In a case study, we use the string subsequence kernel to explicitly encourage n-gram coverage in text generated from a sequence model. We observe that our algorithm offers competitive performance against other diverse set generation strategies in the context of language generation, while providing a more general approach to optimizing for diversity.", "author": "Clara Meister; Martina Forster; Ryan Cotterell", "authorids": "/c/clara-meister/; /m/martina-forster/; /r/ryan-cotterell/", "bibtex": "@inproceedings{meister-etal-2021-determinantal,\n title = \"Determinantal Beam Search\",\n author = \"Meister, Clara and\n Forster, Martina and\n Cotterell, Ryan\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.512/\",\n doi = \"10.18653/v1/2021.acl-long.512\",\n pages = \"6551--6562\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.512.pdf", "site": "https://aclanthology.org/2021.acl-long.512/", "pdf_size": 770936, "gs_citation": 19, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=16056205526340376842&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 7, "aff": "ETH Z\u00fcrich; University of Cambridge; ETH Z\u00fcrich", "aff_domain": "inf.ethz.ch;ethz.ch;inf.ethz.ch", "email": "inf.ethz.ch;ethz.ch;inf.ethz.ch", "github": "", "project": "", "author_num": 3, "aff_unique_index": "0;1;0", "aff_unique_norm": "ETH Z\u00fcrich;University of Cambridge", "aff_unique_dep": ";", "aff_unique_url": "https://www.ethz.ch;https://www.cam.ac.uk", "aff_unique_abbr": "ETHZ;Cambridge", "aff_campus_unique_index": "1", "aff_campus_unique": ";Cambridge", "aff_country_unique_index": "0;1;0", "aff_country_unique": "Switzerland;United Kingdom" }, { "id": "2021.acl-long.137", "title": "Dialogue Response Selection with Hierarchical Curriculum Learning", "track": "main", "status": "Long", "award": false, "abstract": "We study the learning of a matching model for dialogue response selection. Motivated by the recent finding that models trained with random negative samples are not ideal in real-world scenarios, we propose a hierarchical curriculum learning framework that trains the matching model in an \u201ceasy-to-difficult\u201d scheme. Our learning framework consists of two complementary curricula: (1) corpus-level curriculum (CC); and (2) instance-level curriculum (IC). In CC, the model gradually increases its ability in finding the matching clues between the dialogue context and a response candidate. As for IC, it progressively strengthens the model\u2019s ability in identifying the mismatching information between the dialogue context and a response candidate. Empirical studies on three benchmark datasets with three state-of-the-art matching models demonstrate that the proposed learning framework significantly improves the model performance across various evaluation metrics.", "author": "Yixuan Su; Deng Cai; Qingyu Zhou; Zibo Lin; Simon Baker; Yunbo Cao; Shuming Shi; Nigel Collier; Yan Wang", "authorids": "/y/yixuan-su/; /d/deng-cai/; /q/qingyu-zhou/; /z/zibo-lin/; /s/simon-baker/; /y/yunbo-cao/; /s/shuming-shi/; /n/nigel-collier/; /y/yan-wang/", "bibtex": "@inproceedings{su-etal-2021-dialogue,\n title = \"Dialogue Response Selection with Hierarchical Curriculum Learning\",\n author = \"Su, Yixuan and\n Cai, Deng and\n Zhou, Qingyu and\n Lin, Zibo and\n Baker, Simon and\n Cao, Yunbo and\n Shi, Shuming and\n Collier, Nigel and\n Wang, Yan\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.137/\",\n doi = \"10.18653/v1/2021.acl-long.137\",\n pages = \"1740--1751\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.137.pdf", "site": "https://aclanthology.org/2021.acl-long.137/", "pdf_size": 1951703, "gs_citation": 45, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=5657583979837527817&as_sdt=5,33&sciodt=0,33&hl=en", "gs_version_total": 6, "aff": "Language Technology Lab, University of Cambridge; The Chinese University of Hong Kong; Tencent Inc. + Language Technology Lab, University of Cambridge; Tencent Inc.; Tencent Inc.; Tencent Inc.; Tencent Inc.; Language Technology Lab, University of Cambridge; Tencent Inc.", "aff_domain": "cam.ac.uk;cam.ac.uk;tencent.com;tencent.com;tencent.com;tencent.com;tencent.com;cam.ac.uk;gmail.com", "email": "cam.ac.uk;cam.ac.uk;tencent.com;tencent.com;tencent.com;tencent.com;tencent.com;cam.ac.uk;gmail.com", "github": "", "project": "", "author_num": 9, "aff_unique_index": "0;1;2+0;2;2;2;2;0;2", "aff_unique_norm": "University of Cambridge;The Chinese University of Hong Kong;Tencent", "aff_unique_dep": "Language Technology Lab;;", "aff_unique_url": "https://www.cam.ac.uk;https://www.cuhk.edu.hk;https://www.tencent.com", "aff_unique_abbr": "Cambridge;CUHK;Tencent", "aff_campus_unique_index": "0;0;0", "aff_campus_unique": "Cambridge;", "aff_country_unique_index": "0;1;1+0;1;1;1;1;0;1", "aff_country_unique": "United Kingdom;China" }, { "id": "2021.acl-long.547", "title": "DialogueCRN: Contextual Reasoning Networks for Emotion Recognition in Conversations", "track": "main", "status": "Long", "award": false, "abstract": "Emotion Recognition in Conversations (ERC) has gained increasing attention for developing empathetic machines. Recently, many approaches have been devoted to perceiving conversational context by deep learning models. However, these approaches are insufficient in understanding the context due to lacking the ability to extract and integrate emotional clues. In this work, we propose novel Contextual Reasoning Networks (DialogueCRN) to fully understand the conversational context from a cognitive perspective. Inspired by the Cognitive Theory of Emotion, we design multi-turn reasoning modules to extract and integrate emotional clues. The reasoning module iteratively performs an intuitive retrieving process and a conscious reasoning process, which imitates human unique cognitive thinking. Extensive experiments on three public benchmark datasets demonstrate the effectiveness and superiority of the proposed model.", "author": "Dou Hu; Lingwei Wei; Xiaoyong Huai", "authorids": "/d/dou-hu/; /l/lingwei-wei/; /x/xiaoyong-huai/", "bibtex": "@inproceedings{hu-etal-2021-dialoguecrn,\n title = \"{D}ialogue{CRN}: Contextual Reasoning Networks for Emotion Recognition in Conversations\",\n author = \"Hu, Dou and\n Wei, Lingwei and\n Huai, Xiaoyong\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.547/\",\n doi = \"10.18653/v1/2021.acl-long.547\",\n pages = \"7042--7052\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.547.pdf", "site": "https://aclanthology.org/2021.acl-long.547/", "pdf_size": 1486599, "gs_citation": 236, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=15319494068039071696&as_sdt=5,33&sciodt=0,33&hl=en", "gs_version_total": 6, "aff": "National Computer System Engineering Research Institute of China; Institute of Information Engineering, Chinese Academy of Sciences + School of Cyber Security, University of Chinese Academy of Sciences; National Computer System Engineering Research Institute of China", "aff_domain": "mails.ucas.edu.cn;mails.ucas.edu.cn;sina.com", "email": "mails.ucas.edu.cn;mails.ucas.edu.cn;sina.com", "github": "", "project": "", "author_num": 3, "aff_unique_index": "0;1+2;0", "aff_unique_norm": "National Computer System Engineering Research Institute;Chinese Academy of Sciences;University of Chinese Academy of Sciences", "aff_unique_dep": "Computer System Engineering;Institute of Information Engineering;School of Cyber Security", "aff_unique_url": ";http://www.cas.cn;http://www.ucas.ac.cn", "aff_unique_abbr": ";CAS;UCAS", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0;0+0;0", "aff_country_unique": "China" }, { "id": "2021.acl-short.5", "title": "Difficulty-Aware Machine Translation Evaluation", "track": "main", "status": "Short", "award": false, "abstract": "The high-quality translation results produced by machine translation (MT) systems still pose a huge challenge for automatic evaluation. Current MT evaluation pays the same attention to each sentence component, while the questions of real-world examinations (e.g., university examinations) have different difficulties and weightings. In this paper, we propose a novel difficulty-aware MT evaluation metric, expanding the evaluation dimension by taking translation difficulty into consideration. A translation that fails to be predicted by most MT systems will be treated as a difficult one and assigned a large weight in the final score function, and conversely. Experimental results on the WMT19 English-German Metrics shared tasks show that our proposed method outperforms commonly used MT metrics in terms of human correlation. In particular, our proposed method performs well even when all the MT systems are very competitive, which is when most existing metrics fail to distinguish between them. The source code is freely available at https://github.com/NLP2CT/Difficulty-Aware-MT-Evaluation.", "author": "Runzhe Zhan; Xuebo Liu; Derek F. Wong; Lidia S. Chao", "authorids": "/r/runzhe-zhan/; /x/xuebo-liu/; /d/derek-f-wong/; /l/lidia-s-chao/", "bibtex": "@inproceedings{zhan-etal-2021-difficulty,\n title = \"Difficulty-Aware Machine Translation Evaluation\",\n author = \"Zhan, Runzhe and\n Liu, Xuebo and\n Wong, Derek F. and\n Chao, Lidia S.\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-short.5/\",\n doi = \"10.18653/v1/2021.acl-short.5\",\n pages = \"26--32\"\n}", "pdf": "https://aclanthology.org/2021.acl-short.5.pdf", "site": "https://aclanthology.org/2021.acl-short.5/", "pdf_size": 466345, "gs_citation": 7, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=3086066556159094082&as_sdt=5,33&sciodt=0,33&hl=en", "gs_version_total": 7, "aff": "NLP2CT Lab, Department of Computer and Information Science, University of Macau; NLP2CT Lab, Department of Computer and Information Science, University of Macau; NLP2CT Lab, Department of Computer and Information Science, University of Macau; NLP2CT Lab, Department of Computer and Information Science, University of Macau", "aff_domain": "nlp2ct.gmail.com;nlp2ct.gmail.com;um.edu.mo;um.edu.mo", "email": "nlp2ct.gmail.com;nlp2ct.gmail.com;um.edu.mo;um.edu.mo", "github": "https://github.com/NLP2CT/Difficulty-Aware-MT-Evaluation", "project": "", "author_num": 4, "aff_unique_index": "0;0;0;0", "aff_unique_norm": "University of Macau", "aff_unique_dep": "Department of Computer and Information Science", "aff_unique_url": "https://www.um.edu.mo", "aff_unique_abbr": "UM", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0;0;0;0", "aff_country_unique": "Macau" }, { "id": "2021.acl-long.123", "title": "Directed Acyclic Graph Network for Conversational Emotion Recognition", "track": "main", "status": "Long", "award": false, "abstract": "The modeling of conversational context plays a vital role in emotion recognition from conversation (ERC). In this paper, we put forward a novel idea of encoding the utterances with a directed acyclic graph (DAG) to better model the intrinsic structure within a conversation, and design a directed acyclic neural network, namely DAG-ERC, to implement this idea. In an attempt to combine the strengths of conventional graph-based neural models and recurrence-based neural models, DAG-ERC provides a more intuitive way to model the information flow between long-distance conversation background and nearby context. Extensive experiments are conducted on four ERC benchmarks with state-of-the-art models employed as baselines for comparison. The empirical results demonstrate the superiority of this new model and confirm the motivation of the directed acyclic graph architecture for ERC.", "author": "Weizhou Shen; Siyue Wu; Yunyi Yang; Xiaojun Quan", "authorids": "/w/weizhou-shen/; /s/siyue-wu/; /y/yunyi-yang/; /x/xiaojun-quan/", "bibtex": "@inproceedings{shen-etal-2021-directed,\n title = \"Directed Acyclic Graph Network for Conversational Emotion Recognition\",\n author = \"Shen, Weizhou and\n Wu, Siyue and\n Yang, Yunyi and\n Quan, Xiaojun\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.123/\",\n doi = \"10.18653/v1/2021.acl-long.123\",\n pages = \"1551--1560\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.123.pdf", "site": "https://aclanthology.org/2021.acl-long.123/", "pdf_size": 2422849, "gs_citation": 290, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=17593252433930653186&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 6, "aff": "School of Computer Science and Engineering, Sun Yat-sen University, China; School of Computer Science and Engineering, Sun Yat-sen University, China; School of Computer Science and Engineering, Sun Yat-sen University, China; School of Computer Science and Engineering, Sun Yat-sen University, China", "aff_domain": "mail2.sysu.edu.cn;mail2.sysu.edu.cn;mail2.sysu.edu.cn;mail.sysu.edu.cn", "email": "mail2.sysu.edu.cn;mail2.sysu.edu.cn;mail2.sysu.edu.cn;mail.sysu.edu.cn", "github": "https://github.com/shenwzh3/DAG-ERC", "project": "", "author_num": 4, "aff_unique_index": "0;0;0;0", "aff_unique_norm": "Sun Yat-sen University", "aff_unique_dep": "School of Computer Science and Engineering", "aff_unique_url": "http://www.sysu.edu.cn", "aff_unique_abbr": "SYSU", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0;0;0;0", "aff_country_unique": "China" }, { "id": "2021.acl-long.63", "title": "Discontinuous Named Entity Recognition as Maximal Clique Discovery", "track": "main", "status": "Long", "award": false, "abstract": "Named entity recognition (NER) remains challenging when entity mentions can be discontinuous. Existing methods break the recognition process into several sequential steps. In training, they predict conditioned on the golden intermediate results, while at inference relying on the model output of the previous steps, which introduces exposure bias. To solve this problem, we first construct a segment graph for each sentence, in which each node denotes a segment (a continuous entity on its own, or a part of discontinuous entities), and an edge links two nodes that belong to the same entity. The nodes and edges can be generated respectively in one stage with a grid tagging scheme and learned jointly using a novel architecture named Mac. Then discontinuous NER can be reformulated as a non-parametric process of discovering maximal cliques in the graph and concatenating the spans in each clique. Experiments on three benchmarks show that our method outperforms the state-of-the-art (SOTA) results, with up to 3.5 percentage points improvement on F1, and achieves 5x speedup over the SOTA model.", "author": "Yucheng Wang; Bowen Yu; Hongsong Zhu; Tingwen Liu; Nan Yu; Limin Sun", "authorids": "/y/yucheng-wang/; /b/bowen-yu/; /h/hongsong-zhu/; /t/tingwen-liu/; /n/nan-yu/; /l/limin-sun/", "bibtex": "@inproceedings{wang-etal-2021-discontinuous,\n title = \"Discontinuous Named Entity Recognition as Maximal Clique Discovery\",\n author = \"Wang, Yucheng and\n Yu, Bowen and\n Zhu, Hongsong and\n Liu, Tingwen and\n Yu, Nan and\n Sun, Limin\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.63/\",\n doi = \"10.18653/v1/2021.acl-long.63\",\n pages = \"764--774\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.63.pdf", "site": "https://aclanthology.org/2021.acl-long.63/", "pdf_size": 679193, "gs_citation": 57, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=2935400522421125149&as_sdt=5,44&sciodt=0,44&hl=en", "gs_version_total": 4, "aff": "Institute of Information Engineering, Chinese Academy of Sciences; Institute of Information Engineering, Chinese Academy of Sciences; Institute of Information Engineering, Chinese Academy of Sciences; School of Cyber Security, University of Chinese Academy of Sciences; School of Cyber Security, University of Chinese Academy of Sciences; School of Cyber Security, University of Chinese Academy of Sciences", "aff_domain": "iie.ac.cn;iie.ac.cn;iie.ac.cn;iie.ac.cn;iie.ac.cn;iie.ac.cn", "email": "iie.ac.cn;iie.ac.cn;iie.ac.cn;iie.ac.cn;iie.ac.cn;iie.ac.cn", "github": "https://github.com/131250208/InfExtraction", "project": "", "author_num": 6, "aff_unique_index": "0;0;0;1;1;1", "aff_unique_norm": "Chinese Academy of Sciences;University of Chinese Academy of Sciences", "aff_unique_dep": "Institute of Information Engineering;School of Cyber Security", "aff_unique_url": "http://www.cas.cn;http://www.ucas.ac.cn", "aff_unique_abbr": "CAS;UCAS", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0;0;0;0;0;0", "aff_country_unique": "China" }, { "id": "2021.acl-long.136", "title": "Discovering Dialog Structure Graph for Coherent Dialog Generation", "track": "main", "status": "Long", "award": false, "abstract": "Learning discrete dialog structure graph from human-human dialogs yields basic insights into the structure of conversation, and also provides background knowledge to facilitate dialog generation. However, this problem is less studied in open-domain dialogue. In this paper, we conduct unsupervised discovery of discrete dialog structure from chitchat corpora, and then leverage it to facilitate coherent dialog generation in downstream systems. To this end, we present an unsupervised model, Discrete Variational Auto-Encoder with Graph Neural Network (DVAE-GNN), to discover discrete hierarchical latent dialog states (at the level of both session and utterance) and their transitions from corpus as a dialog structure graph. Then we leverage it as background knowledge to facilitate dialog management in a RL based dialog system. Experimental results on two benchmark corpora confirm that DVAE-GNN can discover meaningful dialog structure graph, and the use of dialog structure as background knowledge can significantly improve multi-turn coherence.", "author": "Jun Xu; Zeyang Lei; Haifeng Wang; Zheng-Yu Niu; Hua Wu; Wanxiang Che", "authorids": "/j/jun-xu/; /z/zeyang-lei/; /h/haifeng-wang/; /z/zheng-yu-niu/; /h/hua-wu/; /w/wanxiang-che/", "bibtex": "@inproceedings{xu-etal-2021-discovering,\n title = \"Discovering Dialog Structure Graph for Coherent Dialog Generation\",\n author = \"Xu, Jun and\n Lei, Zeyang and\n Wang, Haifeng and\n Niu, Zheng-Yu and\n Wu, Hua and\n Che, Wanxiang\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.136/\",\n doi = \"10.18653/v1/2021.acl-long.136\",\n pages = \"1726--1739\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.136.pdf", "site": "https://aclanthology.org/2021.acl-long.136/", "pdf_size": 1384007, "gs_citation": 27, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=13906394983264341540&as_sdt=5,44&sciodt=0,44&hl=en", "gs_version_total": 2, "aff": "Research Center for Social Computing and Information Retrieval, Harbin Institute of Technology, Harbin, China; Baidu Inc., Beijing, China; Baidu Inc., Beijing, China; Baidu Inc., Beijing, China; Baidu Inc., Beijing, China; Research Center for Social Computing and Information Retrieval, Harbin Institute of Technology, Harbin, China", "aff_domain": "ir.hit.edu.cn;baidu.com;baidu.com;baidu.com;baidu.com;ir.hit.edu.cn", "email": "ir.hit.edu.cn;baidu.com;baidu.com;baidu.com;baidu.com;ir.hit.edu.cn", "github": "", "project": "", "author_num": 6, "aff_unique_index": "0;1;1;1;1;0", "aff_unique_norm": "Harbin Institute of Technology;Baidu Inc.", "aff_unique_dep": "Research Center for Social Computing and Information Retrieval;", "aff_unique_url": "http://www.hit.edu.cn/;https://www.baidu.com", "aff_unique_abbr": "HIT;Baidu", "aff_campus_unique_index": "0;1;1;1;1;0", "aff_campus_unique": "Harbin;Beijing", "aff_country_unique_index": "0;0;0;0;0;0", "aff_country_unique": "China" }, { "id": "2021.acl-long.189", "title": "Discovering Dialogue Slots with Weak Supervision", "track": "main", "status": "Long", "award": false, "abstract": "Task-oriented dialogue systems typically require manual annotation of dialogue slots in training data, which is costly to obtain. We propose a method that eliminates this requirement: We use weak supervision from existing linguistic annotation models to identify potential slot candidates, then automatically identify domain-relevant slots by using clustering algorithms. Furthermore, we use the resulting slot annotation to train a neural-network-based tagger that is able to perform slot tagging with no human intervention. This tagger is trained solely on the outputs of our method and thus does not rely on any labeled data. Our model demonstrates state-of-the-art performance in slot tagging without labeled training data on four different dialogue domains. Moreover, we find that slot annotations discovered by our model significantly improve the performance of an end-to-end dialogue response generation model, compared to using no slot annotation at all.", "author": "Vojt\u011bch Hude\u010dek; Ond\u0159ej Du\u0161ek; Zhou Yu", "authorids": "/v/vojtech-hudecek/; /o/ondrej-dusek/; /z/zhou-yu/", "bibtex": "@inproceedings{hudecek-etal-2021-discovering,\n title = \"Discovering Dialogue Slots with Weak Supervision\",\n author = \"Hude{\\v{c}}ek, Vojt{\\v{e}}ch and\n Du{\\v{s}}ek, Ond{\\v{r}}ej and\n Yu, Zhou\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.189/\",\n doi = \"10.18653/v1/2021.acl-long.189\",\n pages = \"2430--2442\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.189.pdf", "site": "https://aclanthology.org/2021.acl-long.189/", "pdf_size": 625171, "gs_citation": 28, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=7815448274977505301&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 2, "aff": "Charles University, Faculty of Mathematics and Physics, Institute of Formal and Applied Linguistics; Charles University, Faculty of Mathematics and Physics, Institute of Formal and Applied Linguistics; Columbia University, Department of Computer Science", "aff_domain": "ufal.mff.cuni.cz;ufal.mff.cuni.cz;columbia.edu", "email": "ufal.mff.cuni.cz;ufal.mff.cuni.cz;columbia.edu", "github": "", "project": "", "author_num": 3, "aff_unique_index": "0;0;1", "aff_unique_norm": "Charles University;Columbia University", "aff_unique_dep": "Faculty of Mathematics and Physics, Institute of Formal and Applied Linguistics;Department of Computer Science", "aff_unique_url": "https://www.cuni.cz;https://www.columbia.edu", "aff_unique_abbr": "Charles University;Columbia", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0;0;1", "aff_country_unique": "Czech Republic;United States" }, { "id": "2021.acl-short.53", "title": "Discrete Cosine Transform as Universal Sentence Encoder", "track": "main", "status": "Short", "award": false, "abstract": "Modern sentence encoders are used to generate dense vector representations that capture the underlying linguistic characteristics for a sequence of words, including phrases, sentences, or paragraphs. These kinds of representations are ideal for training a classifier for an end task such as sentiment analysis, question answering and text classification. Different models have been proposed to efficiently generate general purpose sentence representations to be used in pretraining protocols. While averaging is the most commonly used efficient sentence encoder, Discrete Cosine Transform (DCT) was recently proposed as an alternative that captures the underlying syntactic characteristics of a given text without compromising practical efficiency compared to averaging. However, as with most other sentence encoders, the DCT sentence encoder was only evaluated in English. To this end, we utilize DCT encoder to generate universal sentence representation for different languages such as German, French, Spanish and Russian. The experimental results clearly show the superior effectiveness of DCT encoding in which consistent performance improvements are achieved over strong baselines on multiple standardized datasets", "author": "Nada Almarwani; Mona Diab", "authorids": "/n/nada-almarwani/; /m/mona-diab/", "bibtex": "@inproceedings{almarwani-diab-2021-discrete,\n title = \"Discrete Cosine Transform as Universal Sentence Encoder\",\n author = \"Almarwani, Nada and\n Diab, Mona\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-short.53/\",\n doi = \"10.18653/v1/2021.acl-short.53\",\n pages = \"419--426\"\n}", "pdf": "https://aclanthology.org/2021.acl-short.53.pdf", "site": "https://aclanthology.org/2021.acl-short.53/", "pdf_size": 307410, "gs_citation": 3, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=1181382620361587335&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 8, "aff": "Dep. of Computer Science, The George Washington University + Dep. of Computer Science, College of Computer Science and Engineering, Taibah University; Dep. of Computer Science, The George Washington University + Facebook AI Research", "aff_domain": "taibah.edu.sa;fb.com", "email": "taibah.edu.sa;fb.com", "github": "", "project": "", "author_num": 2, "aff_unique_index": "0+1;0+2", "aff_unique_norm": "The George Washington University;Taibah University;Facebook", "aff_unique_dep": "Department of Computer Science;Department of Computer Science;Facebook AI Research", "aff_unique_url": "https://www.gwu.edu;https://www.taibahu.edu.sa;https://research.facebook.com", "aff_unique_abbr": "GWU;;FAIR", "aff_campus_unique_index": ";", "aff_campus_unique": "", "aff_country_unique_index": "0+1;0+0", "aff_country_unique": "United States;Saudi Arabia" }, { "id": "2021.acl-long.563", "title": "Discriminative Reranking for Neural Machine Translation", "track": "main", "status": "Long", "award": false, "abstract": "Reranking models enable the integration of rich features to select a better output hypothesis within an n-best list or lattice. These models have a long history in NLP, and we revisit discriminative reranking for modern neural machine translation models by training a large transformer architecture. This takes as input both the source sentence as well as a list of hypotheses to output a ranked list. The reranker is trained to predict the observed distribution of a desired metric, e.g. BLEU, over the n-best list. Since such a discriminator contains hundreds of millions of parameters, we improve its generalization using pre-training and data augmentation techniques. Experiments on four WMT directions show that our discriminative reranking approach is effective and complementary to existing generative reranking approaches, yielding improvements of up to 4 BLEU over the beam search output.", "author": "Ann Lee; Michael Auli; Marc\u2019Aurelio Ranzato", "authorids": "/a/ann-lee/; /m/michael-auli/; /m/marcaurelio-ranzato/", "bibtex": "@inproceedings{lee-etal-2021-discriminative,\n title = \"Discriminative Reranking for Neural Machine Translation\",\n author = \"Lee, Ann and\n Auli, Michael and\n Ranzato, Marc{'}Aurelio\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.563/\",\n doi = \"10.18653/v1/2021.acl-long.563\",\n pages = \"7250--7264\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.563.pdf", "site": "https://aclanthology.org/2021.acl-long.563/", "pdf_size": 326779, "gs_citation": 51, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=11034667929229295540&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 3, "aff": "Facebook AI Research; Facebook AI Research; Facebook AI Research", "aff_domain": "fb.com;fb.com;fb.com", "email": "fb.com;fb.com;fb.com", "github": "", "project": "", "author_num": 3, "aff_unique_index": "0;0;0", "aff_unique_norm": "Facebook", "aff_unique_dep": "Facebook AI Research", "aff_unique_url": "https://research.facebook.com", "aff_unique_abbr": "FAIR", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0;0;0", "aff_country_unique": "United States" }, { "id": "2021.acl-long.539", "title": "Dissecting Generation Modes for Abstractive Summarization Models via Ablation and Attribution", "track": "main", "status": "Long", "award": false, "abstract": "Despite the prominence of neural abstractive summarization models, we know little about how they actually form summaries and how to understand where their decisions come from. We propose a two-step method to interpret summarization model decisions. We first analyze the model\u2019s behavior by ablating the full model to categorize each decoder decision into one of several generation modes: roughly, is the model behaving like a language model, is it relying heavily on the input, or is it somewhere in between? After isolating decisions that do depend on the input, we explore interpreting these decisions using several different attribution methods. We compare these techniques based on their ability to select content and reconstruct the model\u2019s predicted token from perturbations of the input, thus revealing whether highlighted attributions are truly important for the generation of the next token. While this machinery can be broadly useful even beyond summarization, we specifically demonstrate its capability to identify phrases the summarization model has memorized and determine where in the training pipeline this memorization happened, as well as study complex generation phenomena like sentence fusion on a per-instance basis.", "author": "Jiacheng Xu; Greg Durrett", "authorids": "/j/jiacheng-xu/; /g/greg-durrett/", "bibtex": "@inproceedings{xu-durrett-2021-dissecting,\n title = \"Dissecting Generation Modes for Abstractive Summarization Models via Ablation and Attribution\",\n author = \"Xu, Jiacheng and\n Durrett, Greg\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.539/\",\n doi = \"10.18653/v1/2021.acl-long.539\",\n pages = \"6925--6940\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.539.pdf", "site": "https://aclanthology.org/2021.acl-long.539/", "pdf_size": 1357922, "gs_citation": 17, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=8065753624525147967&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 5, "aff": "Department of Computer Science, The University of Texas at Austin; Department of Computer Science, The University of Texas at Austin", "aff_domain": "cs.utexas.edu;cs.utexas.edu", "email": "cs.utexas.edu;cs.utexas.edu", "github": "https://github.com/jiacheng-xu/sum-interpret", "project": "", "author_num": 2, "aff_unique_index": "0;0", "aff_unique_norm": "The University of Texas at Austin", "aff_unique_dep": "Department of Computer Science", "aff_unique_url": "https://www.utexas.edu", "aff_unique_abbr": "UT Austin", "aff_campus_unique_index": "0;0", "aff_campus_unique": "Austin", "aff_country_unique_index": "0;0", "aff_country_unique": "United States" }, { "id": "2021.acl-short.105", "title": "Distinct Label Representations for Few-Shot Text Classification", "track": "main", "status": "Short", "award": false, "abstract": "Few-shot text classification aims to classify inputs whose label has only a few examples. Previous studies overlooked the semantic relevance between label representations. Therefore, they are easily confused by labels that are relevant. To address this problem, we propose a method that generates distinct label representations that embed information specific to each label. Our method is applicable to conventional few-shot classification models. Experimental results show that our method significantly improved the performance of few-shot text classification across models and datasets.", "author": "Sora Ohashi; Junya Takayama; Tomoyuki Kajiwara; Yuki Arase", "authorids": "/s/sora-ohashi/; /j/junya-takayama/; /t/tomoyuki-kajiwara/; /y/yuki-arase/", "bibtex": "@inproceedings{ohashi-etal-2021-distinct,\n title = \"Distinct Label Representations for Few-Shot Text Classification\",\n author = \"Ohashi, Sora and\n Takayama, Junya and\n Kajiwara, Tomoyuki and\n Arase, Yuki\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-short.105/\",\n doi = \"10.18653/v1/2021.acl-short.105\",\n pages = \"831--836\"\n}", "pdf": "https://aclanthology.org/2021.acl-short.105.pdf", "site": "https://aclanthology.org/2021.acl-short.105/", "pdf_size": 338122, "gs_citation": 13, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=9148146704953922128&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 4, "aff": "Graduate School of Information Science and Technology, Osaka University; Graduate School of Information Science and Technology, Osaka University; Graduate School of Science and Engineering, Ehime University; Graduate School of Information Science and Technology, Osaka University", "aff_domain": "ist.osaka-u.ac.jp;ist.osaka-u.ac.jp;cs.ehime-u.ac.jp;ist.osaka-u.ac.jp", "email": "ist.osaka-u.ac.jp;ist.osaka-u.ac.jp;cs.ehime-u.ac.jp;ist.osaka-u.ac.jp", "github": "", "project": "", "author_num": 4, "aff_unique_index": "0;0;1;0", "aff_unique_norm": "Osaka University;Ehime University", "aff_unique_dep": "Graduate School of Information Science and Technology;Graduate School of Science and Engineering", "aff_unique_url": "https://www.osaka-u.ac.jp;https://www.ehime-u.ac.jp", "aff_unique_abbr": "OU;Ehime U", "aff_campus_unique_index": "0;0;0", "aff_campus_unique": "Osaka;", "aff_country_unique_index": "0;0;0;0", "aff_country_unique": "Japan" }, { "id": "2021.acl-long.184", "title": "Distributed Representations of Emotion Categories in Emotion Space", "track": "main", "status": "Long", "award": false, "abstract": "Emotion category is usually divided into different ones by human beings, but it is indeed difficult to clearly distinguish and define the boundaries between different emotion categories. The existing studies working on emotion detection usually focus on how to improve the performance of model prediction, in which emotions are represented with one-hot vectors. However, emotion relations are ignored in one-hot representations. In this article, we first propose a general framework to learn the distributed representations for emotion categories in emotion space from a given emotion classification dataset. Furthermore, based on the soft labels predicted by the pre-trained neural network model, we derive a simple and effective algorithm. Experiments have validated that the proposed representations in emotion space can express emotion relations much better than word vectors in semantic space.", "author": "Xiangyu Wang; Chengqing Zong", "authorids": "/x/xiangyu-wang/; /c/chengqing-zong/", "bibtex": "@inproceedings{wang-zong-2021-distributed,\n title = \"Distributed Representations of Emotion Categories in Emotion Space\",\n author = \"Wang, Xiangyu and\n Zong, Chengqing\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.184/\",\n doi = \"10.18653/v1/2021.acl-long.184\",\n pages = \"2364--2375\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.184.pdf", "site": "https://aclanthology.org/2021.acl-long.184/", "pdf_size": 1466091, "gs_citation": 19, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=13016030271416327529&as_sdt=5,36&sciodt=0,36&hl=en", "gs_version_total": 3, "aff": "National Laboratory of Pattern Recognition, Institute of Automation, CAS + School of Artificial Intelligence, University of Chinese Academy of Sciences; National Laboratory of Pattern Recognition, Institute of Automation, CAS + School of Artificial Intelligence, University of Chinese Academy of Sciences + CAS Center for Excellence in Brain Science and Intelligence Technology", "aff_domain": "nlpr.ia.ac.cn;nlpr.ia.ac.cn", "email": "nlpr.ia.ac.cn;nlpr.ia.ac.cn", "github": "", "project": "", "author_num": 2, "aff_unique_index": "0+1;0+1+0", "aff_unique_norm": "Chinese Academy of Sciences;University of Chinese Academy of Sciences", "aff_unique_dep": "Institute of Automation;School of Artificial Intelligence", "aff_unique_url": "http://www.ia.cas.cn;http://www.ucas.ac.cn", "aff_unique_abbr": "CAS;UCAS", "aff_campus_unique_index": ";", "aff_campus_unique": "", "aff_country_unique_index": "0+0;0+0+0", "aff_country_unique": "China" }, { "id": "2021.acl-long.104", "title": "Diverse Pretrained Context Encodings Improve Document Translation", "track": "main", "status": "Long", "award": false, "abstract": "We propose a new architecture for adapting a sentence-level sequence-to-sequence transformer by incorporating multiple pre-trained document context signals and assess the impact on translation performance of (1) different pretraining approaches for generating these signals, (2) the quantity of parallel data for which document context is available, and (3) conditioning on source, target, or source and target contexts. Experiments on the NIST Chinese-English, and IWSLT and WMT English-German tasks support four general conclusions: that using pre-trained context representations markedly improves sample efficiency, that adequate parallel data resources are crucial for learning to use document context, that jointly conditioning on multiple context representations outperforms any single representation, and that source context is more valuable for translation performance than target side context. Our best multi-context model consistently outperforms the best existing context-aware transformers.", "author": "Domenic Donato; Lei Yu; Chris Dyer", "authorids": "/d/domenic-donato/; /l/lei-yu/; /c/chris-dyer/", "bibtex": "@inproceedings{donato-etal-2021-diverse,\n title = \"Diverse Pretrained Context Encodings Improve Document Translation\",\n author = \"Donato, Domenic and\n Yu, Lei and\n Dyer, Chris\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.104/\",\n doi = \"10.18653/v1/2021.acl-long.104\",\n pages = \"1299--1311\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.104.pdf", "site": "https://aclanthology.org/2021.acl-long.104/", "pdf_size": 661114, "gs_citation": 12, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=1658832879571134248&as_sdt=5,44&sciodt=0,44&hl=en", "gs_version_total": 4, "aff": "DeepMind; DeepMind; DeepMind", "aff_domain": "deepmind.com;deepmind.com;deepmind.com", "email": "deepmind.com;deepmind.com;deepmind.com", "github": "", "project": "", "author_num": 3, "aff_unique_index": "0;0;0", "aff_unique_norm": "DeepMind", "aff_unique_dep": "", "aff_unique_url": "https://deepmind.com", "aff_unique_abbr": "DeepMind", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0;0;0", "aff_country_unique": "United Kingdom" }, { "id": "2021.acl-long.272", "title": "Diversifying Dialog Generation via Adaptive Label Smoothing", "track": "main", "status": "Long", "award": false, "abstract": "Neural dialogue generation models trained with the one-hot target distribution suffer from the over-confidence issue, which leads to poor generation diversity as widely reported in the literature. Although existing approaches such as label smoothing can alleviate this issue, they fail to adapt to diverse dialog contexts. In this paper, we propose an Adaptive Label Smoothing (AdaLabel) approach that can adaptively estimate a target label distribution at each time step for different contexts. The maximum probability in the predicted distribution is used to modify the soft target distribution produced by a novel light-weight bi-directional decoder module. The resulting target distribution is aware of both previous and future contexts and is adjusted to avoid over-training the dialogue model. Our model can be trained in an endto-end manner. Extensive experiments on two benchmark datasets show that our approach outperforms various competitive baselines in producing diverse responses.", "author": "Yida Wang; Yinhe Zheng; Yong Jiang; Minlie Huang", "authorids": "/y/yida-wang/; /y/yinhe-zheng/; /y/yong-jiang/; /m/minlie-huang/", "bibtex": "@inproceedings{wang-etal-2021-diversifying,\n title = \"Diversifying Dialog Generation via Adaptive Label Smoothing\",\n author = \"Wang, Yida and\n Zheng, Yinhe and\n Jiang, Yong and\n Huang, Minlie\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.272/\",\n doi = \"10.18653/v1/2021.acl-long.272\",\n pages = \"3507--3520\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.272.pdf", "site": "https://aclanthology.org/2021.acl-long.272/", "pdf_size": 900406, "gs_citation": 43, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=3242631292959465364&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 4, "aff": "The CoAI group, DCST, Institute for Artificial Intelligence, State Key Lab of Intelligent Technology and Systems, Beijing National Research Center for Information Science and Technology, Tsinghua University, Beijing, China+Tsinghua-Berkeley Shenzhen Institute, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, China; The CoAI group, DCST, Institute for Artificial Intelligence, State Key Lab of Intelligent Technology and Systems, Beijing National Research Center for Information Science and Technology, Tsinghua University, Beijing, China+Samsung Research China - Beijing (SRC-B); Tsinghua-Berkeley Shenzhen Institute, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, China; The CoAI group, DCST, Institute for Artificial Intelligence, State Key Lab of Intelligent Technology and Systems, Beijing National Research Center for Information Science and Technology, Tsinghua University, Beijing, China", "aff_domain": "mails.tsinghua.edu.cn;samsung.com;sz.tsinghua.edu.cn;tsinghua.edu.cn", "email": "mails.tsinghua.edu.cn;samsung.com;sz.tsinghua.edu.cn;tsinghua.edu.cn", "github": "", "project": "", "author_num": 4, "aff_unique_index": "0+0;0+1;0;0", "aff_unique_norm": "Tsinghua University;Samsung Research China", "aff_unique_dep": "Institute for Artificial Intelligence;", "aff_unique_url": "https://www.tsinghua.edu.cn;https://www.samsung.com/cn/research/", "aff_unique_abbr": "Tsinghua;SRC-B", "aff_campus_unique_index": "0+1;0+0;1;0", "aff_campus_unique": "Beijing;Shenzhen", "aff_country_unique_index": "0+0;0+0;0;0", "aff_country_unique": "China" }, { "id": "2021.acl-long.65", "title": "Do Context-Aware Translation Models Pay the Right Attention?", "track": "main", "status": "Long", "award": false, "abstract": "Context-aware machine translation models are designed to leverage contextual information, but often fail to do so. As a result, they inaccurately disambiguate pronouns and polysemous words that require context for resolution. In this paper, we ask several questions: What contexts do human translators use to resolve ambiguous words? Are models paying large amounts of attention to the same context? What if we explicitly train them to do so? To answer these questions, we introduce SCAT (Supporting Context for Ambiguous Translations), a new English-French dataset comprising supporting context words for 14K translations that professional translators found useful for pronoun disambiguation. Using SCAT, we perform an in-depth analysis of the context used to disambiguate, examining positional and lexical characteristics of the supporting words. Furthermore, we measure the degree of alignment between the model\u2019s attention scores and the supporting context from SCAT, and apply a guided attention strategy to encourage agreement between the two.", "author": "Kayo Yin; Patrick Fernandes; Danish Pruthi; Aditi Chaudhary; Andr\u00e9 F. T. Martins; Graham Neubig", "authorids": "/k/kayo-yin/; /p/patrick-fernandes/; /d/danish-pruthi/; /a/aditi-chaudhary/; /a/andre-f-t-martins/; /g/graham-neubig/", "bibtex": "@inproceedings{yin-etal-2021-context,\n title = \"Do Context-Aware Translation Models Pay the Right Attention?\",\n author = \"Yin, Kayo and\n Fernandes, Patrick and\n Pruthi, Danish and\n Chaudhary, Aditi and\n Martins, Andr{\\'e} F. T. and\n Neubig, Graham\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.65/\",\n doi = \"10.18653/v1/2021.acl-long.65\",\n pages = \"788--801\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.65.pdf", "site": "https://aclanthology.org/2021.acl-long.65/", "pdf_size": 695845, "gs_citation": 37, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=13579968618430837370&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 5, "aff": "Language Technologies Institute, Carnegie Mellon University, Pittsburgh, PA; Language Technologies Institute, Carnegie Mellon University, Pittsburgh, PA + Instituto Superior T \u00b4ecnico & LUMLIS (Lisbon ELLIS Unit), Lisbon, Portugal + Instituto de Telecomunicac \u00b8 \u02dcoes, Lisbon, Portugal; Language Technologies Institute, Carnegie Mellon University, Pittsburgh, PA; Language Technologies Institute, Carnegie Mellon University, Pittsburgh, PA; Instituto Superior T \u00b4ecnico & LUMLIS (Lisbon ELLIS Unit), Lisbon, Portugal + Instituto de Telecomunicac \u00b8 \u02dcoes, Lisbon, Portugal + Unbabel, Lisbon, Portugal; Language Technologies Institute, Carnegie Mellon University, Pittsburgh, PA", "aff_domain": "cs.cmu.edu;cs.cmu.edu;cs.cmu.edu;cs.cmu.edu;tecnico.ulisboa.pt;cs.cmu.edu", "email": "cs.cmu.edu;cs.cmu.edu;cs.cmu.edu;cs.cmu.edu;tecnico.ulisboa.pt;cs.cmu.edu", "github": "https://github.com/neulab/contextual-mt", "project": "", "author_num": 6, "aff_unique_index": "0;0+1+2;0;0;1+2+3;0", "aff_unique_norm": "Carnegie Mellon University;Instituto Superior T\u00e9cnico;Instituto de Telecomunica\u00e7\u00f5es;Unbabel", "aff_unique_dep": "Language Technologies Institute;;;", "aff_unique_url": "https://www.cmu.edu;https://www.ist.utl.pt;https://www.it.pt;https://www.unbabel.com", "aff_unique_abbr": "CMU;IST;;", "aff_campus_unique_index": "0;0+1+1;0;0;1+1;0", "aff_campus_unique": "Pittsburgh;Lisbon;", "aff_country_unique_index": "0;0+1+1;0;0;1+1+1;0", "aff_country_unique": "United States;Portugal" }, { "id": "2021.acl-long.274", "title": "Document-level Event Extraction via Heterogeneous Graph-based Interaction Model with a Tracker", "track": "main", "status": "Long", "award": false, "abstract": "Document-level event extraction aims to recognize event information from a whole piece of article. Existing methods are not effective due to two challenges of this task: a) the target event arguments are scattered across sentences; b) the correlation among events in a document is non-trivial to model. In this paper, we propose Heterogeneous Graph-based Interaction Model with a Tracker (GIT) to solve the aforementioned two challenges. For the first challenge, GIT constructs a heterogeneous graph interaction network to capture global interactions among different sentences and entity mentions. For the second, GIT introduces a Tracker module to track the extracted events and hence capture the interdependency among the events. Experiments on a large-scale dataset (Zheng et al, 2019) show GIT outperforms the previous methods by 2.8 F1. Further analysis reveals is effective in extracting multiple correlated events and event arguments that scatter across the document.", "author": "Runxin Xu; Tianyu Liu; Lei Li; Baobao Chang", "authorids": "/r/runxin-xu/; /t/tianyu-liu/; /l/lei-li/; /b/baobao-chang/", "bibtex": "@inproceedings{xu-etal-2021-document,\n title = \"Document-level Event Extraction via Heterogeneous Graph-based Interaction Model with a Tracker\",\n author = \"Xu, Runxin and\n Liu, Tianyu and\n Li, Lei and\n Chang, Baobao\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.274/\",\n doi = \"10.18653/v1/2021.acl-long.274\",\n pages = \"3533--3546\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.274.pdf", "site": "https://aclanthology.org/2021.acl-long.274/", "pdf_size": 843411, "gs_citation": 127, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=134624455906212773&as_sdt=5,33&sciodt=0,33&hl=en", "gs_version_total": 5, "aff": "Key Laboratory of Computational Linguistics, Peking University, MOE, China+Peng Cheng Laboratory, Shenzhen, China; Key Laboratory of Computational Linguistics, Peking University, MOE, China; ByteDance AI Lab; Key Laboratory of Computational Linguistics, Peking University, MOE, China+Peng Cheng Laboratory, Shenzhen, China", "aff_domain": "gmail.com;pku.edu.cn;bytedance.com;pku.edu.cn", "email": "gmail.com;pku.edu.cn;bytedance.com;pku.edu.cn", "github": "https://github.com/RunxinXu/GIT", "project": "", "author_num": 4, "aff_unique_index": "0+1;0;2;0+1", "aff_unique_norm": "Peking University;Peng Cheng Laboratory;ByteDance", "aff_unique_dep": "Key Laboratory of Computational Linguistics;;AI Lab", "aff_unique_url": "http://www.pku.edu.cn;;https://www.bytedance.com", "aff_unique_abbr": "PKU;;ByteDance", "aff_campus_unique_index": "1;1", "aff_campus_unique": ";Shenzhen", "aff_country_unique_index": "0+0;0;0;0+0", "aff_country_unique": "China" }, { "id": "2021.acl-long.492", "title": "Document-level Event Extraction via Parallel Prediction Networks", "track": "main", "status": "Long", "award": false, "abstract": "Document-level event extraction (DEE) is indispensable when events are described throughout a document. We argue that sentence-level extractors are ill-suited to the DEE task where event arguments always scatter across sentences and multiple events may co-exist in a document. It is a challenging task because it requires a holistic understanding of the document and an aggregated ability to assemble arguments across multiple sentences. In this paper, we propose an end-to-end model, which can extract structured events from a document in a parallel manner. Specifically, we first introduce a document-level encoder to obtain the document-aware representations. Then, a multi-granularity non-autoregressive decoder is used to generate events in parallel. Finally, to train the entire model, a matching loss function is proposed, which can bootstrap a global optimization. The empirical results on the widely used DEE dataset show that our approach significantly outperforms current state-of-the-art methods in the challenging DEE task. Code will be available at https://github.com/HangYang-NLP/DE-PPN.", "author": "Hang Yang; Dianbo Sui; Yubo Chen; Kang Liu; Jun Zhao; Taifeng Wang", "authorids": "/h/hang-yang/; /d/dianbo-sui/; /y/yubo-chen/; /k/kang-liu/; /j/jun-zhao/; /t/taifeng-wang/", "bibtex": "@inproceedings{yang-etal-2021-document,\n title = \"Document-level Event Extraction via Parallel Prediction Networks\",\n author = \"Yang, Hang and\n Sui, Dianbo and\n Chen, Yubo and\n Liu, Kang and\n Zhao, Jun and\n Wang, Taifeng\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.492/\",\n doi = \"10.18653/v1/2021.acl-long.492\",\n pages = \"6298--6308\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.492.pdf", "site": "https://aclanthology.org/2021.acl-long.492/", "pdf_size": 652781, "gs_citation": 93, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=11393698653557440554&as_sdt=5,33&sciodt=0,33&hl=en", "gs_version_total": 2, "aff": "National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China+School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, 100049, China; National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China+School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, 100049, China; National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China+School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, 100049, China; National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China+School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, 100049, China; National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China+School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, 100049, China; Ant Group, Hangzhou, 310013, China", "aff_domain": "nlpr.ia.ac.cn;nlpr.ia.ac.cn;nlpr.ia.ac.cn;nlpr.ia.ac.cn;nlpr.ia.ac.cn;antgroup.com", "email": "nlpr.ia.ac.cn;nlpr.ia.ac.cn;nlpr.ia.ac.cn;nlpr.ia.ac.cn;nlpr.ia.ac.cn;antgroup.com", "github": "https://github.com/HangYang-NLP/DE-PPN", "project": "", "author_num": 6, "aff_unique_index": "0+1;0+1;0+1;0+1;0+1;2", "aff_unique_norm": "Chinese Academy of Sciences;University of Chinese Academy of Sciences;Ant Group", "aff_unique_dep": "Institute of Automation;School of Artificial Intelligence;", "aff_unique_url": "http://www.ia.cas.cn;http://www.ucas.ac.cn;https://www.antgroup.com", "aff_unique_abbr": "CAS;UCAS;Ant Group", "aff_campus_unique_index": "0+0;0+0;0+0;0+0;0+0;1", "aff_campus_unique": "Beijing;Hangzhou", "aff_country_unique_index": "0+0;0+0;0+0;0+0;0+0;0", "aff_country_unique": "China" }, { "id": "2021.acl-short.20", "title": "Doing Good or Doing Right? Exploring the Weakness of Commonsense Causal Reasoning Models", "track": "main", "status": "Short", "award": false, "abstract": "Pretrained language models (PLM) achieve surprising performance on the Choice of Plausible Alternatives (COPA) task. However, whether PLMs have truly acquired the ability of causal reasoning remains a question. In this paper, we investigate the problem of semantic similarity bias and reveal the vulnerability of current COPA models by certain attacks. Previous solutions that tackle the superficial cues of unbalanced token distribution still encounter the same problem of semantic bias, even more seriously due to the utilization of more training data. We mitigate this problem by simply adding a regularization loss and experimental results show that this solution not only improves the model\u2019s generalization ability, but also assists the models to perform more robustly on a challenging dataset, BCOPA-CE, which has unbiased token distribution and is more difficult for models to distinguish cause and effect.", "author": "Mingyue Han; Yinglin Wang", "authorids": "/m/mingyue-han/; /y/yinglin-wang/", "bibtex": "@inproceedings{han-wang-2021-good,\n title = \"Doing Good or Doing Right? Exploring the Weakness of Commonsense Causal Reasoning Models\",\n author = \"Han, Mingyue and\n Wang, Yinglin\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-short.20/\",\n doi = \"10.18653/v1/2021.acl-short.20\",\n pages = \"151--157\"\n}", "pdf": "https://aclanthology.org/2021.acl-short.20.pdf", "site": "https://aclanthology.org/2021.acl-short.20/", "pdf_size": 1341224, "gs_citation": 11, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=15319510821831721300&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 4, "aff": "School of Information Management and Engineering, Shanghai University of Finance and Economics, Shanghai, China; School of Information Management and Engineering, Shanghai University of Finance and Economics, Shanghai, China", "aff_domain": "163.sufe.edu.cn;shufe.edu.cn", "email": "163.sufe.edu.cn;shufe.edu.cn", "github": "", "project": "", "author_num": 2, "aff_unique_index": "0;0", "aff_unique_norm": "Shanghai University of Finance and Economics", "aff_unique_dep": "School of Information Management and Engineering", "aff_unique_url": "http://www.sufe.edu.cn", "aff_unique_abbr": "SUFE", "aff_campus_unique_index": "0;0", "aff_campus_unique": "Shanghai", "aff_country_unique_index": "0;0", "aff_country_unique": "China" }, { "id": "2021.acl-short.84", "title": "Domain-Adaptive Pretraining Methods for Dialogue Understanding", "track": "main", "status": "Short", "award": false, "abstract": "Language models like BERT and SpanBERT pretrained on open-domain data have obtained impressive gains on various NLP tasks. In this paper, we probe the effectiveness of domain-adaptive pretraining objectives on downstream tasks. In particular, three objectives, including a novel objective focusing on modeling predicate-argument relations, are evaluated on two challenging dialogue understanding tasks. Experimental results demonstrate that domain-adaptive pretraining with proper objectives can significantly improve the performance of a strong baseline on these tasks, achieving the new state-of-the-art performances.", "author": "Han Wu; Kun Xu; Linfeng Song; Lifeng Jin; Haisong Zhang; Linqi Song", "authorids": "/h/han-wu/; /k/kun-xu/; /l/linfeng-song/; /l/lifeng-jin/; /h/haisong-zhang/; /l/linqi-song/", "bibtex": "@inproceedings{wu-etal-2021-domain,\n title = \"Domain-Adaptive Pretraining Methods for Dialogue Understanding\",\n author = \"Wu, Han and\n Xu, Kun and\n Song, Linfeng and\n Jin, Lifeng and\n Zhang, Haisong and\n Song, Linqi\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-short.84/\",\n doi = \"10.18653/v1/2021.acl-short.84\",\n pages = \"665--669\"\n}", "pdf": "https://aclanthology.org/2021.acl-short.84.pdf", "site": "https://aclanthology.org/2021.acl-short.84/", "pdf_size": 289923, "gs_citation": 25, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=6477919754960344839&as_sdt=5,33&sciodt=0,33&hl=en", "gs_version_total": 7, "aff": "Department of Computer Science, City University of Hong Kong; Tencent AI Lab; Tencent AI Lab; Tencent AI Lab; Tencent AI Lab; Department of Computer Science, City University of Hong Kong", "aff_domain": "my.cityu.edu.hk;tencent.com;tencent.com;tencent.com;tencent.com;cityu.edu.hk", "email": "my.cityu.edu.hk;tencent.com;tencent.com;tencent.com;tencent.com;cityu.edu.hk", "github": "", "project": "", "author_num": 6, "aff_unique_index": "0;1;1;1;1;0", "aff_unique_norm": "City University of Hong Kong;Tencent", "aff_unique_dep": "Department of Computer Science;Tencent AI Lab", "aff_unique_url": "https://www.cityu.edu.hk;https://ai.tencent.com", "aff_unique_abbr": "CityU;Tencent AI Lab", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0;0;0;0;0;0", "aff_country_unique": "China" }, { "id": "2021.acl-short.76", "title": "Don\u2019t Let Discourse Confine Your Model: Sequence Perturbations for Improved Event Language Models", "track": "main", "status": "Short", "award": false, "abstract": "Event language models represent plausible sequences of events. Most existing approaches train autoregressive models on text, which successfully capture event co-occurrence but unfortunately constrain the model to follow the discourse order in which events are presented. Other domains may employ different discourse orders, and for many applications, we may care about different notions of ordering (e.g., temporal) or not care about ordering at all (e.g., when predicting related events in a schema). We propose a simple yet surprisingly effective strategy for improving event language models by perturbing event sequences so we can relax model dependence on text order. Despite generating completely synthetic event orderings, we show that this technique improves the performance of the event language models on both applications and out-of-domain events data.", "author": "Mahnaz Koupaee; Greg Durrett; Nathanael Chambers; Niranjan Balasubramanian", "authorids": "/m/mahnaz-koupaee/; /g/greg-durrett/; /n/nathanael-chambers/; /n/niranjan-balasubramanian/", "bibtex": "@inproceedings{koupaee-etal-2021-dont,\n title = \"Don`t Let Discourse Confine Your Model: Sequence Perturbations for Improved Event Language Models\",\n author = \"Koupaee, Mahnaz and\n Durrett, Greg and\n Chambers, Nathanael and\n Balasubramanian, Niranjan\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-short.76/\",\n doi = \"10.18653/v1/2021.acl-short.76\",\n pages = \"599--604\"\n}", "pdf": "https://aclanthology.org/2021.acl-short.76.pdf", "site": "https://aclanthology.org/2021.acl-short.76/", "pdf_size": 679490, "gs_citation": 5, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=9078960115141747108&as_sdt=80005&sciodt=0,11&hl=en", "gs_version_total": 0, "aff": "Stony Brook University; The University of Texas at Austin; United States Naval Academy; Stony Brook University", "aff_domain": "cs.stonybrook.edu;cs.utexas.edu;usna.edu;cs.stonybrook.edu", "email": "cs.stonybrook.edu;cs.utexas.edu;usna.edu;cs.stonybrook.edu", "github": "https://github.com/StonyBrookNLP/elm-perturbations", "project": "", "author_num": 4, "aff_unique_index": "0;1;2;0", "aff_unique_norm": "Stony Brook University;University of Texas at Austin;United States Naval Academy", "aff_unique_dep": ";;", "aff_unique_url": "https://www.stonybrook.edu;https://www.utexas.edu;https://www.usna.edu", "aff_unique_abbr": "SBU;UT Austin;USNA", "aff_campus_unique_index": "1", "aff_campus_unique": ";Austin", "aff_country_unique_index": "0;0;0;0", "aff_country_unique": "United States" }, { "id": "2021.acl-short.139", "title": "Don\u2019t Rule Out Monolingual Speakers: A Method For Crowdsourcing Machine Translation Data", "track": "main", "status": "Short", "award": false, "abstract": "High-performing machine translation (MT) systems can help overcome language barriers while making it possible for everyone to communicate and use language technologies in the language of their choice. However, such systems require large amounts of parallel sentences for training, and translators can be difficult to find and expensive. Here, we present a data collection strategy for MT which, in contrast, is cheap and simple, as it does not require bilingual speakers. Based on the insight that humans pay specific attention to movements, we use graphics interchange formats (GIFs) as a pivot to collect parallel sentences from monolingual annotators. We use our strategy to collect data in Hindi, Tamil and English. As a baseline, we also collect data using images as a pivot. We perform an intrinsic evaluation by manually evaluating a subset of the sentence pairs and an extrinsic evaluation by finetuning mBART (Liu et al., 2020) on the collected data. We find that sentences collected via GIFs are indeed of higher quality.", "author": "Rajat Bhatnagar; Ananya Ganesh; Katharina Kann", "authorids": "/r/rajat-bhatnagar/; /a/ananya-ganesh/; /k/katharina-von-der-wense/", "bibtex": "@inproceedings{bhatnagar-etal-2021-dont,\n title = \"Don`t Rule Out Monolingual Speakers: {A} Method For Crowdsourcing Machine Translation Data\",\n author = \"Bhatnagar, Rajat and\n Ganesh, Ananya and\n Kann, Katharina\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-short.139/\",\n doi = \"10.18653/v1/2021.acl-short.139\",\n pages = \"1099--1106\"\n}", "pdf": "https://aclanthology.org/2021.acl-short.139.pdf", "site": "https://aclanthology.org/2021.acl-short.139/", "pdf_size": 1232090, "gs_citation": 2, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=13426202319139058583&as_sdt=5,33&sciodt=0,33&hl=en", "gs_version_total": 4, "aff": "University of Colorado Boulder; University of Colorado Boulder; University of Colorado Boulder", "aff_domain": "colorado.edu;colorado.edu;colorado.edu", "email": "colorado.edu;colorado.edu;colorado.edu", "github": "", "project": "", "author_num": 3, "aff_unique_index": "0;0;0", "aff_unique_norm": "University of Colorado", "aff_unique_dep": "", "aff_unique_url": "https://www.colorado.edu", "aff_unique_abbr": "CU Boulder", "aff_campus_unique_index": "0;0;0", "aff_campus_unique": "Boulder", "aff_country_unique_index": "0;0;0", "aff_country_unique": "United States" }, { "id": "2021.acl-short.120", "title": "DuReader_robust: A Chinese Dataset Towards Evaluating Robustness and Generalization of Machine Reading Comprehension in Real-World Applications", "track": "main", "status": "Short", "award": false, "abstract": "Machine reading comprehension (MRC) is a crucial task in natural language processing and has achieved remarkable advancements. However, most of the neural MRC models are still far from robust and fail to generalize well in real-world applications. In order to comprehensively verify the robustness and generalization of MRC models, we introduce a real-world Chinese dataset \u2013 DuReader_robust . It is designed to evaluate the MRC models from three aspects: over-sensitivity, over-stability and generalization. Comparing to previous work, the instances in DuReader_robust are natural texts, rather than the altered unnatural texts. It presents the challenges when applying MRC models to real-world applications. The experimental results show that MRC models do not perform well on the challenge test set. Moreover, we analyze the behavior of existing models on the challenge test set, which may provide suggestions for future model development. The dataset and codes are publicly available at https://github.com/baidu/DuReader.", "author": "Hongxuan Tang; Hongyu Li; Jing Liu; Yu Hong; Hua Wu; Haifeng Wang", "authorids": "/h/hongxuan-tang/; /h/hongyu-li/; /j/jing-liu/; /y/yu-hong/; /h/hua-wu/; /h/haifeng-wang/", "bibtex": "@inproceedings{tang-etal-2021-dureader,\n title = \"{D}u{R}eader{\\_}robust: A {C}hinese Dataset Towards Evaluating Robustness and Generalization of Machine Reading Comprehension in Real-World Applications\",\n author = \"Tang, Hongxuan and\n Li, Hongyu and\n Liu, Jing and\n Hong, Yu and\n Wu, Hua and\n Wang, Haifeng\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-short.120/\",\n doi = \"10.18653/v1/2021.acl-short.120\",\n pages = \"955--963\"\n}", "pdf": "https://aclanthology.org/2021.acl-short.120.pdf", "site": "https://aclanthology.org/2021.acl-short.120/", "pdf_size": 706090, "gs_citation": 21, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=9753578306563835055&as_sdt=5,48&sciodt=0,48&hl=en", "gs_version_total": 3, "aff": "School of Computer Science and Technology, Soochow University; Baidu Inc.; Baidu Inc.; School of Computer Science and Technology, Soochow University; Baidu Inc.; Baidu Inc.", "aff_domain": "gmail.com;Baidu.com;Baidu.com;gmail.com;Baidu.com;Baidu.com", "email": "gmail.com;Baidu.com;Baidu.com;gmail.com;Baidu.com;Baidu.com", "github": "https://github.com/baidu/DuReader", "project": "", "author_num": 6, "aff_unique_index": "0;1;1;0;1;1", "aff_unique_norm": "Soochow University;Baidu Inc.", "aff_unique_dep": "School of Computer Science and Technology;", "aff_unique_url": "https://eng.suda.edu.cn/;https://www.baidu.com", "aff_unique_abbr": "Soochow U;Baidu", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0;0;0;0;0;0", "aff_country_unique": "China" }, { "id": "2021.acl-long.494", "title": "Dual Graph Convolutional Networks for Aspect-based Sentiment Analysis", "track": "main", "status": "Long", "award": false, "abstract": "Aspect-based sentiment analysis is a fine-grained sentiment classification task. Recently, graph neural networks over dependency trees have been explored to explicitly model connections between aspects and opinion words. However, the improvement is limited due to the inaccuracy of the dependency parsing results and the informal expressions and complexity of online reviews. To overcome these challenges, in this paper, we propose a dual graph convolutional networks (DualGCN) model that considers the complementarity of syntax structures and semantic correlations simultaneously. Particularly, to alleviate dependency parsing errors, we design a SynGCN module with rich syntactic knowledge. To capture semantic correlations, we design a SemGCN module with self-attention mechanism. Furthermore, we propose orthogonal and differential regularizers to capture semantic correlations between words precisely by constraining attention scores in the SemGCN module. The orthogonal regularizer encourages the SemGCN to learn semantically correlated words with less overlap for each word. The differential regularizer encourages the SemGCN to learn semantic features that the SynGCN fails to capture. Experimental results on three public datasets show that our DualGCN model outperforms state-of-the-art methods and verify the effectiveness of our model.", "author": "Ruifan Li; Hao Chen; Fangxiang Feng; Zhanyu Ma; Xiaojie Wang; Eduard Hovy", "authorids": "/r/ruifan-li/; /h/hao-chen/; /f/fangxiang-feng/; /z/zhanyu-ma/; /x/xiaojie-wang/; /e/eduard-hovy/", "bibtex": "@inproceedings{li-etal-2021-dual-graph,\n title = \"Dual Graph Convolutional Networks for Aspect-based Sentiment Analysis\",\n author = \"Li, Ruifan and\n Chen, Hao and\n Feng, Fangxiang and\n Ma, Zhanyu and\n Wang, Xiaojie and\n Hovy, Eduard\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.494/\",\n doi = \"10.18653/v1/2021.acl-long.494\",\n pages = \"6319--6329\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.494.pdf", "site": "https://aclanthology.org/2021.acl-long.494/", "pdf_size": 1773126, "gs_citation": 427, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=8914668183460589287&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 5, "aff": "School of Artificial Intelligence, Beijing University of Posts and Telecommunications, China; School of Artificial Intelligence, Beijing University of Posts and Telecommunications, China; School of Artificial Intelligence, Beijing University of Posts and Telecommunications, China; School of Artificial Intelligence, Beijing University of Posts and Telecommunications, China; School of Artificial Intelligence, Beijing University of Posts and Telecommunications, China; Language Technologies Institute, Carnegie Mellon University, USA", "aff_domain": "bupt.edu.cn;bupt.edu.cn;bupt.edu.cn;bupt.edu.cn;bupt.edu.cn;cmu.edu", "email": "bupt.edu.cn;bupt.edu.cn;bupt.edu.cn;bupt.edu.cn;bupt.edu.cn;cmu.edu", "github": "", "project": "", "author_num": 6, "aff_unique_index": "0;0;0;0;0;1", "aff_unique_norm": "Beijing University of Posts and Telecommunications;Carnegie Mellon University", "aff_unique_dep": "School of Artificial Intelligence;Language Technologies Institute", "aff_unique_url": "http://www.bupt.edu.cn/;https://www.cmu.edu", "aff_unique_abbr": "BUPT;CMU", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0;0;0;0;0;1", "aff_country_unique": "China;United States" }, { "id": "2021.acl-long.315", "title": "Dual Reader-Parser on Hybrid Textual and Tabular Evidence for Open Domain Question Answering", "track": "main", "status": "Long", "award": false, "abstract": "The current state-of-the-art generative models for open-domain question answering (ODQA) have focused on generating direct answers from unstructured textual information. However, a large amount of world\u2019s knowledge is stored in structured databases, and need to be accessed using query languages such as SQL. Furthermore, query languages can answer questions that require complex reasoning, as well as offering full explainability. In this paper, we propose a hybrid framework that takes both textual and tabular evidences as input and generates either direct answers or SQL queries depending on which form could better answer the question. The generated SQL queries can then be executed on the associated databases to obtain the final answers. To the best of our knowledge, this is the first paper that applies Text2SQL to ODQA tasks. Empirically, we demonstrate that on several ODQA datasets, the hybrid methods consistently outperforms the baseline models that only takes homogeneous input by a large margin. Specifically we achieve the state-of-the-art performance on OpenSQuAD dataset using a T5-base model. In a detailed analysis, we demonstrate that the being able to generate structural SQL queries can always bring gains, especially for those questions that requires complex reasoning.", "author": "Alexander Hanbo Li; Patrick Ng; Peng Xu; Henghui Zhu; Zhiguo Wang; Bing Xiang", "authorids": "/a/alexander-hanbo-li/; /p/patrick-ng/; /p/peng-xu/; /h/henghui-zhu/; /z/zhiguo-wang/; /b/bing-xiang/", "bibtex": "@inproceedings{li-etal-2021-dual,\n title = \"Dual Reader-Parser on Hybrid Textual and Tabular Evidence for Open Domain Question Answering\",\n author = \"Li, Alexander Hanbo and\n Ng, Patrick and\n Xu, Peng and\n Zhu, Henghui and\n Wang, Zhiguo and\n Xiang, Bing\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.315/\",\n doi = \"10.18653/v1/2021.acl-long.315\",\n pages = \"4078--4088\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.315.pdf", "site": "https://aclanthology.org/2021.acl-long.315/", "pdf_size": 1317716, "gs_citation": 34, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=3736881855971195924&as_sdt=5,44&sciodt=0,44&hl=en", "gs_version_total": 5, "aff": "AWS AI Labs, Amazon; AWS AI Labs, Amazon; AWS AI Labs, Amazon; AWS AI Labs, Amazon; AWS AI Labs, Amazon; AWS AI Labs, Amazon", "aff_domain": "amazon.com;amazon.com;amazon.com;amazon.com;amazon.com;amazon.com", "email": "amazon.com;amazon.com;amazon.com;amazon.com;amazon.com;amazon.com", "github": "https://github.com/AlexanderYogurt/Hybrid-Open-QA", "project": "", "author_num": 6, "aff_unique_index": "0;0;0;0;0;0", "aff_unique_norm": "Amazon", "aff_unique_dep": "AWS AI Labs", "aff_unique_url": "https://aws.amazon.com", "aff_unique_abbr": "Amazon", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0;0;0;0;0;0", "aff_country_unique": "United States" }, { "id": "2021.acl-long.12", "title": "Dual Slot Selector via Local Reliability Verification for Dialogue State Tracking", "track": "main", "status": "Long", "award": false, "abstract": "The goal of dialogue state tracking (DST) is to predict the current dialogue state given all previous dialogue contexts. Existing approaches generally predict the dialogue state at every turn from scratch. However, the overwhelming majority of the slots in each turn should simply inherit the slot values from the previous turn. Therefore, the mechanism of treating slots equally in each turn not only is inefficient but also may lead to additional errors because of the redundant slot value generation. To address this problem, we devise the two-stage DSS-DST which consists of the Dual Slot Selector based on the current turn dialogue, and the Slot Value Generator based on the dialogue history. The Dual Slot Selector determines each slot whether to update slot value or to inherit the slot value from the previous turn from two aspects: (1) if there is a strong relationship between it and the current turn dialogue utterances; (2) if a slot value with high reliability can be obtained for it through the current turn dialogue. The slots selected to be updated are permitted to enter the Slot Value Generator to update values by a hybrid method, while the other slots directly inherit the values from the previous turn. Empirical results show that our method achieves 56.93%, 60.73%, and 58.04% joint accuracy on MultiWOZ 2.0, MultiWOZ 2.1, and MultiWOZ 2.2 datasets respectively and achieves a new state-of-the-art performance with significant improvements.", "author": "Jinyu Guo; Kai Shuang; Jijie Li; Zihan Wang", "authorids": "/j/jinyu-guo/; /k/kai-shuang/; /j/jijie-li/; /z/zihan-wang/", "bibtex": "@inproceedings{guo-etal-2021-dual,\n title = \"Dual Slot Selector via Local Reliability Verification for Dialogue State Tracking\",\n author = \"Guo, Jinyu and\n Shuang, Kai and\n Li, Jijie and\n Wang, Zihan\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.12/\",\n doi = \"10.18653/v1/2021.acl-long.12\",\n pages = \"139--151\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.12.pdf", "site": "https://aclanthology.org/2021.acl-long.12/", "pdf_size": 569400, "gs_citation": 18, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=5467437310492556795&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 9, "aff": "State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications; State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications; State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications; Graduate School of Information Science and Technology, The University of Tokyo", "aff_domain": "bupt.edu.cn;bupt.edu.cn;bupt.edu.cn;tkl.iis.u-tokyo.ac.jp", "email": "bupt.edu.cn;bupt.edu.cn;bupt.edu.cn;tkl.iis.u-tokyo.ac.jp", "github": "https://github.com/guojinyu88/DSSDST", "project": "", "author_num": 4, "aff_unique_index": "0;0;0;1", "aff_unique_norm": "Beijing University of Posts and Telecommunications;The University of Tokyo", "aff_unique_dep": "State Key Laboratory of Networking and Switching Technology;Graduate School of Information Science and Technology", "aff_unique_url": "http://www.bupt.edu.cn/;https://www.u-tokyo.ac.jp", "aff_unique_abbr": "BUPT;UTokyo", "aff_campus_unique_index": "1", "aff_campus_unique": ";Tokyo", "aff_country_unique_index": "0;0;0;1", "aff_country_unique": "China;Japan" }, { "id": "2021.acl-long.441", "title": "DynaEval: Unifying Turn and Dialogue Level Evaluation", "track": "main", "status": "Long", "award": false, "abstract": "A dialogue is essentially a multi-turn interaction among interlocutors. Effective evaluation metrics should reflect the dynamics of such interaction. Existing automatic metrics are focused very much on the turn-level quality, while ignoring such dynamics. To this end, we propose DynaEval, a unified automatic evaluation framework which is not only capable of performing turn-level evaluation, but also holistically considers the quality of the entire dialogue. In DynaEval, the graph convolutional network (GCN) is adopted to model a dialogue in totality, where the graph nodes denote each individual utterance and the edges represent the dependency between pairs of utterances. A contrastive loss is then applied to distinguish well-formed dialogues from carefully constructed negative samples. Experiments show that DynaEval significantly outperforms the state-of-the-art dialogue coherence model, and correlates strongly with human judgements across multiple dialogue evaluation aspects at both turn and dialogue level.", "author": "Chen Zhang; Yiming Chen; Luis Fernando D\u2019Haro; Yan Zhang; Thomas Friedrichs; Grandee Lee; Haizhou Li", "authorids": "/c/chen-zhang/; /y/yiming-chen/; /l/luis-fernando-dharo/; /y/yan-zhang/; /t/thomas-friedrichs/; /g/grandee-lee/; /h/haizhou-li/", "bibtex": "@inproceedings{zhang-etal-2021-dynaeval,\n title = \"{D}yna{E}val: Unifying Turn and Dialogue Level Evaluation\",\n author = \"Zhang, Chen and\n Chen, Yiming and\n D{'}Haro, Luis Fernando and\n Zhang, Yan and\n Friedrichs, Thomas and\n Lee, Grandee and\n Li, Haizhou\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.441/\",\n doi = \"10.18653/v1/2021.acl-long.441\",\n pages = \"5676--5689\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.441.pdf", "site": "https://aclanthology.org/2021.acl-long.441/", "pdf_size": 560954, "gs_citation": 75, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=7674476394142005910&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 6, "aff": "National University of Singapore+Robert Bosch (SEA); National University of Singapore+Robert Bosch (SEA); Universidad Polit\u00e9cnica de Madrid; National University of Singapore; Robert Bosch (SEA); National University of Singapore; National University of Singapore+Kriston AI Lab", "aff_domain": "u.nus.edu;u.nus.edu;upm.es;nus.edu.sg;sg.bosch.com;u.nus.edu;nus.edu.sg", "email": "u.nus.edu;u.nus.edu;upm.es;nus.edu.sg;sg.bosch.com;u.nus.edu;nus.edu.sg", "github": "https://github.com/e0397123/DynaEval", "project": "", "author_num": 7, "aff_unique_index": "0+1;0+1;2;0;1;0;0+3", "aff_unique_norm": "National University of Singapore;Robert Bosch;Universidad Polit\u00e9cnica de Madrid;Kriston AI Lab", "aff_unique_dep": ";;;AI Lab", "aff_unique_url": "https://www.nus.edu.sg;https://www.bosch.com.sg;https://www.upm.es;", "aff_unique_abbr": "NUS;Bosch SEA;UPM;", "aff_campus_unique_index": ";;", "aff_campus_unique": "", "aff_country_unique_index": "0+0;0+0;1;0;0;0;0", "aff_country_unique": "Singapore;Spain;" }, { "id": "2021.acl-long.186", "title": "DynaSent: A Dynamic Benchmark for Sentiment Analysis", "track": "main", "status": "Long", "award": false, "abstract": "We introduce DynaSent (\u2018Dynamic Sentiment\u2019), a new English-language benchmark task for ternary (positive/negative/neutral) sentiment analysis. DynaSent combines naturally occurring sentences with sentences created using the open-source Dynabench Platform, which facilities human-and-model-in-the-loop dataset creation. DynaSent has a total of 121,634 sentences, each validated by five crowdworkers, and its development and test splits are designed to produce chance performance for even the best models we have been able to develop; when future models solve this task, we will use them to create DynaSent version 2, continuing the dynamic evolution of this benchmark. Here, we report on the dataset creation effort, focusing on the steps we took to increase quality and reduce artifacts. We also present evidence that DynaSent\u2019s Neutral category is more coherent than the comparable category in other benchmarks, and we motivate training models from scratch for each round over successive fine-tuning.", "author": "Christopher Potts; Zhengxuan Wu; Atticus Geiger; Douwe Kiela", "authorids": "/c/christopher-potts/; /z/zhengxuan-wu/; /a/atticus-geiger/; /d/douwe-kiela/", "bibtex": "@inproceedings{potts-etal-2021-dynasent,\n title = \"{D}yna{S}ent: A Dynamic Benchmark for Sentiment Analysis\",\n author = \"Potts, Christopher and\n Wu, Zhengxuan and\n Geiger, Atticus and\n Kiela, Douwe\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.186/\",\n doi = \"10.18653/v1/2021.acl-long.186\",\n pages = \"2388--2404\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.186.pdf", "site": "https://aclanthology.org/2021.acl-long.186/", "pdf_size": 1720935, "gs_citation": 87, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=6003608368578927011&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 4, "aff": "Stanford University; Stanford University; Stanford University; Facebook AI Research", "aff_domain": "stanford.edu;stanford.edu;stanford.edu;fb.com", "email": "stanford.edu;stanford.edu;stanford.edu;fb.com", "github": "https://github.com/cgpotts/dynasent", "project": "", "author_num": 4, "aff_unique_index": "0;0;0;1", "aff_unique_norm": "Stanford University;Facebook", "aff_unique_dep": ";Facebook AI Research", "aff_unique_url": "https://www.stanford.edu;https://research.facebook.com", "aff_unique_abbr": "Stanford;FAIR", "aff_campus_unique_index": "0;0;0", "aff_campus_unique": "Stanford;", "aff_country_unique_index": "0;0;0;0", "aff_country_unique": "United States" }, { "id": "2021.acl-long.542", "title": "Dynamic Contextualized Word Embeddings", "track": "main", "status": "Long", "award": false, "abstract": "Static word embeddings that represent words by a single vector cannot capture the variability of word meaning in different linguistic and extralinguistic contexts. Building on prior work on contextualized and dynamic word embeddings, we introduce dynamic contextualized word embeddings that represent words as a function of both linguistic and extralinguistic context. Based on a pretrained language model (PLM), dynamic contextualized word embeddings model time and social space jointly, which makes them attractive for a range of NLP tasks involving semantic variability. We highlight potential application scenarios by means of qualitative and quantitative analyses on four English datasets.", "author": "Valentin Hofmann; Janet Pierrehumbert; Hinrich Sch\u00fctze", "authorids": "/v/valentin-hofmann/; /j/janet-pierrehumbert/; /h/hinrich-schutze/", "bibtex": "@inproceedings{hofmann-etal-2021-dynamic,\n title = \"Dynamic Contextualized Word Embeddings\",\n author = {Hofmann, Valentin and\n Pierrehumbert, Janet and\n Sch{\\\"u}tze, Hinrich},\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.542/\",\n doi = \"10.18653/v1/2021.acl-long.542\",\n pages = \"6970--6984\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.542.pdf", "site": "https://aclanthology.org/2021.acl-long.542/", "pdf_size": 4462278, "gs_citation": 83, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=17045562912215792019&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 7, "aff": "Faculty of Linguistics, University of Oxford + Center for Information and Language Processing, LMU Munich; Department of Engineering Science, University of Oxford + Center for Information and Language Processing, LMU Munich; Center for Information and Language Processing, LMU Munich", "aff_domain": "ling-phil.ox.ac.uk; ; ", "email": "ling-phil.ox.ac.uk; ; ", "github": "", "project": "", "author_num": 3, "aff_unique_index": "0+1;0+1;1", "aff_unique_norm": "University of Oxford;LMU Munich", "aff_unique_dep": "Faculty of Linguistics;Center for Information and Language Processing", "aff_unique_url": "https://www.ox.ac.uk;https://www.lmu.de", "aff_unique_abbr": "Oxford;LMU", "aff_campus_unique_index": "0+1;0+1;1", "aff_campus_unique": "Oxford;Munich", "aff_country_unique_index": "0+1;0+1;1", "aff_country_unique": "United Kingdom;Germany" }, { "id": "2021.acl-long.42", "title": "E2E-VLP: End-to-End Vision-Language Pre-training Enhanced by Visual Learning", "track": "main", "status": "Long", "award": false, "abstract": "Vision-language pre-training (VLP) on large-scale image-text pairs has achieved huge success for the cross-modal downstream tasks. The most existing pre-training methods mainly adopt a two-step training procedure, which firstly employs a pre-trained object detector to extract region-based visual features, then concatenates the image representation and text embedding as the input of Transformer to train. However, these methods face problems of using task-specific visual representation of the specific object detector for generic cross-modal understanding, and the computation inefficiency of two-stage pipeline. In this paper, we propose the first end-to-end vision-language pre-trained model for both V+L understanding and generation, namely E2E-VLP, where we build a unified Transformer framework to jointly learn visual representation, and semantic alignments between image and text. We incorporate the tasks of object detection and image captioning into pre-training with a unified Transformer encoder-decoder architecture for enhancing visual learning. An extensive set of experiments have been conducted on well-established vision-language downstream tasks to demonstrate the effectiveness of this novel VLP paradigm.", "author": "Haiyang Xu; Ming Yan; Chenliang Li; Bin Bi; Songfang Huang; Wenming Xiao; Fei Huang", "authorids": "/h/haiyang-xu/; /m/ming-yan/; /c/chenliang-li/; /b/bin-bi/; /s/songfang-huang/; /w/wenming-xiao/; /f/fei-huang/", "bibtex": "@inproceedings{xu-etal-2021-e2e,\n title = \"{E}2{E}-{VLP}: End-to-End Vision-Language Pre-training Enhanced by Visual Learning\",\n author = \"Xu, Haiyang and\n Yan, Ming and\n Li, Chenliang and\n Bi, Bin and\n Huang, Songfang and\n Xiao, Wenming and\n Huang, Fei\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.42/\",\n doi = \"10.18653/v1/2021.acl-long.42\",\n pages = \"503--513\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.42.pdf", "site": "https://aclanthology.org/2021.acl-long.42/", "pdf_size": 543518, "gs_citation": 119, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=7183953822994813944&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 6, "aff": "Alibaba Group; Alibaba Group; Alibaba Group; Alibaba Group; Alibaba Group; Alibaba Group; Alibaba Group", "aff_domain": "alibaba-inc.com;alibaba-inc.com;alibaba-inc.com;alibaba-inc.com;alibaba-inc.com;alibaba-inc.com;alibaba-inc.com", "email": "alibaba-inc.com;alibaba-inc.com;alibaba-inc.com;alibaba-inc.com;alibaba-inc.com;alibaba-inc.com;alibaba-inc.com", "github": "", "project": "", "author_num": 7, "aff_unique_index": "0;0;0;0;0;0;0", "aff_unique_norm": "Alibaba Group", "aff_unique_dep": "", "aff_unique_url": "https://www.alibaba.com", "aff_unique_abbr": "Alibaba", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0;0;0;0;0;0;0", "aff_country_unique": "China" }, { "id": "2021.acl-long.260", "title": "ERICA: Improving Entity and Relation Understanding for Pre-trained Language Models via Contrastive Learning", "track": "main", "status": "Long", "award": false, "abstract": "Pre-trained Language Models (PLMs) have shown superior performance on various downstream Natural Language Processing (NLP) tasks. However, conventional pre-training objectives do not explicitly model relational facts in text, which are crucial for textual understanding. To address this issue, we propose a novel contrastive learning framework ERICA to obtain a deep understanding of the entities and their relations in text. Specifically, we define two novel pre-training tasks to better understand entities and relations: (1) the entity discrimination task to distinguish which tail entity can be inferred by the given head entity and relation; (2) the relation discrimination task to distinguish whether two relations are close or not semantically, which involves complex relational reasoning. Experimental results demonstrate that ERICA can improve typical PLMs (BERT and RoBERTa) on several language understanding tasks, including relation extraction, entity typing and question answering, especially under low-resource settings.", "author": "Yujia Qin; Yankai Lin; Ryuichi Takanobu; Zhiyuan Liu; Peng Li; Heng Ji; Minlie Huang; Maosong Sun; Jie Zhou", "authorids": "/y/yujia-qin/; /y/yankai-lin/; /r/ryuichi-takanobu/; /z/zhiyuan-liu/; /p/peng-li/; /h/heng-ji/; /m/minlie-huang/; /m/maosong-sun/; /j/jie-zhou/", "bibtex": "@inproceedings{qin-etal-2021-erica,\n title = \"{ERICA}: Improving Entity and Relation Understanding for Pre-trained Language Models via Contrastive Learning\",\n author = \"Qin, Yujia and\n Lin, Yankai and\n Takanobu, Ryuichi and\n Liu, Zhiyuan and\n Li, Peng and\n Ji, Heng and\n Huang, Minlie and\n Sun, Maosong and\n Zhou, Jie\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.260/\",\n doi = \"10.18653/v1/2021.acl-long.260\",\n pages = \"3350--3363\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.260.pdf", "site": "https://aclanthology.org/2021.acl-long.260/", "pdf_size": 1157874, "gs_citation": 137, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=11536461770171246236&as_sdt=5,33&sciodt=0,33&hl=en", "gs_version_total": 9, "aff": "Department of Computer Science and Technology, Tsinghua University+University of Illinois at Urbana-Champaign; Pattern Recognition Center, WeChat AI, Tencent Inc.; Department of Computer Science and Technology, Tsinghua University+Pattern Recognition Center, WeChat AI, Tencent Inc.; Department of Computer Science and Technology, Tsinghua University; Pattern Recognition Center, WeChat AI, Tencent Inc.; University of Illinois at Urbana-Champaign+Department of Computer Science and Technology, Tsinghua University; Department of Computer Science and Technology, Tsinghua University; Department of Computer Science and Technology, Tsinghua University; Pattern Recognition Center, WeChat AI, Tencent Inc.", "aff_domain": "gmail.com; ; ; ; ; ; ; ; ", "email": "gmail.com; ; ; ; ; ; ; ; ", "github": "https://github.com/thunlp/ERICA", "project": "", "author_num": 9, "aff_unique_index": "0+1;2;0+2;0;2;1+0;0;0;2", "aff_unique_norm": "Tsinghua University;University of Illinois at Urbana-Champaign;Tencent Inc.", "aff_unique_dep": "Department of Computer Science and Technology;;Pattern Recognition Center, WeChat AI", "aff_unique_url": "https://www.tsinghua.edu.cn;https://illinois.edu;https://www.tencent.com", "aff_unique_abbr": "THU;UIUC;Tencent", "aff_campus_unique_index": "1;;1", "aff_campus_unique": ";Urbana-Champaign", "aff_country_unique_index": "0+1;0;0+0;0;0;1+0;0;0;0", "aff_country_unique": "China;United States" }, { "id": "2021.acl-long.227", "title": "ERNIE-Doc: A Retrospective Long-Document Modeling Transformer", "track": "main", "status": "Long", "award": false, "abstract": "Transformers are not suited for processing long documents, due to their quadratically increasing memory and time consumption. Simply truncating a long document or applying the sparse attention mechanism will incur the context fragmentation problem or lead to an inferior modeling capability against comparable model sizes. In this paper, we propose ERNIE-Doc, a document-level language pretraining model based on Recurrence Transformers. Two well-designed techniques, namely the retrospective feed mechanism and the enhanced recurrence mechanism, enable ERNIE-Doc, which has a much longer effective context length, to capture the contextual information of a complete document. We pretrain ERNIE-Doc to explicitly learn the relationships among segments with an additional document-aware segment-reordering objective. Various experiments were conducted on both English and Chinese document-level tasks. ERNIE-Doc improved the state-of-the-art language modeling result of perplexity to 16.8 on WikiText-103. Moreover, it outperformed competitive pretraining models by a large margin on most language understanding tasks, such as text classification and question answering.", "author": "SiYu Ding; Junyuan Shang; Shuohuan Wang; Yu Sun; Hao Tian; Hua Wu; Haifeng Wang", "authorids": "/s/siyu-ding/; /j/junyuan-shang/; /s/shuohuan-wang/; /y/yu-sun/; /h/hao-tian/; /h/hua-wu/; /h/haifeng-wang/", "bibtex": "@inproceedings{ding-etal-2021-ernie,\n title = \"{ERNIE}-{D}oc: A Retrospective Long-Document Modeling Transformer\",\n author = \"Ding, SiYu and\n Shang, Junyuan and\n Wang, Shuohuan and\n Sun, Yu and\n Tian, Hao and\n Wu, Hua and\n Wang, Haifeng\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.227/\",\n doi = \"10.18653/v1/2021.acl-long.227\",\n pages = \"2914--2927\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.227.pdf", "site": "https://aclanthology.org/2021.acl-long.227/", "pdf_size": 597762, "gs_citation": 69, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=13322414640877182225&as_sdt=5,44&sciodt=0,44&hl=en", "gs_version_total": 5, "aff": "Baidu Inc., China; Baidu Inc., China; Baidu Inc., China; Baidu Inc., China; Baidu Inc., China; Baidu Inc., China; Baidu Inc., China", "aff_domain": "baidu.com;baidu.com;baidu.com;baidu.com;baidu.com;baidu.com;baidu.com", "email": "baidu.com;baidu.com;baidu.com;baidu.com;baidu.com;baidu.com;baidu.com", "github": "https://github.com/PaddlePaddle/ERNIE/tree/repro/ernie-doc", "project": "", "author_num": 7, "aff_unique_index": "0;0;0;0;0;0;0", "aff_unique_norm": "Baidu Inc.", "aff_unique_dep": "", "aff_unique_url": "https://www.baidu.com", "aff_unique_abbr": "Baidu", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0;0;0;0;0;0;0", "aff_country_unique": "China" }, { "id": "2021.acl-long.386", "title": "Early Detection of Sexual Predators in Chats", "track": "main", "status": "Long", "award": false, "abstract": "An important risk that children face today is online grooming, where a so-called sexual predator establishes an emotional connection with a minor online with the objective of sexual abuse. Prior work has sought to automatically identify grooming chats, but only after an incidence has already happened in the context of legal prosecution. In this work, we instead investigate this problem from the point of view of prevention. We define and study the task of early sexual predator detection (eSPD) in chats, where the goal is to analyze a running chat from its beginning and predict grooming attempts as early and as accurately as possible. We survey existing datasets and their limitations regarding eSPD, and create a new dataset called PANC for more realistic evaluations. We present strong baselines built on BERT that also reach state-of-the-art results for conventional SPD. Finally, we consider coping with limited computational resources, as real-life applications require eSPD on mobile devices.", "author": "Matthias Vogt; Ulf Leser; Alan Akbik", "authorids": "/m/matthias-vogt/; /u/ulf-leser/; /a/alan-akbik/", "bibtex": "@inproceedings{vogt-etal-2021-early,\n title = \"Early Detection of Sexual Predators in Chats\",\n author = \"Vogt, Matthias and\n Leser, Ulf and\n Akbik, Alan\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.386/\",\n doi = \"10.18653/v1/2021.acl-long.386\",\n pages = \"4985--4999\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.386.pdf", "site": "https://aclanthology.org/2021.acl-long.386/", "pdf_size": 828573, "gs_citation": 33, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=2301284069231977841&as_sdt=5,33&sciodt=0,33&hl=en", "gs_version_total": 5, "aff": "Humboldt Universit \u00a8at zu Berlin; Humboldt Universit \u00a8at zu Berlin; Humboldt Universit \u00a8at zu Berlin", "aff_domain": "campus.tu-berlin.de;informatik.hu-berlin.de;hu-berlin.de", "email": "campus.tu-berlin.de;informatik.hu-berlin.de;hu-berlin.de", "github": "", "project": "", "author_num": 3, "aff_unique_index": "0;0;0", "aff_unique_norm": "Humboldt University of Berlin", "aff_unique_dep": "", "aff_unique_url": "https://www.hu-berlin.de", "aff_unique_abbr": "HU Berlin", "aff_campus_unique_index": "0;0;0", "aff_campus_unique": "Berlin", "aff_country_unique_index": "0;0;0", "aff_country_unique": "Germany" }, { "id": "2021.acl-long.171", "title": "EarlyBERT: Efficient BERT Training via Early-bird Lottery Tickets", "track": "main", "status": "Long", "award": false, "abstract": "Heavily overparameterized language models such as BERT, XLNet and T5 have achieved impressive success in many NLP tasks. However, their high model complexity requires enormous computation resources and extremely long training time for both pre-training and fine-tuning. Many works have studied model compression on large NLP models, but only focusing on reducing inference time while still requiring an expensive training process. Other works use extremely large batch sizes to shorten the pre-training time, at the expense of higher computational resource demands. In this paper, inspired by the Early-Bird Lottery Tickets recently studied for computer vision tasks, we propose EarlyBERT, a general computationally-efficient training algorithm applicable to both pre-training and fine-tuning of large-scale language models. By slimming the self-attention and fully-connected sub-layers inside a transformer, we are the first to identify structured winning tickets in the early stage of BERT training. We apply those tickets towards efficient BERT training, and conduct comprehensive pre-training and fine-tuning experiments on GLUE and SQuAD downstream tasks. Our results show that EarlyBERT achieves comparable performance to standard BERT, with 35 45% less training time. Code is available at https://github.com/VITA-Group/EarlyBERT.", "author": "Xiaohan Chen; Yu Cheng; Shuohang Wang; Zhe Gan; Zhangyang Wang; Jingjing Liu", "authorids": "/x/xiaohan-chen/; /y/yu-cheng/; /s/shuohang-wang/; /z/zhe-gan/; /z/zhangyang-wang/; /j/jingjing-liu/", "bibtex": "@inproceedings{chen-etal-2021-earlybert,\n title = \"{E}arly{BERT}: Efficient {BERT} Training via Early-bird Lottery Tickets\",\n author = \"Chen, Xiaohan and\n Cheng, Yu and\n Wang, Shuohang and\n Gan, Zhe and\n Wang, Zhangyang and\n Liu, Jingjing\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.171/\",\n doi = \"10.18653/v1/2021.acl-long.171\",\n pages = \"2195--2207\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.171.pdf", "site": "https://aclanthology.org/2021.acl-long.171/", "pdf_size": 4562441, "gs_citation": 101, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=7718345183696994473&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 11, "aff": "University of Texas at Austin+Microsoft Corporation; Microsoft Corporation; Microsoft Corporation; Microsoft Corporation; University of Texas at Austin+Microsoft Corporation; Microsoft Corporation", "aff_domain": "utexas.edu;microsoft.com;microsoft.com;microsoft.com;utexas.edu;microsoft.com", "email": "utexas.edu;microsoft.com;microsoft.com;microsoft.com;utexas.edu;microsoft.com", "github": "https://github.com/VITA-Group/EarlyBERT", "project": "", "author_num": 6, "aff_unique_index": "0+1;1;1;1;0+1;1", "aff_unique_norm": "University of Texas at Austin;Microsoft Corporation", "aff_unique_dep": ";", "aff_unique_url": "https://www.utexas.edu;https://www.microsoft.com", "aff_unique_abbr": "UT Austin;Microsoft", "aff_campus_unique_index": "0;0", "aff_campus_unique": "Austin;", "aff_country_unique_index": "0+0;0;0;0;0+0;0", "aff_country_unique": "United States" }, { "id": "2021.acl-long.158", "title": "Edited Media Understanding Frames: Reasoning About the Intent and Implications of Visual Misinformation", "track": "main", "status": "Long", "award": false, "abstract": "Understanding manipulated media, from automatically generated \u2018deepfakes\u2019 to manually edited ones, raises novel research challenges. Because the vast majority of edited or manipulated images are benign, such as photoshopped images for visual enhancements, the key challenge is to understand the complex layers of underlying intents of media edits and their implications with respect to disinformation. In this paper, we study Edited Media Frames, a new formalism to understand visual media manipulation as structured annotations with respect to the intents, emotional reactions, attacks on individuals, and the overall implications of disinformation. We introduce a dataset for our task, EMU, with 56k question-answer pairs written in rich natural language. We evaluate a wide variety of vision-and-language models for our task, and introduce a new model PELICAN, which builds upon recent progress in pretrained multimodal representations. Our model obtains promising results on our dataset, with humans rating its answers as accurate 48.2% of the time. At the same time, there is still much work to be done \u2013 and we provide analysis that highlights areas for further progress.", "author": "Jeff Da; Maxwell Forbes; Rowan Zellers; Anthony Zheng; Jena D. Hwang; Antoine Bosselut; Yejin Choi", "authorids": "/j/jeff-da/; /m/maxwell-forbes/; /r/rowan-zellers/; /a/anthony-zheng/; /j/jena-d-hwang/; /a/antoine-bosselut/; /y/yejin-choi/", "bibtex": "@inproceedings{da-etal-2021-edited,\n title = \"Edited Media Understanding Frames: Reasoning About the Intent and Implications of Visual Misinformation\",\n author = \"Da, Jeff and\n Forbes, Maxwell and\n Zellers, Rowan and\n Zheng, Anthony and\n Hwang, Jena D. and\n Bosselut, Antoine and\n Choi, Yejin\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.158/\",\n doi = \"10.18653/v1/2021.acl-long.158\",\n pages = \"2026--2039\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.158.pdf", "site": "https://aclanthology.org/2021.acl-long.158/", "pdf_size": 8316041, "gs_citation": 17, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=9173540587329030833&as_sdt=5,31&sciodt=0,31&hl=en", "gs_version_total": 2, "aff": "Allen Institute for Artificial Intelligence; Allen Institute for Artificial Intelligence+Paul G. Allen School of Computer Science & Engineering, University of Washington; Allen Institute for Artificial Intelligence+Paul G. Allen School of Computer Science & Engineering, University of Washington; University of Michigan; Allen Institute for Artificial Intelligence; Allen Institute for Artificial Intelligence+Stanford University; Allen Institute for Artificial Intelligence+Paul G. Allen School of Computer Science & Engineering, University of Washington", "aff_domain": "; ; ; ; ; ; ", "email": "; ; ; ; ; ; ", "github": "", "project": "jeffda.com/edited-media-understanding", "author_num": 7, "aff_unique_index": "0;0+1;0+1;2;0;0+3;0+1", "aff_unique_norm": "Allen Institute for Artificial Intelligence;University of Washington;University of Michigan;Stanford University", "aff_unique_dep": ";Paul G. Allen School of Computer Science & Engineering;;", "aff_unique_url": "https://allenai.org;https://www.washington.edu;https://www.umich.edu;https://www.stanford.edu", "aff_unique_abbr": "AI2;UW;UM;Stanford", "aff_campus_unique_index": "1;1;2;1", "aff_campus_unique": ";Seattle;Stanford", "aff_country_unique_index": "0;0+0;0+0;0;0;0+0;0+0", "aff_country_unique": "United States" }, { "id": "2021.acl-short.123", "title": "Efficient Passage Retrieval with Hashing for Open-domain Question Answering", "track": "main", "status": "Short", "award": false, "abstract": "Most state-of-the-art open-domain question answering systems use a neural retrieval model to encode passages into continuous vectors and extract them from a knowledge source. However, such retrieval models often require large memory to run because of the massive size of their passage index. In this paper, we introduce Binary Passage Retriever (BPR), a memory-efficient neural retrieval model that integrates a learning-to-hash technique into the state-of-the-art Dense Passage Retriever (DPR) to represent the passage index using compact binary codes rather than continuous vectors. BPR is trained with a multi-task objective over two tasks: efficient candidate generation based on binary codes and accurate reranking based on continuous vectors. Compared with DPR, BPR substantially reduces the memory cost from 65GB to 2GB without a loss of accuracy on two standard open-domain question answering benchmarks: Natural Questions and TriviaQA. Our code and trained models are available at https://github.com/studio-ousia/bpr.", "author": "Ikuya Yamada; Akari Asai; Hannaneh Hajishirzi", "authorids": "/i/ikuya-yamada/; /a/akari-asai/; /h/hannaneh-hajishirzi/", "bibtex": "@inproceedings{yamada-etal-2021-efficient,\n title = \"Efficient Passage Retrieval with Hashing for Open-domain Question Answering\",\n author = \"Yamada, Ikuya and\n Asai, Akari and\n Hajishirzi, Hannaneh\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-short.123/\",\n doi = \"10.18653/v1/2021.acl-short.123\",\n pages = \"979--986\"\n}", "pdf": "https://aclanthology.org/2021.acl-short.123.pdf", "site": "https://aclanthology.org/2021.acl-short.123/", "pdf_size": 424457, "gs_citation": 95, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=16930472319336698294&as_sdt=5,33&sciodt=0,33&hl=en", "gs_version_total": 5, "aff": "Studio Ousia+RIKEN; University of Washington; University of Washington+Allen Institute for AI", "aff_domain": "ousia.jp;cs.washington.edu;cs.washington.edu", "email": "ousia.jp;cs.washington.edu;cs.washington.edu", "github": "https://github.com/studio-ousia/bpr", "project": "", "author_num": 3, "aff_unique_index": "0+1;2;2+3", "aff_unique_norm": "Studio Ousia;RIKEN;University of Washington;Allen Institute for AI", "aff_unique_dep": ";;;", "aff_unique_url": "https://www.studioousia.com;https://www.riken.jp;https://www.washington.edu;https://allenai.org", "aff_unique_abbr": ";RIKEN;UW;AI2", "aff_campus_unique_index": ";", "aff_campus_unique": "", "aff_country_unique_index": "0+0;1;1+1", "aff_country_unique": "Japan;United States" }, { "id": "2021.acl-short.91", "title": "Efficient Text-based Reinforcement Learning by Jointly Leveraging State and Commonsense Graph Representations", "track": "main", "status": "Short", "award": false, "abstract": "Text-based games (TBGs) have emerged as useful benchmarks for evaluating progress at the intersection of grounded language understanding and reinforcement learning (RL). Recent work has proposed the use of external knowledge to improve the efficiency of RL agents for TBGs. In this paper, we posit that to act efficiently in TBGs, an agent must be able to track the state of the game while retrieving and using relevant commonsense knowledge. Thus, we propose an agent for TBGs that induces a graph representation of the game state and jointly grounds it with a graph of commonsense knowledge from ConceptNet. This combination is achieved through bidirectional knowledge graph attention between the two symbolic representations. We show that agents that incorporate commonsense into the game state graph outperform baseline agents.", "author": "Keerthiram Murugesan; Mattia Atzeni; Pavan Kapanipathi; Kartik Talamadupula; Mrinmaya Sachan; Murray Campbell", "authorids": "/k/keerthiram-murugesan/; /m/mattia-atzeni/; /p/pavan-kapanipathi/; /k/kartik-talamadupula/; /m/mrinmaya-sachan/; /m/murray-campbell/", "bibtex": "@inproceedings{murugesan-etal-2021-efficient,\n title = \"Efficient Text-based Reinforcement Learning by Jointly Leveraging State and Commonsense Graph Representations\",\n author = \"Murugesan, Keerthiram and\n Atzeni, Mattia and\n Kapanipathi, Pavan and\n Talamadupula, Kartik and\n Sachan, Mrinmaya and\n Campbell, Murray\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-short.91/\",\n doi = \"10.18653/v1/2021.acl-short.91\",\n pages = \"719--725\"\n}", "pdf": "https://aclanthology.org/2021.acl-short.91.pdf", "site": "https://aclanthology.org/2021.acl-short.91/", "pdf_size": 594256, "gs_citation": 17, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=3884136098447165457&as_sdt=5,31&sciodt=0,31&hl=en", "gs_version_total": 6, "aff": "IBM Research; IBM Research + LEPFL; IBM Research; IBM Research; ETH Z\u00fcrich; IBM Research", "aff_domain": "ibm.com;zurich.ibm.com;us.ibm.com;us.ibm.com;inf.ethz.ch;us.ibm.com", "email": "ibm.com;zurich.ibm.com;us.ibm.com;us.ibm.com;inf.ethz.ch;us.ibm.com", "github": "", "project": "", "author_num": 6, "aff_unique_index": "0;0+1;0;0;2;0", "aff_unique_norm": "IBM;Laboratoire d'\u00c9tudes des Particules et des Cosmiques;ETH Z\u00fcrich", "aff_unique_dep": "IBM Research;;", "aff_unique_url": "https://www.ibm.com/research;https://lepfl.unil.ch;https://www.ethz.ch", "aff_unique_abbr": "IBM;LEPFL;ETHZ", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0;0+1;0;0;1;0", "aff_country_unique": "United States;Switzerland" }, { "id": "2021.acl-long.361", "title": "Element Intervention for Open Relation Extraction", "track": "main", "status": "Long", "award": false, "abstract": "Open relation extraction aims to cluster relation instances referring to the same underlying relation, which is a critical step for general relation extraction. Current OpenRE models are commonly trained on the datasets generated from distant supervision, which often results in instability and makes the model easily collapsed. In this paper, we revisit the procedure of OpenRE from a causal view. By formulating OpenRE using a structural causal model, we identify that the above-mentioned problems stem from the spurious correlations from entities and context to the relation type. To address this issue, we conduct Element Intervention, which intervene on the context and entities respectively to obtain the underlying causal effects of them. We also provide two specific implementations of the interventions based on entity ranking and context contrasting. Experimental results on unsupervised relation extraction datasets show our method to outperform previous state-of-the-art methods and is robust across different datasets.", "author": "Fangchao Liu; Lingyong Yan; Hongyu Lin; Xianpei Han; Le Sun", "authorids": "/f/fangchao-liu/; /l/lingyong-yan/; /h/hongyu-lin/; /x/xianpei-han/; /l/le-sun/", "bibtex": "@inproceedings{liu-etal-2021-element,\n title = \"Element Intervention for Open Relation Extraction\",\n author = \"Liu, Fangchao and\n Yan, Lingyong and\n Lin, Hongyu and\n Han, Xianpei and\n Sun, Le\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.361/\",\n doi = \"10.18653/v1/2021.acl-long.361\",\n pages = \"4683--4693\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.361.pdf", "site": "https://aclanthology.org/2021.acl-long.361/", "pdf_size": 552627, "gs_citation": 29, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=4500050148888398337&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 5, "aff": "Chinese Information Processing Laboratory+University of Chinese Academy of Sciences; Chinese Information Processing Laboratory+University of Chinese Academy of Sciences; Chinese Information Processing Laboratory; Chinese Information Processing Laboratory+State Key Laboratory of Computer Science; Chinese Information Processing Laboratory+State Key Laboratory of Computer Science", "aff_domain": "iscas.ac.cn;iscas.ac.cn;iscas.ac.cn;iscas.ac.cn;iscas.ac.cn", "email": "iscas.ac.cn;iscas.ac.cn;iscas.ac.cn;iscas.ac.cn;iscas.ac.cn", "github": "https://github.com/Lfc1993/EI-ORE", "project": "", "author_num": 5, "aff_unique_index": "0+1;0+1;0;0+2;0+2", "aff_unique_norm": "Chinese Information Processing Laboratory;University of Chinese Academy of Sciences;State Key Laboratory of Computer Science", "aff_unique_dep": "Information Processing;;", "aff_unique_url": ";http://www.ucas.ac.cn;", "aff_unique_abbr": ";UCAS;", "aff_campus_unique_index": ";;;", "aff_campus_unique": "", "aff_country_unique_index": "0+0;0+0;0;0+0;0+0", "aff_country_unique": "China" }, { "id": "2021.acl-long.537", "title": "EmailSum: Abstractive Email Thread Summarization", "track": "main", "status": "Long", "award": false, "abstract": "Recent years have brought about an interest in the challenging task of summarizing conversation threads (meetings, online discussions, etc.). Such summaries help analysis of the long text to quickly catch up with the decisions made and thus improve our work or communication efficiency. To spur research in thread summarization, we have developed an abstractive Email Thread Summarization (EmailSum) dataset, which contains human-annotated short (<30 words) and long (<100 words) summaries of 2,549 email threads (each containing 3 to 10 emails) over a wide variety of topics. We perform a comprehensive empirical study to explore different summarization techniques (including extractive and abstractive methods, single-document and hierarchical models, as well as transfer and semisupervised learning) and conduct human evaluations on both short and long summary generation tasks. Our results reveal the key challenges of current abstractive summarization models in this task, such as understanding the sender\u2019s intent and identifying the roles of sender and receiver. Furthermore, we find that widely used automatic evaluation metrics (ROUGE, BERTScore) are weakly correlated with human judgments on this email thread summarization task. Hence, we emphasize the importance of human evaluation and the development of better metrics by the community.", "author": "Shiyue Zhang; Asli Celikyilmaz; Jianfeng Gao; Mohit Bansal", "authorids": "/s/shiyue-zhang/; /a/asli-celikyilmaz/; /j/jianfeng-gao/; /m/mohit-bansal/", "bibtex": "@inproceedings{zhang-etal-2021-emailsum,\n title = \"{E}mail{S}um: Abstractive Email Thread Summarization\",\n author = \"Zhang, Shiyue and\n Celikyilmaz, Asli and\n Gao, Jianfeng and\n Bansal, Mohit\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.537/\",\n doi = \"10.18653/v1/2021.acl-long.537\",\n pages = \"6895--6909\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.537.pdf", "site": "https://aclanthology.org/2021.acl-long.537/", "pdf_size": 1027730, "gs_citation": 42, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=12596715738215817778&as_sdt=5,44&sciodt=0,44&hl=en", "gs_version_total": 4, "aff": "UNC Chapel Hill; Facebook AI Research; Microsoft Research; UNC Chapel Hill", "aff_domain": "cs.unc.edu;cs.unc.edu;fb.com;microsoft.com", "email": "cs.unc.edu;cs.unc.edu;fb.com;microsoft.com", "github": "https://github.com/ZhangShiyue/EmailSum", "project": "", "author_num": 4, "aff_unique_index": "0;1;2;0", "aff_unique_norm": "University of North Carolina at Chapel Hill;Facebook;Microsoft Corporation", "aff_unique_dep": ";Facebook AI Research;Microsoft Research", "aff_unique_url": "https://www.unc.edu;https://research.facebook.com;https://www.microsoft.com/en-us/research", "aff_unique_abbr": "UNC;FAIR;MSR", "aff_campus_unique_index": "0;0", "aff_campus_unique": "Chapel Hill;", "aff_country_unique_index": "0;0;0;0", "aff_country_unique": "United States" }, { "id": "2021.acl-short.80", "title": "Embedding Time Differences in Context-sensitive Neural Networks for Learning Time to Event", "track": "main", "status": "Short", "award": false, "abstract": "We propose an effective context-sensitive neural model for time to event (TTE) prediction task, which aims to predict the amount of time to/from the occurrence of given events in streaming content. We investigate this problem in the context of a multi-task learning framework, which we enrich with time difference embeddings. In addition, we develop a multi-genre dataset of English events about soccer competitions and academy awards ceremonies, and their relevant tweets obtained from Twitter. Our model is 1.4 and 3.3 hours more accurate than the current state-of-the-art model in estimating TTE on English and Dutch tweets respectively. We examine different aspects of our model to illustrate its source of improvement.", "author": "Nazanin Dehghani; Hassan Hajipoor; Hadi Amiri", "authorids": "/n/nazanin-dehghani/; /h/hassan-hajipoor/; /h/hadi-amiri/", "bibtex": "@inproceedings{dehghani-etal-2021-embedding,\n title = \"Embedding Time Differences in Context-sensitive Neural Networks for Learning Time to Event\",\n author = \"Dehghani, Nazanin and\n Hajipoor, Hassan and\n Amiri, Hadi\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-short.80/\",\n doi = \"10.18653/v1/2021.acl-short.80\",\n pages = \"630--636\"\n}", "pdf": "https://aclanthology.org/2021.acl-short.80.pdf", "site": "https://aclanthology.org/2021.acl-short.80/", "pdf_size": 532555, "gs_citation": 1, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=6249381775472513088&as_sdt=40005&sciodt=0,10&hl=en", "gs_version_total": 0, "aff": "IRISA, University of Rennes 1 (ENSSAT), Lannion, France; Department Computer Science, University of Massachusetts, Lowell, USA; Department Computer Science, University of Massachusetts, Lowell, USA + Department of Biomedical Informatics, Harvard University, Boston, USA", "aff_domain": "irisa.fr;student.uml.edu;uml.edu", "email": "irisa.fr;student.uml.edu;uml.edu", "github": "https://github.com/hajipoor/time2event", "project": "", "author_num": 3, "aff_unique_index": "0;1;1+2", "aff_unique_norm": "University of Rennes 1;University of Massachusetts Lowell;Harvard University", "aff_unique_dep": "IRISA;Department of Computer Science;Department of Biomedical Informatics", "aff_unique_url": "https://www.univ-rennes1.fr;https://www.uml.edu;https://www.harvard.edu", "aff_unique_abbr": "UR1;UMass Lowell;Harvard", "aff_campus_unique_index": "0;1;1+2", "aff_campus_unique": "Lannion;Lowell;Boston", "aff_country_unique_index": "0;1;1+1", "aff_country_unique": "France;United States" }, { "id": "2021.acl-short.109", "title": "Embracing Ambiguity: Shifting the Training Target of NLI Models", "track": "main", "status": "Short", "award": false, "abstract": "Natural Language Inference (NLI) datasets contain examples with highly ambiguous labels. While many research works do not pay much attention to this fact, several recent efforts have been made to acknowledge and embrace the existence of ambiguity, such as UNLI and ChaosNLI. In this paper, we explore the option of training directly on the estimated label distribution of the annotators in the NLI task, using a learning loss based on this ambiguity distribution instead of the gold-labels. We prepare AmbiNLI, a trial dataset obtained from readily available sources, and show it is possible to reduce ChaosNLI divergence scores when finetuning on this data, a promising first step towards learning how to capture linguistic ambiguity. Additionally, we show that training on the same amount of data but targeting the ambiguity distribution instead of gold-labels can result in models that achieve higher performance and learn better representations for downstream tasks.", "author": "Johannes Mario Meissner; Napat Thumwanit; Saku Sugawara; Akiko Aizawa", "authorids": "/j/johannes-mario-meissner/; /n/napat-thumwanit/; /s/saku-sugawara/; /a/akiko-aizawa/", "bibtex": "@inproceedings{meissner-etal-2021-embracing,\n title = \"Embracing Ambiguity: {S}hifting the Training Target of {NLI} Models\",\n author = \"Meissner, Johannes Mario and\n Thumwanit, Napat and\n Sugawara, Saku and\n Aizawa, Akiko\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-short.109/\",\n doi = \"10.18653/v1/2021.acl-short.109\",\n pages = \"862--869\"\n}", "pdf": "https://aclanthology.org/2021.acl-short.109.pdf", "site": "https://aclanthology.org/2021.acl-short.109/", "pdf_size": 572959, "gs_citation": 25, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=6476382877390479819&as_sdt=5,44&sciodt=0,44&hl=en", "gs_version_total": 4, "aff": "The University of Tokyo; The University of Tokyo; National Institute of Informatics; The University of Tokyo+National Institute of Informatics", "aff_domain": "nii.ac.jp;nii.ac.jp;nii.ac.jp;nii.ac.jp", "email": "nii.ac.jp;nii.ac.jp;nii.ac.jp;nii.ac.jp", "github": "", "project": "", "author_num": 4, "aff_unique_index": "0;0;1;0+1", "aff_unique_norm": "University of Tokyo;National Institute of Informatics", "aff_unique_dep": ";", "aff_unique_url": "https://www.u-tokyo.ac.jp;https://www.nii.ac.jp/", "aff_unique_abbr": "UTokyo;NII", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0;0;0;0+0", "aff_country_unique": "Japan" }, { "id": "2021.acl-long.366", "title": "Employing Argumentation Knowledge Graphs for Neural Argument Generation", "track": "main", "status": "Long", "award": false, "abstract": "Generating high-quality arguments, while being challenging, may benefit a wide range of downstream applications, such as writing assistants and argument search engines. Motivated by the effectiveness of utilizing knowledge graphs for supporting general text generation tasks, this paper investigates the usage of argumentation-related knowledge graphs to control the generation of arguments. In particular, we construct and populate three knowledge graphs, employing several compositions of them to encode various knowledge into texts of debate portals and relevant paragraphs from Wikipedia. Then, the texts with the encoded knowledge are used to fine-tune a pre-trained text generation model, GPT-2. We evaluate the newly created arguments manually and automatically, based on several dimensions important in argumentative contexts, including argumentativeness and plausibility. The results demonstrate the positive impact of encoding the graphs\u2019 knowledge into debate portal texts for generating arguments with superior quality than those generated without knowledge.", "author": "Khalid Al Khatib; Lukas Trautner; Henning Wachsmuth; Yufang Hou; Benno Stein", "authorids": "/k/khalid-al-khatib/; /l/lukas-trautner/; /h/henning-wachsmuth/; /y/yufang-hou/; /b/benno-stein/", "bibtex": "@inproceedings{al-khatib-etal-2021-employing,\n title = \"Employing Argumentation Knowledge Graphs for Neural Argument Generation\",\n author = \"Al Khatib, Khalid and\n Trautner, Lukas and\n Wachsmuth, Henning and\n Hou, Yufang and\n Stein, Benno\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.366/\",\n doi = \"10.18653/v1/2021.acl-long.366\",\n pages = \"4744--4754\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.366.pdf", "site": "https://aclanthology.org/2021.acl-long.366/", "pdf_size": 422398, "gs_citation": 32, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=11899396772129000802&as_sdt=5,33&sciodt=0,33&hl=en", "gs_version_total": 5, "aff": "Leipzig University, Germany; University of Erlangen-Nuremberg, Germany; Paderborn University, Germany; IBM Research Europe, Ireland; Bauhaus-Universit\u00e4t Weimar, Germany", "aff_domain": "uni-leipzig.de;fau.de;upb.de;ie.ibm.com;uni-weimar.de", "email": "uni-leipzig.de;fau.de;upb.de;ie.ibm.com;uni-weimar.de", "github": "", "project": "", "author_num": 5, "aff_unique_index": "0;1;2;3;4", "aff_unique_norm": "Leipzig University;Friedrich-Alexander-Universit\u00e4t Erlangen-N\u00fcrnberg;Paderborn University;IBM Research Europe;Bauhaus-Universit\u00e4t Weimar", "aff_unique_dep": ";;;;", "aff_unique_url": "https://www.uni-leipzig.de;https://www fau.de;https://www.uni-paderborn.de;https://www.ibm.com/research/europe;https://www.bauhaus-university.de", "aff_unique_abbr": "Uni Leipzig;FAU;UPB;IBM RE;Bauhaus-Uni Weimar", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0;0;0;1;0", "aff_country_unique": "Germany;Ireland" }, { "id": "2021.acl-long.418", "title": "Enabling Lightweight Fine-tuning for Pre-trained Language Model Compression based on Matrix Product Operators", "track": "main", "status": "Long", "award": false, "abstract": "This paper presents a novel pre-trained language models (PLM) compression approach based on the matrix product operator (short as MPO) from quantum many-body physics. It can decompose an original matrix into central tensors (containing the core information) and auxiliary tensors (with only a small proportion of parameters). With the decomposed MPO structure, we propose a novel fine-tuning strategy by only updating the parameters from the auxiliary tensors, and design an optimization algorithm for MPO-based approximation over stacked network architectures. Our approach can be applied to the original or the compressed PLMs in a general way, which derives a lighter network and significantly reduces the parameters to be fine-tuned. Extensive experiments have demonstrated the effectiveness of the proposed approach in model compression, especially the reduction in fine-tuning parameters (91% reduction on average). The code to reproduce the results of this paper can be found at https://github.com/RUCAIBox/MPOP.", "author": "Peiyu Liu; Ze-Feng Gao; Wayne Xin Zhao; Zhi-Yuan Xie; Zhong-Yi Lu; Ji-Rong Wen", "authorids": "/p/peiyu-liu/; /z/ze-feng-gao/; /w/wayne-xin-zhao/; /z/zhi-yuan-xie/; /z/zhong-yi-lu/; /j/ji-rong-wen/", "bibtex": "@inproceedings{liu-etal-2021-enabling,\n title = \"Enabling Lightweight Fine-tuning for Pre-trained Language Model Compression based on Matrix Product Operators\",\n author = \"Liu, Peiyu and\n Gao, Ze-Feng and\n Zhao, Wayne Xin and\n Xie, Zhi-Yuan and\n Lu, Zhong-Yi and\n Wen, Ji-Rong\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.418/\",\n doi = \"10.18653/v1/2021.acl-long.418\",\n pages = \"5388--5398\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.418.pdf", "site": "https://aclanthology.org/2021.acl-long.418/", "pdf_size": 467098, "gs_citation": 33, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=16699174889327602001&as_sdt=5,44&sciodt=0,44&hl=en", "gs_version_total": 8, "aff": "Gaoling School of Artificial Intelligence, Renmin University of China + Beijing Key Laboratory of Big Data Management and Analysis Methods + Beijing Academy of Artificial Intelligence; Department of Physics, Renmin University of China; Gaoling School of Artificial Intelligence, Renmin University of China + Beijing Key Laboratory of Big Data Management and Analysis Methods + Beijing Academy of Artificial Intelligence; Department of Physics, Renmin University of China; Department of Physics, Renmin University of China + Beijing Key Laboratory of Big Data Management and Analysis Methods; Gaoling School of Artificial Intelligence, Renmin University of China + School of Information, Renmin University of China + Beijing Key Laboratory of Big Data Management and Analysis Methods + Beijing Academy of Artificial Intelligence", "aff_domain": "ruc.edu.cn;ruc.edu.cn;ruc.edu.cn;ruc.edu.cn;gmail.com;ruc.edu.cn", "email": "ruc.edu.cn;ruc.edu.cn;ruc.edu.cn;ruc.edu.cn;gmail.com;ruc.edu.cn", "github": "https://github.com/RUCAIBox/MPOP", "project": "", "author_num": 6, "aff_unique_index": "0+1+2;0;0+1+2;0;0+1;0+0+1+2", "aff_unique_norm": "Renmin University of China;Beijing Key Laboratory of Big Data Management and Analysis Methods;Beijing Academy of Artificial Intelligence", "aff_unique_dep": "Gaoling School of Artificial Intelligence;Big Data Management and Analysis;", "aff_unique_url": "http://www.ruc.edu.cn;;https://www.baaic.cn", "aff_unique_abbr": "RUC;;BAAI", "aff_campus_unique_index": "0;0;;0", "aff_campus_unique": "Beijing;", "aff_country_unique_index": "0+0+0;0;0+0+0;0;0+0;0+0+0+0", "aff_country_unique": "China" }, { "id": "2021.acl-long.324", "title": "End-to-End AMR Coreference Resolution", "track": "main", "status": "Long", "award": false, "abstract": "Although parsing to Abstract Meaning Representation (AMR) has become very popular and AMR has been shown effective on the many sentence-level downstream tasks, little work has studied how to generate AMRs that can represent multi-sentence information. We introduce the first end-to-end AMR coreference resolution model in order to build multi-sentence AMRs. Compared with the previous pipeline and rule-based approaches, our model alleviates error propagation and it is more robust for both in-domain and out-domain situations. Besides, the document-level AMRs obtained by our model can significantly improve over the AMRs generated by a rule-based method (Liu et al., 2015) on text summarization.", "author": "Qiankun Fu; Linfeng Song; Wenyu Du; Yue Zhang", "authorids": "/q/qiankun-fu/; /l/linfeng-song/; /w/wenyu-du/; /y/yue-zhang/", "bibtex": "@inproceedings{fu-etal-2021-end,\n title = \"End-to-End {AMR} Coreference Resolution\",\n author = \"Fu, Qiankun and\n Song, Linfeng and\n Du, Wenyu and\n Zhang, Yue\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.324/\",\n doi = \"10.18653/v1/2021.acl-long.324\",\n pages = \"4204--4214\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.324.pdf", "site": "https://aclanthology.org/2021.acl-long.324/", "pdf_size": 907583, "gs_citation": 31, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=13969340148055930416&as_sdt=5,33&sciodt=0,33&hl=en", "gs_version_total": 3, "aff": "Zhejiang University + School of Engineering, Westlake University + Institute of Advanced Technology, Westlake Institute for Advanced Study; Tencent AI Lab, Bellevue, WA, USA; School of Engineering, Westlake University + Institute of Advanced Technology, Westlake Institute for Advanced Study; School of Engineering, Westlake University + Institute of Advanced Technology, Westlake Institute for Advanced Study", "aff_domain": "gmail.com;tencent.com;westlake.edu.cn;westlake.edu.cn", "email": "gmail.com;tencent.com;westlake.edu.cn;westlake.edu.cn", "github": "", "project": "", "author_num": 4, "aff_unique_index": "0+1+2;3;1+2;1+2", "aff_unique_norm": "Zhejiang University;Westlake University;Westlake Institute for Advanced Study;Tencent", "aff_unique_dep": ";School of Engineering;Institute of Advanced Technology;AI Lab", "aff_unique_url": "https://www.zju.edu.cn;https://www.westlake.edu.cn;http://www.wias.org.cn/;https://ai.tencent.com", "aff_unique_abbr": "ZJU;;WIAS;Tencent AI Lab", "aff_campus_unique_index": ";1;;", "aff_campus_unique": ";Bellevue", "aff_country_unique_index": "0+0+0;1;0+0;0+0", "aff_country_unique": "China;United States" }, { "id": "2021.acl-long.311", "title": "End-to-End Lexically Constrained Machine Translation for Morphologically Rich Languages", "track": "main", "status": "Long", "award": false, "abstract": "Lexically constrained machine translation allows the user to manipulate the output sentence by enforcing the presence or absence of certain words and phrases. Although current approaches can enforce terms to appear in the translation, they often struggle to make the constraint word form agree with the rest of the generated output. Our manual analysis shows that 46% of the errors in the output of a baseline constrained model for English to Czech translation are related to agreement. We investigate mechanisms to allow neural machine translation to infer the correct word inflection given lemmatized constraints. In particular, we focus on methods based on training the model with constraints provided as part of the input sequence. Our experiments on English-Czech language pair show that this approach improves translation of constrained terms in both automatic and manual evaluation by reducing errors in agreement. Our approach thus eliminates inflection errors, without introducing new errors or decreasing overall quality of the translation.", "author": "Josef Jon; Jo\u00e3o Paulo Aires; Dusan Varis; Ond\u0159ej Bojar", "authorids": "/j/josef-jon/; /j/joao-paulo-aires/; /d/dusan-varis/; /o/ondrej-bojar/", "bibtex": "@inproceedings{jon-etal-2021-end,\n title = \"End-to-End Lexically Constrained Machine Translation for Morphologically Rich Languages\",\n author = \"Jon, Josef and\n Aires, Jo{\\~a}o Paulo and\n Varis, Dusan and\n Bojar, Ond{\\v{r}}ej\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.311/\",\n doi = \"10.18653/v1/2021.acl-long.311\",\n pages = \"4019--4033\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.311.pdf", "site": "https://aclanthology.org/2021.acl-long.311/", "pdf_size": 382961, "gs_citation": 17, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=2590912673555465777&as_sdt=5,33&sciodt=0,33&hl=en", "gs_version_total": 6, "aff": "Charles University; Charles University; Charles University; Charles University", "aff_domain": "mail.ufal.mff.cuni.cz; ; ; ", "email": "mail.ufal.mff.cuni.cz; ; ; ", "github": "", "project": "", "author_num": 4, "aff_unique_index": "0;0;0;0", "aff_unique_norm": "Charles University", "aff_unique_dep": "", "aff_unique_url": "https://www.cuni.cz", "aff_unique_abbr": "Charles U", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0;0;0;0", "aff_country_unique": "Czech Republic" }, { "id": "2021.acl-long.519", "title": "End-to-End Training of Neural Retrievers for Open-Domain Question Answering", "track": "main", "status": "Long", "award": false, "abstract": "Recent work on training neural retrievers for open-domain question answering (OpenQA) has employed both supervised and unsupervised approaches. However, it remains unclear how unsupervised and supervised methods can be used most effectively for neural retrievers. In this work, we systematically study retriever pre-training. We first propose an approach of unsupervised pre-training with the Inverse Cloze Task and masked salient spans, followed by supervised finetuning using question-context pairs. This approach leads to absolute gains of 2+ points over the previous best result in the top-20 retrieval accuracy on Natural Questions and TriviaQA datasets. We next explore two approaches for end-to-end training of the reader and retriever components in OpenQA models, which differ in the manner the reader ingests the retrieved documents. Our experiments demonstrate the effectiveness of these approaches as we obtain state-of-the-art results. On the Natural Questions dataset, we obtain a top-20 retrieval accuracy of 84%, an improvement of 5 points over the recent DPR model. We also achieve good results on answer extraction, outperforming recent models like REALM and RAG by 3+ points.", "author": "Devendra Sachan; Mostofa Patwary; Mohammad Shoeybi; Neel Kant; Wei Ping; William L. Hamilton; Bryan Catanzaro", "authorids": "/d/devendra-sachan/; /m/mostofa-patwary/; /m/mohammad-shoeybi/; /n/neel-kant/; /w/wei-ping/; /w/william-l-hamilton/; /b/bryan-catanzaro/", "bibtex": "@inproceedings{sachan-etal-2021-end,\n title = \"End-to-End Training of Neural Retrievers for Open-Domain Question Answering\",\n author = \"Sachan, Devendra and\n Patwary, Mostofa and\n Shoeybi, Mohammad and\n Kant, Neel and\n Ping, Wei and\n Hamilton, William L. and\n Catanzaro, Bryan\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.519/\",\n doi = \"10.18653/v1/2021.acl-long.519\",\n pages = \"6648--6662\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.519.pdf", "site": "https://aclanthology.org/2021.acl-long.519/", "pdf_size": 611522, "gs_citation": 103, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=1562679263075477996&as_sdt=5,33&sciodt=0,33&hl=en", "gs_version_total": 4, "aff": "Mila - Quebec AI Institute+McGill University; NVIDIA; NVIDIA; NVIDIA; NVIDIA; Mila - Quebec AI Institute+McGill University+Canada CIFAR AI Chair; NVIDIA", "aff_domain": "mila.quebec;nvidia.com; ; ; ; ; ", "email": "mila.quebec;nvidia.com; ; ; ; ; ", "github": "https://github.com/NVIDIA/Megatron-LM", "project": "", "author_num": 7, "aff_unique_index": "0+1;2;2;2;2;0+1+3;2", "aff_unique_norm": "Quebec AI Institute;McGill University;NVIDIA Corporation;Canadian Institute for Advanced Research", "aff_unique_dep": "AI Institute;;;AI Chair", "aff_unique_url": "https://mila.quebec;https://www.mcgill.ca;https://www.nvidia.com;https://www.cifar.ca", "aff_unique_abbr": "Mila;McGill;NVIDIA;CIFAR", "aff_campus_unique_index": ";", "aff_campus_unique": "", "aff_country_unique_index": "0+0;1;1;1;1;0+0+0;1", "aff_country_unique": "Canada;United States" }, { "id": "2021.acl-long.349", "title": "Energy-Based Reranking: Improving Neural Machine Translation Using Energy-Based Models", "track": "main", "status": "Long", "award": false, "abstract": "The discrepancy between maximum likelihood estimation (MLE) and task measures such as BLEU score has been studied before for autoregressive neural machine translation (NMT) and resulted in alternative training algorithms (Ranzato et al., 2016; Norouzi et al., 2016; Shen et al., 2016; Wu et al., 2018). However, MLE training remains the de facto approach for autoregressive NMT because of its computational efficiency and stability. Despite this mismatch between the training objective and task measure, we notice that the samples drawn from an MLE-based trained NMT support the desired distribution \u2013 there are samples with much higher BLEU score comparing to the beam decoding output. To benefit from this observation, we train an energy-based model to mimic the behavior of the task measure (i.e., the energy-based model assigns lower energy to samples with higher BLEU score), which is resulted in a re-ranking algorithm based on the samples drawn from NMT: energy-based re-ranking (EBR). We use both marginal energy models (over target sentence) and joint energy models (over both source and target sentences). Our EBR with the joint energy model consistently improves the performance of the Transformer-based NMT: +3.7 BLEU points on IWSLT\u201914 German-English, +3.37 BELU points on Sinhala-English, +1.4 BLEU points on WMT\u201916 English-German tasks.", "author": "Sumanta Bhattacharyya; Amirmohammad Rooshenas; Subhajit Naskar; Simeng Sun; Mohit Iyyer; Andrew McCallum", "authorids": "/s/sumanta-bhattacharyya/; /a/amirmohammad-rooshenas/; /s/subhajit-naskar/; /s/simeng-sun/; /m/mohit-iyyer/; /a/andrew-mccallum/", "bibtex": "@inproceedings{bhattacharyya-etal-2021-energy,\n title = \"Energy-Based Reranking: Improving Neural Machine Translation Using Energy-Based Models\",\n author = \"Bhattacharyya, Sumanta and\n Rooshenas, Amirmohammad and\n Naskar, Subhajit and\n Sun, Simeng and\n Iyyer, Mohit and\n McCallum, Andrew\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.349/\",\n doi = \"10.18653/v1/2021.acl-long.349\",\n pages = \"4528--4537\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.349.pdf", "site": "https://aclanthology.org/2021.acl-long.349/", "pdf_size": 514274, "gs_citation": 48, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=12766259251197529376&as_sdt=5,33&sciodt=0,33&hl=en", "gs_version_total": 6, "aff": "Department of Computer Science, College of Computing and Informatics, University of North Carolina Charlotte; Department of Computer Science, College of Computing and Informatics, University of North Carolina Charlotte; College of Information and Computer Science, University of Massachusetts Amherst; College of Information and Computer Science, University of Massachusetts Amherst; College of Information and Computer Science, University of Massachusetts Amherst; College of Information and Computer Science, University of Massachusetts Amherst", "aff_domain": "uncc.edu;uncc.edu;cs.umass.edu;cs.umass.edu;cs.umass.edu;cs.umass.edu", "email": "uncc.edu;uncc.edu;cs.umass.edu;cs.umass.edu;cs.umass.edu;cs.umass.edu", "github": "", "project": "", "author_num": 6, "aff_unique_index": "0;0;1;1;1;1", "aff_unique_norm": "University of North Carolina Charlotte;University of Massachusetts Amherst", "aff_unique_dep": "Department of Computer Science;College of Information and Computer Science", "aff_unique_url": "https://www.uncc.edu;https://www.umass.edu", "aff_unique_abbr": "UNCC;UMass Amherst", "aff_campus_unique_index": "0;0;1;1;1;1", "aff_campus_unique": "Charlotte;Amherst", "aff_country_unique_index": "0;0;0;0;0;0", "aff_country_unique": "United States" }, { "id": "2021.acl-short.22", "title": "Enforcing Consistency in Weakly Supervised Semantic Parsing", "track": "main", "status": "Short", "award": false, "abstract": "The predominant challenge in weakly supervised semantic parsing is that of spurious programs that evaluate to correct answers for the wrong reasons. Prior work uses elaborate search strategies to mitigate the prevalence of spurious programs; however, they typically consider only one input at a time. In this work we explore the use of consistency between the output programs for related inputs to reduce the impact of spurious programs. We bias the program search (and thus the model\u2019s training signal) towards programs that map the same phrase in related inputs to the same sub-parts in their respective programs. Additionally, we study the importance of designing logical formalisms that facilitate this kind of consistency-based training. We find that a more consistent formalism leads to improved model performance even without consistency-based training. When combined together, these two insights lead to a 10% absolute improvement over the best prior result on the Natural Language Visual Reasoning dataset.", "author": "Nitish Gupta; Sameer Singh; Matt Gardner", "authorids": "/n/nitish-gupta/; /s/sameer-singh/; /m/matt-gardner/", "bibtex": "@inproceedings{gupta-etal-2021-enforcing,\n title = \"Enforcing Consistency in Weakly Supervised Semantic Parsing\",\n author = \"Gupta, Nitish and\n Singh, Sameer and\n Gardner, Matt\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-short.22/\",\n doi = \"10.18653/v1/2021.acl-short.22\",\n pages = \"168--174\"\n}", "pdf": "https://aclanthology.org/2021.acl-short.22.pdf", "site": "https://aclanthology.org/2021.acl-short.22/", "pdf_size": 776665, "gs_citation": 6, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=2236131965713065081&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 8, "aff": "University of Pennsylvania; University of California, Irvine; Allen Institute for AI", "aff_domain": "seas.upenn.edu;uci.edu;allenai.org", "email": "seas.upenn.edu;uci.edu;allenai.org", "github": "", "project": "", "author_num": 3, "aff_unique_index": "0;1;2", "aff_unique_norm": "University of Pennsylvania;University of California, Irvine;Allen Institute for AI", "aff_unique_dep": ";;", "aff_unique_url": "https://www.upenn.edu;https://www.uci.edu;https://allenai.org", "aff_unique_abbr": "UPenn;UCI;AI2", "aff_campus_unique_index": "1", "aff_campus_unique": ";Irvine", "aff_country_unique_index": "0;0;0", "aff_country_unique": "United States" }, { "id": "2021.acl-long.3", "title": "Engage the Public: Poll Question Generation for Social Media Posts", "track": "main", "status": "Long", "award": false, "abstract": "This paper presents a novel task to generate poll questions for social media posts. It offers an easy way to hear the voice from the public and learn from their feelings to important social topics. While most related work tackles formal languages (e.g., exam papers), we generate poll questions for short and colloquial social media messages exhibiting severe data sparsity. To deal with that, we propose to encode user comments and discover latent topics therein as contexts. They are then incorporated into a sequence-to-sequence (S2S) architecture for question generation and its extension with dual decoders to additionally yield poll choices (answers). For experiments, we collect a large-scale Chinese dataset from Sina Weibo containing over 20K polls. The results show that our model outperforms the popular S2S models without exploiting topics from comments and the dual decoder design can further benefit the prediction of both questions and answers. Human evaluations further exhibit our superiority in yielding high-quality polls helpful to draw user engagements.", "author": "Zexin Lu; Keyang Ding; Yuji Zhang; Jing Li; Baolin Peng; Lemao Liu", "authorids": "/z/zexin-lu/; /k/keyang-ding/; /y/yuji-zhang/; /j/jing-li/; /b/baolin-peng/; /l/lemao-liu/", "bibtex": "@inproceedings{lu-etal-2021-engage,\n title = \"Engage the Public: Poll Question Generation for Social Media Posts\",\n author = \"Lu, Zexin and\n Ding, Keyang and\n Zhang, Yuji and\n Li, Jing and\n Peng, Baolin and\n Liu, Lemao\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.3/\",\n doi = \"10.18653/v1/2021.acl-long.3\",\n pages = \"29--40\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.3.pdf", "site": "https://aclanthology.org/2021.acl-long.3/", "pdf_size": 1328298, "gs_citation": 15, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=117624027855129679&as_sdt=5,33&sciodt=0,33&hl=en", "gs_version_total": 7, "aff": "Department of Computing, The Hong Kong Polytechnic University, HKSAR, China; Department of Computing, The Hong Kong Polytechnic University, HKSAR, China; Department of Computing, The Hong Kong Polytechnic University, HKSAR, China; Department of Computing, The Hong Kong Polytechnic University, HKSAR, China; Microsoft Research, Redmond, WA; Tencent AI Lab, Shenzhen, China", "aff_domain": "connect.polyu.hk;connect.polyu.hk;connect.polyu.hk;polyu.edu.hk;microsoft.com;tencent.com", "email": "connect.polyu.hk;connect.polyu.hk;connect.polyu.hk;polyu.edu.hk;microsoft.com;tencent.com", "github": "", "project": "", "author_num": 6, "aff_unique_index": "0;0;0;0;1;2", "aff_unique_norm": "The Hong Kong Polytechnic University;Microsoft Research;Tencent AI Lab", "aff_unique_dep": "Department of Computing;;AI Lab", "aff_unique_url": "https://www.polyu.edu.hk;https://www.microsoft.com/en-us/research;https://ai.tencent.com", "aff_unique_abbr": "PolyU;MSR;Tencent AI Lab", "aff_campus_unique_index": "1;2", "aff_campus_unique": ";Redmond;Shenzhen", "aff_country_unique_index": "0;0;0;0;1;0", "aff_country_unique": "China;United States" }, { "id": "2021.acl-long.8", "title": "Enhancing Content Preservation in Text Style Transfer Using Reverse Attention and Conditional Layer Normalization", "track": "main", "status": "Long", "award": false, "abstract": "Text style transfer aims to alter the style (e.g., sentiment) of a sentence while preserving its content. A common approach is to map a given sentence to content representation that is free of style, and the content representation is fed to a decoder with a target style. Previous methods in filtering style completely remove tokens with style at the token level, which incurs the loss of content information. In this paper, we propose to enhance content preservation by implicitly removing the style information of each token with reverse attention, and thereby retain the content. Furthermore, we fuse content information when building the target style representation, making it dynamic with respect to the content. Our method creates not only style-independent content representation, but also content-dependent style representation in transferring style. Empirical results show that our method outperforms the state-of-the-art baselines by a large margin in terms of content preservation. In addition, it is also competitive in terms of style transfer accuracy and fluency.", "author": "Dongkyu Lee; Zhiliang Tian; Lanqing Xue; Nevin L. Zhang", "authorids": "/d/dongkyu-lee/; /z/zhiliang-tian/; /l/lanqing-xue/; /n/nevin-l-zhang/", "bibtex": "@inproceedings{lee-etal-2021-enhancing,\n title = \"Enhancing Content Preservation in Text Style Transfer Using Reverse Attention and Conditional Layer Normalization\",\n author = \"Lee, Dongkyu and\n Tian, Zhiliang and\n Xue, Lanqing and\n Zhang, Nevin L.\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.8/\",\n doi = \"10.18653/v1/2021.acl-long.8\",\n pages = \"93--102\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.8.pdf", "site": "https://aclanthology.org/2021.acl-long.8/", "pdf_size": 2042960, "gs_citation": 50, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=12549329633335887267&as_sdt=5,33&sciodt=0,33&hl=en", "gs_version_total": 5, "aff": "Department of Computer Science and Engineering, The Hong Kong University of Science and Technology; Department of Computer Science and Engineering, The Hong Kong University of Science and Technology; Department of Computer Science and Engineering, The Hong Kong University of Science and Technology; Department of Computer Science and Engineering, The Hong Kong University of Science and Technology", "aff_domain": "cse.ust.hk;cse.ust.hk;cse.ust.hk;cse.ust.hk", "email": "cse.ust.hk;cse.ust.hk;cse.ust.hk;cse.ust.hk", "github": "", "project": "", "author_num": 4, "aff_unique_index": "0;0;0;0", "aff_unique_norm": "The Hong Kong University of Science and Technology", "aff_unique_dep": "Department of Computer Science and Engineering", "aff_unique_url": "https://www.ust.hk", "aff_unique_abbr": "HKUST", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0;0;0;0", "aff_country_unique": "China" }, { "id": "2021.acl-short.36", "title": "Enhancing Descriptive Image Captioning with Natural Language Inference", "track": "main", "status": "Short", "award": false, "abstract": "Generating descriptive sentences that convey non-trivial, detailed, and salient information about images is an important goal of image captioning. In this paper we propose a novel approach to encourage captioning models to produce more detailed captions using natural language inference, based on the motivation that, among different captions of an image, descriptive captions are more likely to entail less descriptive captions. Specifically, we construct directed inference graphs for reference captions based on natural language inference. A PageRank algorithm is then employed to estimate the descriptiveness score of each node. Built on that, we use reference sampling and weighted designated rewards to guide captioning to generate descriptive captions. The results on MSCOCO show that the proposed method outperforms the baselines significantly on a wide range of conventional and descriptiveness-related evaluation metrics.", "author": "Zhan Shi; Hui Liu; Xiaodan Zhu", "authorids": "/z/zhan-shi/; /h/hui-liu/; /x/xiaodan-zhu/", "bibtex": "@inproceedings{shi-etal-2021-enhancing,\n title = \"Enhancing Descriptive Image Captioning with Natural Language Inference\",\n author = \"Shi, Zhan and\n Liu, Hui and\n Zhu, Xiaodan\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-short.36/\",\n doi = \"10.18653/v1/2021.acl-short.36\",\n pages = \"269--277\"\n}", "pdf": "https://aclanthology.org/2021.acl-short.36.pdf", "site": "https://aclanthology.org/2021.acl-short.36/", "pdf_size": 1091539, "gs_citation": 23, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=17396509855836808031&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 2, "aff": "Ingenuity Labs Research Institute, Queen\u2019s University; Department of Electrical and Computer Engineering, Queen\u2019s University; Department of Electrical and Computer Engineering, Queen\u2019s University", "aff_domain": "queensu.ca;queensu.ca;queensu.ca", "email": "queensu.ca;queensu.ca;queensu.ca", "github": "https://github.com/Gitsamshi/Nli-image-caption", "project": "", "author_num": 3, "aff_unique_index": "0;0;0", "aff_unique_norm": "Queen\u2019s University", "aff_unique_dep": "Ingenuity Labs Research Institute", "aff_unique_url": "https://www.queensu.ca", "aff_unique_abbr": "Queen's U", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0;0;0", "aff_country_unique": "Canada" }, { "id": "2021.acl-short.4", "title": "Enhancing Entity Boundary Detection for Better Chinese Named Entity Recognition", "track": "main", "status": "Short", "award": false, "abstract": "In comparison with English, due to the lack of explicit word boundary and tenses information, Chinese Named Entity Recognition (NER) is much more challenging. In this paper, we propose a boundary enhanced approach for better Chinese NER. In particular, our approach enhances the boundary information from two perspectives. On one hand, we enhance the representation of the internal dependency of phrases by an additional Graph Attention Network(GAT) layer. On the other hand, taking the entity head-tail prediction (i.e., boundaries) as an auxiliary task, we propose an unified framework to learn the boundary information and recognize the NE jointly. Experiments on both the OntoNotes and the Weibo corpora show the effectiveness of our approach.", "author": "Chun Chen; Fang Kong", "authorids": "/c/chun-chen/; /f/fang-kong/", "bibtex": "https://aclanthology.org/2021.acl-short.4.bib", "pdf": "https://aclanthology.org/2021.acl-short.4.pdf", "site": "https://aclanthology.org/2021.acl-short.4/", "gs_citation": 43, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=10622199598545282946&as_sdt=5,44&sciodt=0,44&hl=en", "gs_version_total": 2, "aff": "School of Computer Science and Technology, Soochow University; School of Computer Science and Technology, Soochow University", "aff_domain": "stu.suda.edu.cn;suda.edu.cn", "email": "stu.suda.edu.cn;suda.edu.cn", "github": "", "project": "", "author_num": 2, "aff_unique_index": "0;0", "aff_unique_norm": "Soochow University", "aff_unique_dep": "School of Computer Science and Technology", "aff_unique_url": "https://eng.suda.edu.cn/", "aff_unique_abbr": "Soochow U", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0;0", "aff_country_unique": "China" }, { "id": "2021.acl-long.190", "title": "Enhancing the generalization for Intent Classification and Out-of-Domain Detection in SLU", "track": "main", "status": "Long", "award": false, "abstract": "Intent classification is a major task in spoken language understanding (SLU). Since most models are built with pre-collected in-domain (IND) training utterances, their ability to detect unsupported out-of-domain (OOD) utterances has a critical effect in practical use. Recent works have shown that using extra data and labels can improve the OOD detection performance, yet it could be costly to collect such data. This paper proposes to train a model with only IND data while supporting both IND intent classification and OOD detection. Our method designs a novel domain-regularized module (DRM) to reduce the overconfident phenomenon of a vanilla classifier, achieving a better generalization in both cases. Besides, DRM can be used as a drop-in replacement for the last layer in any neural network-based intent classifier, providing a low-cost strategy for a significant improvement. The evaluation on four datasets shows that our method built on BERT and RoBERTa models achieves state-of-the-art performance against existing approaches and the strong baselines we created for the comparisons.", "author": "Yilin Shen; Yen-Chang Hsu; Avik Ray; Hongxia Jin", "authorids": "/y/yilin-shen/; /y/yen-chang-hsu/; /a/avik-ray/; /h/hongxia-jin/", "bibtex": "@inproceedings{shen-etal-2021-enhancing,\n title = \"Enhancing the generalization for Intent Classification and Out-of-Domain Detection in {SLU}\",\n author = \"Shen, Yilin and\n Hsu, Yen-Chang and\n Ray, Avik and\n Jin, Hongxia\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.190/\",\n doi = \"10.18653/v1/2021.acl-long.190\",\n pages = \"2443--2453\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.190.pdf", "site": "https://aclanthology.org/2021.acl-long.190/", "pdf_size": 1058674, "gs_citation": 45, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=6739782714936693828&as_sdt=5,33&sciodt=0,33&hl=en", "gs_version_total": 4, "aff": "Samsung Research America; Samsung Research America; Samsung Research America; Samsung Research America", "aff_domain": "samsung.com;samsung.com;samsung.com;samsung.com", "email": "samsung.com;samsung.com;samsung.com;samsung.com", "github": "", "project": "", "author_num": 4, "aff_unique_index": "0;0;0;0", "aff_unique_norm": "Samsung Research America", "aff_unique_dep": "", "aff_unique_url": "https://www.samsung.com/us/careers/research/", "aff_unique_abbr": "SRA", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0;0;0;0", "aff_country_unique": "United States" }, { "id": "2021.acl-long.230", "title": "EnsLM: Ensemble Language Model for Data Diversity by Semantic Clustering", "track": "main", "status": "Long", "award": false, "abstract": "Natural language processing (NLP) often faces the problem of data diversity such as different domains, themes, styles, and so on. Therefore, a single language model (LM) is insufficient to learn all knowledge from diverse samples. To solve this problem, we firstly propose an autoencoding topic model with a mixture prior (mATM) to perform clustering for the data, where the clusters defined in semantic space describes the data diversity. Having obtained the clustering assignment for each sample, we develop the ensemble LM (EnsLM) with the technique of weight modulation. Specifically, EnsLM contains a backbone that is adjusted by a few modulated weights to fit for different sample clusters. As a result, the backbone learns the shared knowledge among all clusters while modulated weights extract the cluster-specific features. EnsLM can be trained jointly with mATM with a flexible LM backbone. We evaluate the effectiveness of both mATM and EnsLM on various tasks.", "author": "Zhibin Duan; Hao Zhang; Chaojie Wang; Zhengjue Wang; Bo Chen; Mingyuan Zhou", "authorids": "/z/zhibin-duan/; /h/hao-zhang/; /c/chaojie-wang/; /z/zhengjue-wang/; /b/bo-chen/; /m/mingyuan-zhou/", "bibtex": "@inproceedings{duan-etal-2021-enslm,\n title = \"{E}ns{LM}: Ensemble Language Model for Data Diversity by Semantic Clustering\",\n author = \"Duan, Zhibin and\n Zhang, Hao and\n Wang, Chaojie and\n Wang, Zhengjue and\n Chen, Bo and\n Zhou, Mingyuan\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.230/\",\n doi = \"10.18653/v1/2021.acl-long.230\",\n pages = \"2954--2967\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.230.pdf", "site": "https://aclanthology.org/2021.acl-long.230/", "pdf_size": 1039944, "gs_citation": 14, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=13342009909798694749&as_sdt=5,33&sciodt=0,33&hl=en", "gs_version_total": 4, "aff": "National Laboratory of Radar Signal Processing, Xidian University, Xi\u2019an, China; National Laboratory of Radar Signal Processing, Xidian University, Xi\u2019an, China; National Laboratory of Radar Signal Processing, Xidian University, Xi\u2019an, China; National Laboratory of Radar Signal Processing, Xidian University, Xi\u2019an, China; National Laboratory of Radar Signal Processing, Xidian University, Xi\u2019an, China; McCombs School of Business The University of Texas at Austin, Austin, TX 78712, USA", "aff_domain": "163.com;163.com; ; ;mail.xidian.edu.cn;mccombs.utexas.edu", "email": "163.com;163.com; ; ;mail.xidian.edu.cn;mccombs.utexas.edu", "github": "", "project": "", "author_num": 6, "aff_unique_index": "0;0;0;0;0;1", "aff_unique_norm": "Xidian University;The University of Texas at Austin", "aff_unique_dep": "National Laboratory of Radar Signal Processing;McCombs School of Business", "aff_unique_url": "http://www.xidian.edu.cn/;https://www.mccombs.utexas.edu", "aff_unique_abbr": "Xidian;UT Austin", "aff_campus_unique_index": "0;0;0;0;0;1", "aff_campus_unique": "Xi'an;Austin", "aff_country_unique_index": "0;0;0;0;0;1", "aff_country_unique": "China;United States" }, { "id": "2021.acl-short.124", "title": "Entity Concept-enhanced Few-shot Relation Extraction", "track": "main", "status": "Short", "award": false, "abstract": "Few-shot relation extraction (FSRE) is of great importance in long-tail distribution problem, especially in special domain with low-resource data. Most existing FSRE algorithms fail to accurately classify the relations merely based on the information of the sentences together with the recognized entity pairs, due to limited samples and lack of knowledge. To address this problem, in this paper, we proposed a novel entity CONCEPT-enhanced FEw-shot Relation Extraction scheme (ConceptFERE), which introduces the inherent concepts of entities to provide clues for relation prediction and boost the relations classification performance. Firstly, a concept-sentence attention module is developed to select the most appropriate concept from multiple concepts of each entity by calculating the semantic similarity between sentences and concepts. Secondly, a self-attention based fusion module is presented to bridge the gap of concept embedding and sentence embedding from different semantic spaces. Extensive experiments on the FSRE benchmark dataset FewRel have demonstrated the effectiveness and the superiority of the proposed ConceptFERE scheme as compared to the state-of-the-art baselines. Code is available at https://github.com/LittleGuoKe/ConceptFERE.", "author": "Shan Yang; Yongfei Zhang; Guanglin Niu; Qinghua Zhao; Shiliang Pu", "authorids": "/s/shan-yang/; /y/yongfei-zhang/; /g/guanglin-niu/; /q/qinghua-zhao/; /s/shiliang-pu/", "bibtex": "@inproceedings{yang-etal-2021-entity,\n title = \"Entity Concept-enhanced Few-shot Relation Extraction\",\n author = \"Yang, Shan and\n Zhang, Yongfei and\n Niu, Guanglin and\n Zhao, Qinghua and\n Pu, Shiliang\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-short.124/\",\n doi = \"10.18653/v1/2021.acl-short.124\",\n pages = \"987--991\"\n}", "pdf": "https://aclanthology.org/2021.acl-short.124.pdf", "site": "https://aclanthology.org/2021.acl-short.124/", "pdf_size": 626277, "gs_citation": 79, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=6138580740680180806&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 4, "aff": "Beijing Key Laboratory of Digital Media, School of Computer Science and Engineering, BeiHang University, Beijing 100191, China+State Key Laboratory of Virtual Reality Technology and Systems, BeiHang University, Beijing 100191, China+Pengcheng Laboratory, Shenzhen 518055, China; Beijing Key Laboratory of Digital Media, School of Computer Science and Engineering, BeiHang University, Beijing 100191, China+State Key Laboratory of Virtual Reality Technology and Systems, BeiHang University, Beijing 100191, China+Pengcheng Laboratory, Shenzhen 518055, China; Beijing Key Laboratory of Digital Media, School of Computer Science and Engineering, BeiHang University, Beijing 100191, China; State Key Laboratory of Software Development Environment School of Computer Science and Engineering, BeiHang University, Beijing 100191, China; Hikvision Research Institute, Hangzhou 311500, China", "aff_domain": "buaa.edu.cn;buaa.edu.cn;buaa.edu.cn;buaa.edu.cn;hikvision.com", "email": "buaa.edu.cn;buaa.edu.cn;buaa.edu.cn;buaa.edu.cn;hikvision.com", "github": "https://github.com/LittleGuoKe/ConceptFERE", "project": "", "author_num": 5, "aff_unique_index": "0+0+1;0+0+1;0;0;2", "aff_unique_norm": "BeiHang University;Pengcheng Laboratory;Hikvision Research Institute", "aff_unique_dep": "School of Computer Science and Engineering;;", "aff_unique_url": ";;https://www.hikvision.com/cn/", "aff_unique_abbr": "Beihang;;HRI", "aff_campus_unique_index": "0+0+1;0+0+1;0;0;2", "aff_campus_unique": "Beijing;Shenzhen;Hangzhou", "aff_country_unique_index": "0+0+0;0+0+0;0;0;0", "aff_country_unique": "China" }, { "id": "2021.acl-short.116", "title": "Entity Enhancement for Implicit Discourse Relation Classification in the Biomedical Domain", "track": "main", "status": "Short", "award": false, "abstract": "Implicit discourse relation classification is a challenging task, in particular when the text domain is different from the standard Penn Discourse Treebank (PDTB; Prasad et al., 2008) training corpus domain (Wall Street Journal in 1990s). We here tackle the task of implicit discourse relation classification on the biomedical domain, for which the Biomedical Discourse Relation Bank (BioDRB; Prasad et al., 2011) is available. We show that entity information can be used to improve discourse relational argument representation. In a first step, we show that explicitly marked instances that are content-wise similar to the target relations can be used to achieve good performance in the cross-domain setting using a simple unsupervised voting pipeline. As a further step, we show that with the linked entity information from the first step, a transformer which is augmented with entity-related information (KBERT; Liu et al., 2020) sets the new state of the art performance on the dataset, outperforming the large pre-trained BioBERT (Lee et al., 2020) model by 2% points.", "author": "Wei Shi; Vera Demberg", "authorids": "/w/wei-shi/; /v/vera-demberg/", "bibtex": "@inproceedings{shi-demberg-2021-entity,\n title = \"Entity Enhancement for Implicit Discourse Relation Classification in the Biomedical Domain\",\n author = \"Shi, Wei and\n Demberg, Vera\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-short.116/\",\n doi = \"10.18653/v1/2021.acl-short.116\",\n pages = \"925--931\"\n}", "pdf": "https://aclanthology.org/2021.acl-short.116.pdf", "site": "https://aclanthology.org/2021.acl-short.116/", "pdf_size": 854795, "gs_citation": 5, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=1931043809705894814&as_sdt=5,44&sciodt=0,44&hl=en", "gs_version_total": 4, "aff": "Alibaba Group, Hangzhou, China+Dept. of Language Science and Technology; Dept. of Language Science and Technology+Dept. of Mathematics and Computer Science, Saarland University", "aff_domain": "coli.uni-saarland.de;coli.uni-saarland.de", "email": "coli.uni-saarland.de;coli.uni-saarland.de", "github": "", "project": "", "author_num": 2, "aff_unique_index": "0+1;1+2", "aff_unique_norm": "Alibaba Group;University of California, San Diego;Saarland University", "aff_unique_dep": ";Department of Language Science and Technology;Dept. of Mathematics and Computer Science", "aff_unique_url": "https://www.alibaba.com;https://linguistics.ucsd.edu;https://www.uni-saarland.de", "aff_unique_abbr": "Alibaba;UCSD;Saarland U", "aff_campus_unique_index": "0+1;1", "aff_campus_unique": "Hangzhou;La Jolla;", "aff_country_unique_index": "0+1;1+2", "aff_country_unique": "China;United States;Germany" }, { "id": "2021.acl-long.345", "title": "Evaluating Entity Disambiguation and the Role of Popularity in Retrieval-Based NLP", "track": "main", "status": "Long", "award": false, "abstract": "Retrieval is a core component for open-domain NLP tasks. In open-domain tasks, multiple entities can share a name, making disambiguation an inherent yet under-explored problem. We propose an evaluation benchmark for assessing the entity disambiguation capabilities of these retrievers, which we call Ambiguous Entity Retrieval (AmbER) sets. We define an AmbER set as a collection of entities that share a name along with queries about those entities. By covering the set of entities for polysemous names, AmbER sets act as a challenging test of entity disambiguation. We create AmbER sets for three popular open-domain tasks: fact checking, slot filling, and question answering, and evaluate a diverse set of retrievers. We find that the retrievers exhibit popularity bias, significantly under-performing on rarer entities that share a name, e.g., they are twice as likely to retrieve erroneous documents on queries for the less popular entity under the same name. These experiments on AmbER sets show their utility as an evaluation tool and highlight the weaknesses of popular retrieval systems.", "author": "Anthony Chen; Pallavi Gudipati; Shayne Longpre; Xiao Ling; Sameer Singh", "authorids": "/a/anthony-chen/; /p/pallavi-gudipati/; /s/shayne-longpre/; /x/xiao-ling/; /s/sameer-singh/", "bibtex": "@inproceedings{chen-etal-2021-evaluating,\n title = \"Evaluating Entity Disambiguation and the Role of Popularity in Retrieval-Based {NLP}\",\n author = \"Chen, Anthony and\n Gudipati, Pallavi and\n Longpre, Shayne and\n Ling, Xiao and\n Singh, Sameer\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.345/\",\n doi = \"10.18653/v1/2021.acl-long.345\",\n pages = \"4472--4485\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.345.pdf", "site": "https://aclanthology.org/2021.acl-long.345/", "pdf_size": 676931, "gs_citation": 48, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=16668638908979206401&as_sdt=5,33&sciodt=0,33&hl=en", "gs_version_total": 4, "aff": "University of California, Irvine + Apple; Apple; Apple; Apple; University of California, Irvine", "aff_domain": "uci.edu;apple.com;apple.com;apple.com;uci.edu", "email": "uci.edu;apple.com;apple.com;apple.com;uci.edu", "github": "https://github.com/anthonywchen/AmbER-Sets", "project": "", "author_num": 5, "aff_unique_index": "0+1;1;1;1;0", "aff_unique_norm": "University of California, Irvine;Apple Inc.", "aff_unique_dep": ";", "aff_unique_url": "https://www.uci.edu;https://www.apple.com", "aff_unique_abbr": "UCI;Apple", "aff_campus_unique_index": "0;0", "aff_campus_unique": "Irvine;", "aff_country_unique_index": "0+0;0;0;0;0", "aff_country_unique": "United States" }, { "id": "2021.acl-long.214", "title": "Evaluating Evaluation Measures for Ordinal Classification and Ordinal Quantification", "track": "main", "status": "Long", "award": false, "abstract": "Ordinal Classification (OC) is an important classification task where the classes are ordinal. For example, an OC task for sentiment analysis could have the following classes: highly positive, positive, neutral, negative, highly negative. Clearly, evaluation measures for an OC task should penalise misclassifications by considering the ordinal nature of the classes. Ordinal Quantification (OQ) is a related task where the gold data is a distribution over ordinal classes, and the system is required to estimate this distribution. Evaluation measures for an OQ task should also take the ordinal nature of the classes into account. However, for both OC and OQ, there are only a small number of known evaluation measures that meet this basic requirement. In the present study, we utilise data from the SemEval and NTCIR communities to clarify the properties of nine evaluation measures in the context of OC tasks, and six measures in the context of OQ tasks.", "author": "Tetsuya Sakai", "authorids": "/t/tetsuya-sakai/", "bibtex": "@inproceedings{sakai-2021-evaluating,\n title = \"Evaluating Evaluation Measures for Ordinal Classification and Ordinal Quantification\",\n author = \"Sakai, Tetsuya\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.214/\",\n doi = \"10.18653/v1/2021.acl-long.214\",\n pages = \"2759--2769\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.214.pdf", "site": "https://aclanthology.org/2021.acl-long.214/", "pdf_size": 793698, "gs_citation": 22, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=6145918073099280910&as_sdt=5,33&sciodt=0,33&hl=en", "gs_version_total": 5, "aff": "Waseda University", "aff_domain": "acm.org", "email": "acm.org", "github": "", "project": "", "author_num": 1, "aff_unique_index": "0", "aff_unique_norm": "Waseda University", "aff_unique_dep": "", "aff_unique_url": "https://www.waseda.jp/top", "aff_unique_abbr": "Waseda", "aff_country_unique_index": "0", "aff_country_unique": "Japan" }, { "id": "2021.acl-long.244", "title": "Evaluating morphological typology in zero-shot cross-lingual transfer", "track": "main", "status": "Long", "award": false, "abstract": "Cross-lingual transfer has improved greatly through multi-lingual language model pretraining, reducing the need for parallel data and increasing absolute performance. However, this progress has also brought to light the differences in performance across languages. Specifically, certain language families and typologies seem to consistently perform worse in these models. In this paper, we address what effects morphological typology has on zero-shot cross-lingual transfer for two tasks: Part-of-speech tagging and sentiment analysis. We perform experiments on 19 languages from four language typologies (fusional, isolating, agglutinative, and introflexive) and find that transfer to another morphological type generally implies a higher loss than transfer to another language with the same morphological typology. Furthermore, POS tagging is more sensitive to morphological typology than sentiment analysis and, on this task, models perform much better on fusional languages than on the other typologies.", "author": "Antonio Mart\u00ednez-Garc\u00eda; Toni Badia; Jeremy Barnes", "authorids": "/a/antonio-martinez-garcia/; /t/toni-badia/; /j/jeremy-barnes/", "bibtex": "@inproceedings{martinez-garcia-etal-2021-evaluating,\n title = \"Evaluating morphological typology in zero-shot cross-lingual transfer\",\n author = \"Mart{\\'i}nez-Garc{\\'i}a, Antonio and\n Badia, Toni and\n Barnes, Jeremy\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.244/\",\n doi = \"10.18653/v1/2021.acl-long.244\",\n pages = \"3136--3153\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.244.pdf", "site": "https://aclanthology.org/2021.acl-long.244/", "pdf_size": 544218, "gs_citation": 17, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=15946785104637555484&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 7, "aff": "Universitat de Barcelona; Universitat Pompeu Fabra; University of Oslo", "aff_domain": "gmail.com;upf.edu;ifi.uio.no", "email": "gmail.com;upf.edu;ifi.uio.no", "github": "https://github.com/jerbarnes/typology_of_crosslingual", "project": "", "author_num": 3, "aff_unique_index": "0;1;2", "aff_unique_norm": "Universitat de Barcelona;Universitat Pompeu Fabra;University of Oslo", "aff_unique_dep": ";;", "aff_unique_url": "https://www.ub.edu;https://www.upf.edu/;https://www.uio.no", "aff_unique_abbr": "UB;UPF;UiO", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0;0;1", "aff_country_unique": "Spain;Norway" }, { "id": "2021.acl-long.346", "title": "Evaluation Examples are not Equally Informative: How should that change NLP Leaderboards?", "track": "main", "status": "Long", "award": false, "abstract": "Leaderboards are widely used in NLP and push the field forward. While leaderboards are a straightforward ranking of NLP models, this simplicity can mask nuances in evaluation items (examples) and subjects (NLP models). Rather than replace leaderboards, we advocate a re-imagining so that they better highlight if and where progress is made. Building on educational testing, we create a Bayesian leaderboard model where latent subject skill and latent item difficulty predict correct responses. Using this model, we analyze the ranking reliability of leaderboards. Afterwards, we show the model can guide what to annotate, identify annotation errors, detect overfitting, and identify informative examples. We conclude with recommendations for future benchmark tasks.", "author": "Pedro Rodriguez; Joe Barrow; Alexander Miserlis Hoyle; John P. Lalor; Robin Jia; Jordan Boyd-Graber", "authorids": "/p/pedro-rodriguez/; /j/joe-barrow/; /a/alexander-miserlis-hoyle/; /j/john-p-lalor/; /r/robin-jia/; /j/jordan-boyd-graber/", "bibtex": "@inproceedings{rodriguez-etal-2021-evaluation,\n title = \"Evaluation Examples are not Equally Informative: How should that change {NLP} Leaderboards?\",\n author = \"Rodriguez, Pedro and\n Barrow, Joe and\n Hoyle, Alexander Miserlis and\n Lalor, John P. and\n Jia, Robin and\n Boyd-Graber, Jordan\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.346/\",\n doi = \"10.18653/v1/2021.acl-long.346\",\n pages = \"4486--4503\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.346.pdf", "site": "https://aclanthology.org/2021.acl-long.346/", "pdf_size": 2207531, "gs_citation": 90, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=9611371869389669417&as_sdt=5,33&sciodt=0,33&hl=en", "gs_version_total": 5, "aff": "University of Maryland + Facebook Reality Labs; University of Maryland; University of Maryland; University of Notre Dame; University of Southern California; University of Maryland", "aff_domain": "pedro.ai;cs.umd.edu;umd.edu;nd.edu;usc.edu;umiacs.umd.edu", "email": "pedro.ai;cs.umd.edu;umd.edu;nd.edu;usc.edu;umiacs.umd.edu", "github": "", "project": "irt.pedro.ai", "author_num": 6, "aff_unique_index": "0+1;0;0;2;3;0", "aff_unique_norm": "University of Maryland;Facebook Reality Labs;University of Notre Dame;University of Southern California", "aff_unique_dep": ";Facebook Reality Labs;;", "aff_unique_url": "https://www/umd.edu;https://www.facebook.com/realitylabs;https://www.nd.edu;https://www.usc.edu", "aff_unique_abbr": "UMD;FRL;Notre Dame;USC", "aff_campus_unique_index": ";1", "aff_campus_unique": ";Los Angeles", "aff_country_unique_index": "0+0;0;0;0;0;0", "aff_country_unique": "United States" }, { "id": "2021.acl-long.530", "title": "Evaluation of Thematic Coherence in Microblogs", "track": "main", "status": "Long", "award": false, "abstract": "Collecting together microblogs representing opinions about the same topics within the same timeframe is useful to a number of different tasks and practitioners. A major question is how to evaluate the quality of such thematic clusters. Here we create a corpus of microblog clusters from three different domains and time windows and define the task of evaluating thematic coherence. We provide annotation guidelines and human annotations of thematic coherence by journalist experts. We subsequently investigate the efficacy of different automated evaluation metrics for the task. We consider a range of metrics including surface level metrics, ones for topic model coherence and text generation metrics (TGMs). While surface level metrics perform well, outperforming topic coherence metrics, they are not as consistent as TGMs. TGMs are more reliable than all other metrics considered for capturing thematic coherence in microblog clusters due to being less sensitive to the effect of time windows.", "author": "Iman Munire Bilal; Bo Wang; Maria Liakata; Rob Procter; Adam Tsakalidis", "authorids": "/i/iman-munire-bilal/; /b/bo-wang/; /m/maria-liakata/; /r/rob-procter/; /a/adam-tsakalidis/", "bibtex": "@inproceedings{bilal-etal-2021-evaluation,\n title = \"Evaluation of Thematic Coherence in Microblogs\",\n author = \"Bilal, Iman Munire and\n Wang, Bo and\n Liakata, Maria and\n Procter, Rob and\n Tsakalidis, Adam\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.530/\",\n doi = \"10.18653/v1/2021.acl-long.530\",\n pages = \"6800--6814\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.530.pdf", "site": "https://aclanthology.org/2021.acl-long.530/", "pdf_size": 363432, "gs_citation": 11, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=16733886948411271023&as_sdt=5,33&sciodt=0,33&hl=en", "gs_version_total": 7, "aff": "Department of Computer Science, University of Warwick + The Alan Turing Institute, London, UK; Department of Psychiatry, University of Oxford + The Alan Turing Institute, London, UK; School of Electronic Engineering and Computer Science, Queen Mary University of London + The Alan Turing Institute, London, UK; Department of Computer Science, University of Warwick + The Alan Turing Institute, London, UK; School of Electronic Engineering and Computer Science, Queen Mary University of London + The Alan Turing Institute, London, UK", "aff_domain": "warwick.ac.uk;turing.ac.uk;turing.ac.uk;warwick.ac.uk;turing.ac.uk", "email": "warwick.ac.uk;turing.ac.uk;turing.ac.uk;warwick.ac.uk;turing.ac.uk", "github": "", "project": "", "author_num": 5, "aff_unique_index": "0+1;2+1;3+1;0+1;3+1", "aff_unique_norm": "University of Warwick;The Alan Turing Institute;University of Oxford;Queen Mary University of London", "aff_unique_dep": "Department of Computer Science;;Department of Psychiatry;School of Electronic Engineering and Computer Science", "aff_unique_url": "https://warwick.ac.uk;https://www.turing.ac.uk;https://www.ox.ac.uk;https://www.qmul.ac.uk", "aff_unique_abbr": "Warwick;ATI;Oxford;QMUL", "aff_campus_unique_index": "1;2+1;1+1;1;1+1", "aff_campus_unique": ";London;Oxford", "aff_country_unique_index": "0+0;0+0;0+0;0+0;0+0", "aff_country_unique": "United Kingdom" }, { "id": "2021.acl-long.262", "title": "Every Bite Is an Experience: Key Point Analysis of Business Reviews", "track": "main", "status": "Long", "award": false, "abstract": "Previous work on review summarization focused on measuring the sentiment toward the main aspects of the reviewed product or business, or on creating a textual summary. These approaches provide only a partial view of the data: aspect-based sentiment summaries lack sufficient explanation or justification for the aspect rating, while textual summaries do not quantify the significance of each element, and are not well-suited for representing conflicting views. Recently, Key Point Analysis (KPA) has been proposed as a summarization framework that provides both textual and quantitative summary of the main points in the data. We adapt KPA to review data by introducing Collective Key Point Mining for better key point extraction; integrating sentiment analysis into KPA; identifying good key point candidates for review summaries; and leveraging the massive amount of available reviews and their metadata. We show empirically that these novel extensions of KPA substantially improve its performance. We demonstrate that promising results can be achieved without any domain-specific annotation, while human supervision can lead to further improvement.", "author": "Roy Bar-Haim; Lilach Eden; Yoav Kantor; Roni Friedman; Noam Slonim", "authorids": "/r/roy-bar-haim/; /l/lilach-eden/; /y/yoav-kantor/; /r/roni-friedman/; /n/noam-slonim/", "bibtex": "@inproceedings{bar-haim-etal-2021-every,\n title = \"Every Bite Is an Experience: {K}ey {P}oint {A}nalysis of Business Reviews\",\n author = \"Bar-Haim, Roy and\n Eden, Lilach and\n Kantor, Yoav and\n Friedman, Roni and\n Slonim, Noam\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.262/\",\n doi = \"10.18653/v1/2021.acl-long.262\",\n pages = \"3376--3386\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.262.pdf", "site": "https://aclanthology.org/2021.acl-long.262/", "pdf_size": 404475, "gs_citation": 25, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=1213514611309153201&as_sdt=5,33&sciodt=0,33&hl=en", "gs_version_total": 5, "aff": "IBM Research; IBM Research; IBM Research; IBM Research; IBM Research", "aff_domain": "il.ibm.com;il.ibm.com;il.ibm.com;il.ibm.com;il.ibm.com", "email": "il.ibm.com;il.ibm.com;il.ibm.com;il.ibm.com;il.ibm.com", "github": "", "project": "", "author_num": 5, "aff_unique_index": "0;0;0;0;0", "aff_unique_norm": "IBM", "aff_unique_dep": "IBM Research", "aff_unique_url": "https://www.ibm.com/research", "aff_unique_abbr": "IBM", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0;0;0;0;0", "aff_country_unique": "United States" }, { "id": "2021.acl-long.256", "title": "Evidence-based Factual Error Correction", "track": "main", "status": "Long", "award": false, "abstract": "This paper introduces the task of factual error correction: performing edits to a claim so that the generated rewrite is better supported by evidence. This extends the well-studied task of fact verification by providing a mechanism to correct written texts that are refuted or only partially supported by evidence. We demonstrate that it is feasible to train factual error correction systems from existing fact checking datasets which only contain labeled claims accompanied by evidence, but not the correction. We achieve this by employing a two-stage distant supervision approach that incorporates evidence into masked claims when generating corrections. Our approach, based on the T5 transformer and using retrieved evidence, achieved better results than existing work which used a pointer copy network and gold evidence, producing accurate factual error corrections for 5x more instances in human evaluation and a .125 increase in SARI score. The evaluation is conducted on a dataset of 65,000 instances based on a recent fact verification shared task and we release it to enable further work on the task.", "author": "James Thorne; Andreas Vlachos", "authorids": "/j/james-thorne/; /a/andreas-vlachos/", "bibtex": "@inproceedings{thorne-vlachos-2021-evidence,\n title = \"Evidence-based Factual Error Correction\",\n author = \"Thorne, James and\n Vlachos, Andreas\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.256/\",\n doi = \"10.18653/v1/2021.acl-long.256\",\n pages = \"3298--3309\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.256.pdf", "site": "https://aclanthology.org/2021.acl-long.256/", "pdf_size": 408352, "gs_citation": 65, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=13608293482972218689&as_sdt=5,44&sciodt=0,44&hl=en", "gs_version_total": 10, "aff": "Department of Computer Science, University of Cambridge; Department of Computer Science, University of Cambridge", "aff_domain": "cam.ac.uk;cam.ac.uk", "email": "cam.ac.uk;cam.ac.uk", "github": "https://github.com/j6mes/2021-acl-factual-error-correction", "project": "", "author_num": 2, "aff_unique_index": "0;0", "aff_unique_norm": "University of Cambridge", "aff_unique_dep": "Department of Computer Science", "aff_unique_url": "https://www.cam.ac.uk", "aff_unique_abbr": "Cambridge", "aff_campus_unique_index": "0;0", "aff_campus_unique": "Cambridge", "aff_country_unique_index": "0;0", "aff_country_unique": "United Kingdom" }, { "id": "2021.acl-long.183", "title": "ExCAR: Event Graph Knowledge Enhanced Explainable Causal Reasoning", "track": "main", "status": "Long", "award": false, "abstract": "Prior work infers the causation between events mainly based on the knowledge induced from the annotated causal event pairs. However, additional evidence information intermediate to the cause and effect remains unexploited. By incorporating such information, the logical law behind the causality can be unveiled, and the interpretability and stability of the causal reasoning system can be improved. To facilitate this, we present an Event graph knowledge enhanced explainable CAusal Reasoning framework (ExCAR). ExCAR first acquires additional evidence information from a large-scale causal event graph as logical rules for causal reasoning. To learn the conditional probabilistic of logical rules, we propose the Conditional Markov Neural Logic Network (CMNLN) that combines the representation learning and structure learning of logical rules in an end-to-end differentiable manner. Experimental results demonstrate that ExCAR outperforms previous state-of-the-art methods. Adversarial evaluation shows the improved stability of ExCAR over baseline systems. Human evaluation shows that ExCAR can achieve a promising explainable performance.", "author": "Li Du; Xiao Ding; Kai Xiong; Ting Liu; Bing Qin", "authorids": "/l/li-du/; /x/xiao-ding/; /k/kai-xiong/; /t/ting-liu/; /b/bing-qin/", "bibtex": "@inproceedings{du-etal-2021-excar,\n title = \"{E}x{CAR}: Event Graph Knowledge Enhanced Explainable Causal Reasoning\",\n author = \"Du, Li and\n Ding, Xiao and\n Xiong, Kai and\n Liu, Ting and\n Qin, Bing\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.183/\",\n doi = \"10.18653/v1/2021.acl-long.183\",\n pages = \"2354--2363\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.183.pdf", "site": "https://aclanthology.org/2021.acl-long.183/", "pdf_size": 722699, "gs_citation": 35, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=17162425603683950968&as_sdt=80000005&sciodt=0,23&hl=en", "gs_version_total": 3, "aff": "Research Center for Social Computing and Information Retrieval, Harbin Institute of Technology, China; Research Center for Social Computing and Information Retrieval, Harbin Institute of Technology, China; Research Center for Social Computing and Information Retrieval, Harbin Institute of Technology, China; Research Center for Social Computing and Information Retrieval, Harbin Institute of Technology, China; Research Center for Social Computing and Information Retrieval, Harbin Institute of Technology, China", "aff_domain": "ir.hit.edu.cn;ir.hit.edu.cn;ir.hit.edu.cn;ir.hit.edu.cn;ir.hit.edu.cn", "email": "ir.hit.edu.cn;ir.hit.edu.cn;ir.hit.edu.cn;ir.hit.edu.cn;ir.hit.edu.cn", "github": "", "project": "", "author_num": 5, "aff_unique_index": "0;0;0;0;0", "aff_unique_norm": "Harbin Institute of Technology", "aff_unique_dep": "Research Center for Social Computing and Information Retrieval", "aff_unique_url": "http://www.hit.edu.cn/", "aff_unique_abbr": "HIT", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0;0;0;0;0", "aff_country_unique": "China" }, { "id": "2021.acl-long.38", "title": "Examining the Inductive Bias of Neural Language Models with Artificial Languages", "track": "main", "status": "Long", "award": false, "abstract": "Since language models are used to model a wide variety of languages, it is natural to ask whether the neural architectures used for the task have inductive biases towards modeling particular types of languages. Investigation of these biases has proved complicated due to the many variables that appear in the experimental setup. Languages vary in many typological dimensions, and it is difficult to single out one or two to investigate without the others acting as confounders. We propose a novel method for investigating the inductive biases of language models using artificial languages. These languages are constructed to allow us to create parallel corpora across languages that differ only in the typological feature being investigated, such as word order. We then use them to train and test language models. This constitutes a fully controlled causal framework, and demonstrates how grammar engineering can serve as a useful tool for analyzing neural models. Using this method, we find that commonly used neural architectures exhibit different inductive biases: LSTMs display little preference with respect to word ordering, while transformers display a clear preference for some orderings over others. Further, we find that neither the inductive bias of the LSTM nor that of the transformer appear to reflect any tendencies that we see in attested natural languages.", "author": "Jennifer C. White; Ryan Cotterell", "authorids": "/j/jennifer-c-white/; /r/ryan-cotterell/", "bibtex": "@inproceedings{white-cotterell-2021-examining,\n title = \"Examining the Inductive Bias of Neural Language Models with Artificial Languages\",\n author = \"White, Jennifer C. and\n Cotterell, Ryan\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.38/\",\n doi = \"10.18653/v1/2021.acl-long.38\",\n pages = \"454--463\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.38.pdf", "site": "https://aclanthology.org/2021.acl-long.38/", "pdf_size": 465873, "gs_citation": 57, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=10350159405383967699&as_sdt=40000005&sciodt=0,22&hl=en", "gs_version_total": 9, "aff": "University of Cambridge; ETH Z\u00fcrich", "aff_domain": "cam.ac.uk;inf.ethz.ch", "email": "cam.ac.uk;inf.ethz.ch", "github": "", "project": "", "author_num": 2, "aff_unique_index": "0;1", "aff_unique_norm": "University of Cambridge;ETH Z\u00fcrich", "aff_unique_dep": ";", "aff_unique_url": "https://www.cam.ac.uk;https://www.ethz.ch", "aff_unique_abbr": "Cambridge;ETHZ", "aff_campus_unique_index": "0", "aff_campus_unique": "Cambridge;", "aff_country_unique_index": "0;1", "aff_country_unique": "United Kingdom;Switzerland" }, { "id": "2021.acl-long.88", "title": "Explainable Prediction of Text Complexity: The Missing Preliminaries for Text Simplification", "track": "main", "status": "Long", "award": false, "abstract": "Text simplification reduces the language complexity of professional content for accessibility purposes. End-to-end neural network models have been widely adopted to directly generate the simplified version of input text, usually functioning as a blackbox. We show that text simplification can be decomposed into a compact pipeline of tasks to ensure the transparency and explainability of the process. The first two steps in this pipeline are often neglected: 1) to predict whether a given piece of text needs to be simplified, and 2) if yes, to identify complex parts of the text. The two tasks can be solved separately using either lexical or deep learning methods, or solved jointly. Simply applying explainable complexity prediction as a preliminary step, the out-of-sample text simplification performance of the state-of-the-art, black-box simplification models can be improved by a large margin.", "author": "Cristina Garbacea; Mengtian Guo; Samuel Carton; Qiaozhu Mei", "authorids": "/c/cristina-garbacea/; /m/mengtian-guo/; /s/samuel-carton/; /q/qiaozhu-mei/", "bibtex": "@inproceedings{garbacea-etal-2021-explainable,\n title = \"Explainable Prediction of Text Complexity: The Missing Preliminaries for Text Simplification\",\n author = \"Garbacea, Cristina and\n Guo, Mengtian and\n Carton, Samuel and\n Mei, Qiaozhu\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.88/\",\n doi = \"10.18653/v1/2021.acl-long.88\",\n pages = \"1086--1097\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.88.pdf", "site": "https://aclanthology.org/2021.acl-long.88/", "pdf_size": 421383, "gs_citation": 30, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=14284076660637828541&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 9, "aff": "Department of EECS, University of Michigan, Ann Arbor; School of Information and Library Science, University of North Carolina, Chapel Hill; Department of CS, University of Colorado, Boulder; Department of EECS, University of Michigan, Ann Arbor+School of Information, University of Michigan, Ann Arbor", "aff_domain": "cs.washington.edu; ; ; ", "email": "cs.washington.edu; ; ; ", "github": "", "project": "", "author_num": 4, "aff_unique_index": "0;1;2;0+0", "aff_unique_norm": "University of Michigan;University of North Carolina;University of Colorado", "aff_unique_dep": "Department of EECS;School of Information and Library Science;Department of CS", "aff_unique_url": "https://www.umich.edu;https://www.unc.edu;https://www.colorado.edu", "aff_unique_abbr": "UM;UNC;CU", "aff_campus_unique_index": "0;1;2;0+0", "aff_campus_unique": "Ann Arbor;Chapel Hill;Boulder", "aff_country_unique_index": "0;0;0;0+0", "aff_country_unique": "United States" }, { "id": "2021.acl-long.39", "title": "Explaining Contextualization in Language Models using Visual Analytics", "track": "main", "status": "Long", "award": false, "abstract": "Despite the success of contextualized language models on various NLP tasks, it is still unclear what these models really learn. In this paper, we contribute to the current efforts of explaining such models by exploring the continuum between function and content words with respect to contextualization in BERT, based on linguistically-informed insights. In particular, we utilize scoring and visual analytics techniques: we use an existing similarity-based score to measure contextualization and integrate it into a novel visual analytics technique, presenting the model\u2019s layers simultaneously and highlighting intra-layer properties and inter-layer differences. We show that contextualization is neither driven by polysemy nor by pure context variation. We also provide insights on why BERT fails to model words in the middle of the functionality continuum.", "author": "Rita Sevastjanova; Aikaterini-Lida Kalouli; Christin Beck; Hanna Sch\u00e4fer; Mennatallah El-Assady", "authorids": "/r/rita-sevastjanova/; /a/aikaterini-lida-kalouli/; /c/christin-beck/; /h/hanna-schafer/; /m/mennatallah-el-assady/", "bibtex": "@inproceedings{sevastjanova-etal-2021-explaining,\n title = \"Explaining Contextualization in Language Models using Visual Analytics\",\n author = {Sevastjanova, Rita and\n Kalouli, Aikaterini-Lida and\n Beck, Christin and\n Sch{\\\"a}fer, Hanna and\n El-Assady, Mennatallah},\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.39/\",\n doi = \"10.18653/v1/2021.acl-long.39\",\n pages = \"464--476\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.39.pdf", "site": "https://aclanthology.org/2021.acl-long.39/", "pdf_size": 1874174, "gs_citation": 25, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=5730439260912564357&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 7, "aff": "University of Konstanz; University of Konstanz; University of Konstanz; University of Konstanz; University of Konstanz", "aff_domain": "uni-konstanz.de;uni-konstanz.de;uni-konstanz.de;uni-konstanz.de;uni-konstanz.de", "email": "uni-konstanz.de;uni-konstanz.de;uni-konstanz.de;uni-konstanz.de;uni-konstanz.de", "github": "", "project": "", "author_num": 5, "aff_unique_index": "0;0;0;0;0", "aff_unique_norm": "University of Konstanz", "aff_unique_dep": "", "aff_unique_url": "https://www.uni-konstanz.de", "aff_unique_abbr": "Uni Konstanz", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0;0;0;0;0", "aff_country_unique": "Germany" }, { "id": "2021.acl-long.166", "title": "Explaining Relationships Between Scientific Documents", "track": "main", "status": "Long", "award": false, "abstract": "We address the task of explaining relationships between two scientific documents using natural language text. This task requires modeling the complex content of long technical documents, deducing a relationship between these documents, and expressing the details of that relationship in text. In addition to the theoretical interest of this task, successful solutions can help improve researcher efficiency in search and review. In this paper we establish a dataset of 622K examples from 154K documents. We pretrain a large language model to serve as the foundation for autoregressive approaches to the task. We explore the impact of taking different views on the two documents, including the use of dense representations extracted with scientific IE systems. We provide extensive automatic and human evaluations which show the promise of such models, but make clear challenges for future work.", "author": "Kelvin Luu; Xinyi Wu; Rik Koncel-Kedziorski; Kyle Lo; Isabel Cachola; Noah A. Smith", "authorids": "/k/kelvin-luu/; /x/xinyi-wu/; /r/rik-koncel-kedziorski/; /k/kyle-lo/; /i/isabel-cachola/; /n/noah-a-smith/", "bibtex": "@inproceedings{luu-etal-2021-explaining,\n title = \"Explaining Relationships Between Scientific Documents\",\n author = \"Luu, Kelvin and\n Wu, Xinyi and\n Koncel-Kedziorski, Rik and\n Lo, Kyle and\n Cachola, Isabel and\n Smith, Noah A.\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.166/\",\n doi = \"10.18653/v1/2021.acl-long.166\",\n pages = \"2130--2144\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.166.pdf", "site": "https://aclanthology.org/2021.acl-long.166/", "pdf_size": 548263, "gs_citation": 57, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=16399981651571984030&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 7, "aff": "University of Washington; University of Washington; University of Washington; Allen Institute for AI; Johns Hopkins University; University of Washington+Allen Institute for AI", "aff_domain": "cs.washington.edu;uw.edu;uw.edu;allenai.org;cs.jhu.edu;cs.washington.edu", "email": "cs.washington.edu;uw.edu;uw.edu;allenai.org;cs.jhu.edu;cs.washington.edu", "github": "", "project": "", "author_num": 6, "aff_unique_index": "0;0;0;1;2;0+1", "aff_unique_norm": "University of Washington;Allen Institute for AI;Johns Hopkins University", "aff_unique_dep": ";;", "aff_unique_url": "https://www.washington.edu;https://allenai.org;https://www.jhu.edu", "aff_unique_abbr": "UW;AI2;JHU", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0;0;0;0;0;0+0", "aff_country_unique": "United States" }, { "id": "2021.acl-long.238", "title": "Explanations for CommonsenseQA: New Dataset and Models", "track": "main", "status": "Long", "award": false, "abstract": "CommonsenseQA (CQA) (Talmor et al., 2019) dataset was recently released to advance the research on common-sense question answering (QA) task. Whereas the prior work has mostly focused on proposing QA models for this dataset, our aim is to retrieve as well as generate explanation for a given (question, correct answer choice, incorrect answer choices) tuple from this dataset. Our explanation definition is based on certain desiderata, and translates an explanation into a set of positive and negative common-sense properties (aka facts) which not only explain the correct answer choice but also refute the incorrect ones. We human-annotate a first-of-its-kind dataset (called ECQA) of positive and negative properties, as well as free-flow explanations, for 11K QA pairs taken from the CQA dataset. We propose a latent representation based property retrieval model as well as a GPT-2 based property generation model with a novel two step fine-tuning procedure. We also propose a free-flow explanation generation model. Extensive experiments show that our retrieval model beats BM25 baseline by a relative gain of 100% in F1 score, property generation model achieves a respectable F1 score of 36.4, and free-flow generation model achieves a similarity score of 61.9, where last two scores are based on a human correlated semantic similarity metric.", "author": "Shourya Aggarwal; Divyanshu Mandowara; Vishwajeet Agrawal; Dinesh Khandelwal; Parag Singla; Dinesh Garg", "authorids": "/s/shourya-aggarwal/; /d/divyanshu-mandowara/; /v/vishwajeet-agrawal/; /d/dinesh-khandelwal/; /p/parag-singla/; /d/dinesh-garg/", "bibtex": "@inproceedings{aggarwal-etal-2021-explanations,\n title = \"{E}xplanations for {C}ommonsense{QA}: {N}ew {D}ataset and {M}odels\",\n author = \"Aggarwal, Shourya and\n Mandowara, Divyanshu and\n Agrawal, Vishwajeet and\n Khandelwal, Dinesh and\n Singla, Parag and\n Garg, Dinesh\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.238/\",\n doi = \"10.18653/v1/2021.acl-long.238\",\n pages = \"3050--3065\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.238.pdf", "site": "https://aclanthology.org/2021.acl-long.238/", "pdf_size": 578969, "gs_citation": 156, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=12572404236806289388&as_sdt=5,33&sciodt=0,33&hl=en", "gs_version_total": 5, "aff": "IIT Delhi; IIT Delhi; IIT Delhi; IBM Research AI, India; IIT Delhi; IBM Research AI, India", "aff_domain": "gmail.com;gmail.com;gmail.com;in.ibm.com;cse.iitd.ac.in;in.ibm.com", "email": "gmail.com;gmail.com;gmail.com;in.ibm.com;cse.iitd.ac.in;in.ibm.com", "github": "", "project": "", "author_num": 6, "aff_unique_index": "0;0;0;1;0;1", "aff_unique_norm": "Indian Institute of Technology Delhi;IBM Research", "aff_unique_dep": ";AI", "aff_unique_url": "https://www.iitd.ac.in;https://www.ibm.com/research", "aff_unique_abbr": "IITD;IBM", "aff_campus_unique_index": "0;0;0;0", "aff_campus_unique": "Delhi;", "aff_country_unique_index": "0;0;0;0;0;0", "aff_country_unique": "India" }, { "id": "2021.acl-short.93", "title": "Explicitly Capturing Relations between Entity Mentions via Graph Neural Networks for Domain-specific Named Entity Recognition", "track": "main", "status": "Short", "award": false, "abstract": "Named entity recognition (NER) is well studied for the general domain, and recent systems have achieved human-level performance for identifying common entity types. However, the NER performance is still moderate for specialized domains that tend to feature complicated contexts and jargonistic entity types. To address these challenges, we propose explicitly connecting entity mentions based on both global coreference relations and local dependency relations for building better entity mention representations. In our experiments, we incorporate entity mention relations by Graph Neural Networks and show that our system noticeably improves the NER performance on two datasets from different domains. We further show that the proposed lightweight system can effectively elevate the NER performance to a higher level even when only a tiny amount of labeled data is available, which is desirable for domain-specific NER.", "author": "Pei Chen; Haibo Ding; Jun Araki; Ruihong Huang", "authorids": "/p/pei-chen/; /h/haibo-ding/; /j/jun-araki/; /r/ruihong-huang/", "bibtex": "@inproceedings{chen-etal-2021-explicitly,\n title = \"Explicitly Capturing Relations between Entity Mentions via Graph Neural Networks for Domain-specific Named Entity Recognition\",\n author = \"Chen, Pei and\n Ding, Haibo and\n Araki, Jun and\n Huang, Ruihong\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-short.93/\",\n doi = \"10.18653/v1/2021.acl-short.93\",\n pages = \"735--742\"\n}", "pdf": "https://aclanthology.org/2021.acl-short.93.pdf", "site": "https://aclanthology.org/2021.acl-short.93/", "pdf_size": 638755, "gs_citation": 23, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=7089290055586826769&as_sdt=80000005&sciodt=0,23&hl=en", "gs_version_total": 7, "aff": "Department of Computer Science and Engineering, Texas A&M University; Bosch Research North America; Bosch Research North America; Department of Computer Science and Engineering, Texas A&M University", "aff_domain": "tamu.edu;us.bosch.com;us.bosch.com;tamu.edu", "email": "tamu.edu;us.bosch.com;us.bosch.com;tamu.edu", "github": "https://github.com/brickee/EnRel-Gre", "project": "", "author_num": 4, "aff_unique_index": "0;1;1;0", "aff_unique_norm": "Texas A&M University;Bosch Research North America", "aff_unique_dep": "Department of Computer Science and Engineering;", "aff_unique_url": "https://www.tamu.edu;https://research.bosch.com/northamerica", "aff_unique_abbr": "TAMU;Bosch RNA", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0;0;0;0", "aff_country_unique": "United States" }, { "id": "2021.acl-long.374", "title": "Exploiting Document Structures and Cluster Consistencies for Event Coreference Resolution", "track": "main", "status": "Long", "award": false, "abstract": "We study the problem of event coreference resolution (ECR) that seeks to group coreferent event mentions into the same clusters. Deep learning methods have recently been applied for this task to deliver state-of-the-art performance. However, existing deep learning models for ECR are limited in that they cannot exploit important interactions between relevant objects for ECR, e.g., context words and entity mentions, to support the encoding of document-level context. In addition, consistency constraints between golden and predicted clusters of event mentions have not been considered to improve representation learning in prior deep learning models for ECR. This work addresses such limitations by introducing a novel deep learning model for ECR. At the core of our model are document structures to explicitly capture relevant objects for ECR. Our document structures introduce diverse knowledge sources (discourse, syntax, semantics) to compute edges/interactions between structure nodes for document-level representation learning. We also present novel regularization techniques based on consistencies of golden and predicted clusters for event mentions in documents. Extensive experiments show that our model achieve state-of-the-art performance on two benchmark datasets.", "author": "Hieu Minh Tran; Duy Phung; Thien Huu Nguyen", "authorids": "/h/hieu-minh-tran/; /d/duy-phung/; /t/thien-huu-nguyen/", "bibtex": "@inproceedings{minh-tran-etal-2021-exploiting,\n title = \"Exploiting Document Structures and Cluster Consistencies for Event Coreference Resolution\",\n author = \"Minh Tran, Hieu and\n Phung, Duy and\n Nguyen, Thien Huu\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.374/\",\n doi = \"10.18653/v1/2021.acl-long.374\",\n pages = \"4840--4850\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.374.pdf", "site": "https://aclanthology.org/2021.acl-long.374/", "pdf_size": 487331, "gs_citation": 29, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=10632508763839792476&as_sdt=5,33&sciodt=0,33&hl=en", "gs_version_total": 5, "aff": "VinAI Research, Vietnam; VinAI Research, Vietnam; Department of Computer and Information Science, University of Oregon, Eugene, OR 97403, USA", "aff_domain": "vinai.io;vinai.io;cs.uoregon.edu", "email": "vinai.io;vinai.io;cs.uoregon.edu", "github": "", "project": "", "author_num": 3, "aff_unique_index": "0;0;1", "aff_unique_norm": "VinAI Research;University of Oregon", "aff_unique_dep": ";Department of Computer and Information Science", "aff_unique_url": "https://www.vin.ai;https://www.uoregon.edu", "aff_unique_abbr": "VinAI;UO", "aff_campus_unique_index": "1", "aff_campus_unique": ";Eugene", "aff_country_unique_index": "0;0;1", "aff_country_unique": "Vietnam;United States" }, { "id": "2021.acl-long.105", "title": "Exploiting Language Relatedness for Low Web-Resource Language Model Adaptation: An Indic Languages Study", "track": "main", "status": "Long", "award": false, "abstract": "Recent research in multilingual language models (LM) has demonstrated their ability to effectively handle multiple languages in a single model. This holds promise for low web-resource languages (LRL) as multilingual models can enable transfer of supervision from high resource languages to LRLs. However, incorporating a new language in an LM still remains a challenge, particularly for languages with limited corpora and in unseen scripts. In this paper we argue that relatedness among languages in a language family may be exploited to overcome some of the corpora limitations of LRLs, and propose RelateLM. We focus on Indian languages, and exploit relatedness along two dimensions: (1) script (since many Indic scripts originated from the Brahmic script), and (2) sentence structure. RelateLM uses transliteration to convert the unseen script of limited LRL text into the script of a Related Prominent Language (RPL) (Hindi in our case). While exploiting similar sentence structures, RelateLM utilizes readily available bilingual dictionaries to pseudo translate RPL text into LRL corpora. Experiments on multiple real-world benchmark datasets provide validation to our hypothesis that using a related language as pivot, along with transliteration and pseudo translation based data augmentation, can be an effective way to adapt LMs for LRLs, rather than direct training or pivoting through English.", "author": "Yash Khemchandani; Sarvesh Mehtani; Vaidehi Patil; Abhijeet Awasthi; Partha Talukdar; Sunita Sarawagi", "authorids": "/y/yash-khemchandani/; /s/sarvesh-mehtani/; /v/vaidehi-patil/; /a/abhijeet-awasthi/; /p/partha-talukdar/; /s/sunita-sarawagi/", "bibtex": "@inproceedings{khemchandani-etal-2021-exploiting,\n title = \"Exploiting Language Relatedness for Low Web-Resource Language Model Adaptation: {A}n {I}ndic Languages Study\",\n author = \"Khemchandani, Yash and\n Mehtani, Sarvesh and\n Patil, Vaidehi and\n Awasthi, Abhijeet and\n Talukdar, Partha and\n Sarawagi, Sunita\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.105/\",\n doi = \"10.18653/v1/2021.acl-long.105\",\n pages = \"1312--1323\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.105.pdf", "site": "https://aclanthology.org/2021.acl-long.105/", "pdf_size": 772391, "gs_citation": 33, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=2474062955403310964&as_sdt=5,44&sciodt=0,44&hl=en", "gs_version_total": 10, "aff": "Indian Institute of Technology Bombay; Indian Institute of Technology Bombay; Indian Institute of Technology Bombay; Indian Institute of Technology Bombay; Google Research; Indian Institute of Technology Bombay", "aff_domain": "cse.iitb.ac.in;cse.iitb.ac.in;ee.iitb.ac.in;cse.iitb.ac.in;google.com;cse.iitb.ac.in", "email": "cse.iitb.ac.in;cse.iitb.ac.in;ee.iitb.ac.in;cse.iitb.ac.in;google.com;cse.iitb.ac.in", "github": "", "project": "", "author_num": 6, "aff_unique_index": "0;0;0;0;1;0", "aff_unique_norm": "Indian Institute of Technology Bombay;Google", "aff_unique_dep": ";Google Research", "aff_unique_url": "https://www.iitb.ac.in;https://research.google", "aff_unique_abbr": "IIT Bombay;Google Research", "aff_campus_unique_index": "0;0;0;0;1;0", "aff_campus_unique": "Bombay;Mountain View", "aff_country_unique_index": "0;0;0;0;1;0", "aff_country_unique": "India;United States" }, { "id": "2021.acl-short.56", "title": "Exploration and Exploitation: Two Ways to Improve Chinese Spelling Correction Models", "track": "main", "status": "Short", "award": false, "abstract": "A sequence-to-sequence learning with neural networks has empirically proven to be an effective framework for Chinese Spelling Correction (CSC), which takes a sentence with some spelling errors as input and outputs the corrected one. However, CSC models may fail to correct spelling errors covered by the confusion sets, and also will encounter unseen ones. We propose a method, which continually identifies the weak spots of a model to generate more valuable training instances, and apply a task-specific pre-training strategy to enhance the model. The generated adversarial examples are gradually added to the training set. Experimental results show that such an adversarial training method combined with the pre-training strategy can improve both the generalization and robustness of multiple CSC models across three different datasets, achieving state-of-the-art performance for CSC task.", "author": "Chong Li; Cenyuan Zhang; Xiaoqing Zheng; Xuanjing Huang", "authorids": "/c/chong-li/; /c/cenyuan-zhang/; /x/xiaoqing-zheng/; /x/xuan-jing-huang/", "bibtex": "@inproceedings{li-etal-2021-exploration,\n title = \"Exploration and Exploitation: Two Ways to Improve {C}hinese Spelling Correction Models\",\n author = \"Li, Chong and\n Zhang, Cenyuan and\n Zheng, Xiaoqing and\n Huang, Xuanjing\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-short.56/\",\n doi = \"10.18653/v1/2021.acl-short.56\",\n pages = \"441--446\"\n}", "pdf": "https://aclanthology.org/2021.acl-short.56.pdf", "site": "https://aclanthology.org/2021.acl-short.56/", "pdf_size": 335643, "gs_citation": 34, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=407176423726885798&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 5, "aff": "School of Computer Science, Fudan University, Shanghai, China+Shanghai Key Laboratory of Intelligent Information Processing; School of Computer Science, Fudan University, Shanghai, China+Shanghai Key Laboratory of Intelligent Information Processing; School of Computer Science, Fudan University, Shanghai, China+Shanghai Key Laboratory of Intelligent Information Processing; School of Computer Science, Fudan University, Shanghai, China+Shanghai Key Laboratory of Intelligent Information Processing", "aff_domain": "fudan.edu.cn;fudan.edu.cn;fudan.edu.cn;fudan.edu.cn", "email": "fudan.edu.cn;fudan.edu.cn;fudan.edu.cn;fudan.edu.cn", "github": "https://github.com/FDChongli/TwoWaysToImproveCSC", "project": "", "author_num": 4, "aff_unique_index": "0+1;0+1;0+1;0+1", "aff_unique_norm": "Fudan University;Shanghai Key Laboratory of Intelligent Information Processing", "aff_unique_dep": "School of Computer Science;Intelligent Information Processing", "aff_unique_url": "https://www.fudan.edu.cn;", "aff_unique_abbr": "Fudan;", "aff_campus_unique_index": "0;0;0;0", "aff_campus_unique": "Shanghai;", "aff_country_unique_index": "0+0;0+0;0+0;0+0", "aff_country_unique": "China" }, { "id": "2021.acl-long.306", "title": "Exploring Discourse Structures for Argument Impact Classification", "track": "main", "status": "Long", "award": false, "abstract": "Discourse relations among arguments reveal logical structures of a debate conversation. However, no prior work has explicitly studied how the sequence of discourse relations influence a claim\u2019s impact. This paper empirically shows that the discourse relations between two arguments along the context path are essential factors for identifying the persuasive power of an argument. We further propose DisCOC to inject and fuse the sentence-level structural discourse information with contextualized features derived from large-scale language models. Experimental results and extensive analysis show that the attention and gate mechanisms that explicitly model contexts and texts can indeed help the argument impact classification task defined by Durmus et al. (2019), and discourse structures among the context path of the claim to be classified can further boost the performance.", "author": "Xin Liu; Jiefu Ou; Yangqiu Song; Xin Jiang", "authorids": "/x/xin-liu/; /j/jiefu-ou/; /y/yangqiu-song/; /x/xin-jiang/", "bibtex": "@inproceedings{liu-etal-2021-exploring-discourse,\n title = \"Exploring Discourse Structures for Argument Impact Classification\",\n author = \"Liu, Xin and\n Ou, Jiefu and\n Song, Yangqiu and\n Jiang, Xin\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.306/\",\n doi = \"10.18653/v1/2021.acl-long.306\",\n pages = \"3958--3969\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.306.pdf", "site": "https://aclanthology.org/2021.acl-long.306/", "pdf_size": 609811, "gs_citation": 11, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=8664027551654131692&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 5, "aff": "Department of CSE, the Hong Kong University of Science and Technology; Department of CSE, the Hong Kong University of Science and Technology; Department of CSE, the Hong Kong University of Science and Technology; Huawei Noah\u2019s Ark Lab", "aff_domain": "cse.ust.hk;connect.ust.hk;cse.ust.hk;huawei.com", "email": "cse.ust.hk;connect.ust.hk;cse.ust.hk;huawei.com", "github": "", "project": "", "author_num": 4, "aff_unique_index": "0;0;0;1", "aff_unique_norm": "Hong Kong University of Science and Technology;Huawei", "aff_unique_dep": "Department of CSE;Noah\u2019s Ark Lab", "aff_unique_url": "https://www.ust.hk;https://www.huawei.com", "aff_unique_abbr": "HKUST;Huawei", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0;0;0;0", "aff_country_unique": "China" }, { "id": "2021.acl-long.433", "title": "Exploring Distantly-Labeled Rationales in Neural Network Models", "track": "main", "status": "Long", "award": false, "abstract": "Recent studies strive to incorporate various human rationales into neural networks to improve model performance, but few pay attention to the quality of the rationales. Most existing methods distribute their models\u2019 focus to distantly-labeled rationale words entirely and equally, while ignoring the potential important non-rationale words and not distinguishing the importance of different rationale words. In this paper, we propose two novel auxiliary loss functions to make better use of distantly-labeled rationales, which encourage models to maintain their focus on important words beyond labeled rationales (PINs) and alleviate redundant training on non-helpful rationales (NoIRs). Experiments on two representative classification tasks show that our proposed methods can push a classification model to effectively learn crucial clues from non-perfect rationales while maintaining the ability to spread its focus to other unlabeled important words, thus significantly outperform existing methods.", "author": "Quzhe Huang; Shengqi Zhu; Yansong Feng; Dongyan Zhao", "authorids": "/q/quzhe-huang/; /s/shengqi-zhu/; /y/yansong-feng/; /d/dongyan-zhao/", "bibtex": "@inproceedings{huang-etal-2021-exploring,\n title = \"Exploring Distantly-Labeled Rationales in Neural Network Models\",\n author = \"Huang, Quzhe and\n Zhu, Shengqi and\n Feng, Yansong and\n Zhao, Dongyan\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.433/\",\n doi = \"10.18653/v1/2021.acl-long.433\",\n pages = \"5571--5582\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.433.pdf", "site": "https://aclanthology.org/2021.acl-long.433/", "pdf_size": 377761, "gs_citation": 7, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=11958312314218237480&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 4, "aff": "Wangxuan Institute of Computer Technology, Peking University, China+The MOE Key Laboratory of Computational Linguistics, Peking University, China; Wangxuan Institute of Computer Technology, Peking University, China+The MOE Key Laboratory of Computational Linguistics, Peking University, China; Wangxuan Institute of Computer Technology, Peking University, China+The MOE Key Laboratory of Computational Linguistics, Peking University, China; Wangxuan Institute of Computer Technology, Peking University, China+The MOE Key Laboratory of Computational Linguistics, Peking University, China", "aff_domain": "pku.edu.cn;pku.edu.cn;pku.edu.cn;pku.edu.cn", "email": "pku.edu.cn;pku.edu.cn;pku.edu.cn;pku.edu.cn", "github": "", "project": "", "author_num": 4, "aff_unique_index": "0+0;0+0;0+0;0+0", "aff_unique_norm": "Peking University", "aff_unique_dep": "Wangxuan Institute of Computer Technology", "aff_unique_url": "http://www.pku.edu.cn", "aff_unique_abbr": "PKU", "aff_campus_unique_index": ";;;", "aff_campus_unique": "", "aff_country_unique_index": "0+0;0+0;0+0;0+0", "aff_country_unique": "China" }, { "id": "2021.acl-long.394", "title": "Exploring Dynamic Selection of Branch Expansion Orders for Code Generation", "track": "main", "status": "Long", "award": false, "abstract": "Due to the great potential in facilitating software development, code generation has attracted increasing attention recently. Generally, dominant models are Seq2Tree models, which convert the input natural language description into a sequence of tree-construction actions corresponding to the pre-order traversal of an Abstract Syntax Tree (AST). However, such a traversal order may not be suitable for handling all multi-branch nodes. In this paper, we propose to equip the Seq2Tree model with a context-based Branch Selector, which is able to dynamically determine optimal expansion orders of branches for multi-branch nodes. Particularly, since the selection of expansion orders is a non-differentiable multi-step operation, we optimize the selector through reinforcement learning, and formulate the reward function as the difference of model losses obtained through different expansion orders. Experimental results and in-depth analysis on several commonly-used datasets demonstrate the effectiveness and generality of our approach. We have released our code at https://github.com/DeepLearnXMU/CG-RL.", "author": "Hui Jiang; Chulun Zhou; Fandong Meng; Biao Zhang; Jie Zhou; Degen Huang; Qingqiang Wu; Jinsong Su", "authorids": "/h/hui-jiang/; /c/chulun-zhou/; /f/fandong-meng/; /b/biao-zhang/; /j/jie-zhou/; /d/degen-huang/; /q/qingqiang-wu/; /j/jinsong-su/", "bibtex": "@inproceedings{jiang-etal-2021-exploring,\n title = \"Exploring Dynamic Selection of Branch Expansion Orders for Code Generation\",\n author = \"Jiang, Hui and\n Zhou, Chulun and\n Meng, Fandong and\n Zhang, Biao and\n Zhou, Jie and\n Huang, Degen and\n Wu, Qingqiang and\n Su, Jinsong\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.394/\",\n doi = \"10.18653/v1/2021.acl-long.394\",\n pages = \"5076--5085\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.394.pdf", "site": "https://aclanthology.org/2021.acl-long.394/", "pdf_size": 1504191, "gs_citation": 28, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=7139226685171597653&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 4, "aff": "1School of Informatics, Xiamen University + 2Institute of Arti\ufb01cial Intelligence, Xiamen University; 1School of Informatics, Xiamen University + 2Institute of Arti\ufb01cial Intelligence, Xiamen University; 3Pattern Recognition Center, WeChat AI, Tencent Inc, China; 4School of Informatics, University of Edinburgh; 3Pattern Recognition Center, WeChat AI, Tencent Inc, China; 5Dalian University of Technology; 1School of Informatics, Xiamen University + 2Institute of Arti\ufb01cial Intelligence, Xiamen University; 1School of Informatics, Xiamen University + 2Institute of Arti\ufb01cial Intelligence, Xiamen University", "aff_domain": "stu.xmu.edu.cn;stu.xmu.edu.cn;tencent.com;ed.ac.uk;tencent.com;dlut.edu.cn;xmu.edu.cn;xmu.edu.cn", "email": "stu.xmu.edu.cn;stu.xmu.edu.cn;tencent.com;ed.ac.uk;tencent.com;dlut.edu.cn;xmu.edu.cn;xmu.edu.cn", "github": "https://github.com/DeepLearnXMU/CG-RL", "project": "", "author_num": 8, "aff_unique_index": "0+0;0+0;1;2;1;3;0+0;0+0", "aff_unique_norm": "Xiamen University;Tencent Inc;University of Edinburgh;Dalian University of Technology", "aff_unique_dep": "School of Informatics;Pattern Recognition Center;School of Informatics;", "aff_unique_url": "https://www.xmu.edu.cn;https://www.tencent.com;https://www.ed.ac.uk;http://www.dlut.edu.cn/", "aff_unique_abbr": "XMU;Tencent;Edinburgh;DUT", "aff_campus_unique_index": ";;1;;", "aff_campus_unique": ";Edinburgh", "aff_country_unique_index": "0+0;0+0;0;1;0;0;0+0;0+0", "aff_country_unique": "China;United Kingdom" }, { "id": "2021.acl-short.51", "title": "Exploring Listwise Evidence Reasoning with T5 for Fact Verification", "track": "main", "status": "Short", "award": false, "abstract": "This work explores a framework for fact verification that leverages pretrained sequence-to-sequence transformer models for sentence selection and label prediction, two key sub-tasks in fact verification. Most notably, improving on previous pointwise aggregation approaches for label prediction, we take advantage of T5 using a listwise approach coupled with data augmentation. With this enhancement, we observe that our label prediction stage is more robust to noise and capable of verifying complex claims by jointly reasoning over multiple pieces of evidence. Experimental results on the FEVER task show that our system attains a FEVER score of 75.87% on the blind test set. This puts our approach atop the competitive FEVER leaderboard at the time of our work, scoring higher than the second place submission by almost two points in label accuracy and over one point in FEVER score.", "author": "Kelvin Jiang; Ronak Pradeep; Jimmy Lin", "authorids": "/k/kelvin-jiang/; /r/ronak-pradeep/; /j/jimmy-lin/", "bibtex": "@inproceedings{jiang-etal-2021-exploring-listwise,\n title = \"Exploring Listwise Evidence Reasoning with T5 for Fact Verification\",\n author = \"Jiang, Kelvin and\n Pradeep, Ronak and\n Lin, Jimmy\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-short.51/\",\n doi = \"10.18653/v1/2021.acl-short.51\",\n pages = \"402--410\"\n}", "pdf": "https://aclanthology.org/2021.acl-short.51.pdf", "site": "https://aclanthology.org/2021.acl-short.51/", "pdf_size": 302064, "gs_citation": 66, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=1985714077029788913&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 3, "aff": "David R. Cheriton School of Computer Science, University of Waterloo; David R. Cheriton School of Computer Science, University of Waterloo; David R. Cheriton School of Computer Science, University of Waterloo", "aff_domain": "uwaterloo.ca;uwaterloo.ca;uwaterloo.ca", "email": "uwaterloo.ca;uwaterloo.ca;uwaterloo.ca", "github": "", "project": "", "author_num": 3, "aff_unique_index": "0;0;0", "aff_unique_norm": "University of Waterloo", "aff_unique_dep": "David R. Cheriton School of Computer Science", "aff_unique_url": "https://uwaterloo.ca", "aff_unique_abbr": "UWaterloo", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0;0;0", "aff_country_unique": "Canada" }, { "id": "2021.acl-long.26", "title": "Exploring the Efficacy of Automatically Generated Counterfactuals for Sentiment Analysis", "track": "main", "status": "Long", "award": false, "abstract": "While state-of-the-art NLP models have been achieving the excellent performance of a wide range of tasks in recent years, important questions are being raised about their robustness and their underlying sensitivity to systematic biases that may exist in their training and test data. Such issues come to be manifest in performance problems when faced with out-of-distribution data in the field. One recent solution has been to use counterfactually augmented datasets in order to reduce any reliance on spurious patterns that may exist in the original data. Producing high-quality augmented data can be costly and time-consuming as it usually needs to involve human feedback and crowdsourcing efforts. In this work, we propose an alternative by describing and evaluating an approach to automatically generating counterfactual data for the purpose of data augmentation and explanation. A comprehensive evaluation on several different datasets and using a variety of state-of-the-art benchmarks demonstrate how our approach can achieve significant improvements in model performance when compared to models training on the original data and even when compared to models trained with the benefit of human-generated augmented data.", "author": "Linyi Yang; Jiazheng Li; Padraig Cunningham; Yue Zhang; Barry Smyth; Ruihai Dong", "authorids": "/l/linyi-yang/; /j/jiazheng-li/; /p/padraig-cunningham/; /y/yue-zhang/; /b/barry-smyth/; /r/ruihai-dong/", "bibtex": "@inproceedings{yang-etal-2021-exploring,\n title = \"Exploring the Efficacy of Automatically Generated Counterfactuals for Sentiment Analysis\",\n author = \"Yang, Linyi and\n Li, Jiazheng and\n Cunningham, Padraig and\n Zhang, Yue and\n Smyth, Barry and\n Dong, Ruihai\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.26/\",\n doi = \"10.18653/v1/2021.acl-long.26\",\n pages = \"306--316\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.26.pdf", "site": "https://aclanthology.org/2021.acl-long.26/", "pdf_size": 1335289, "gs_citation": 66, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=4392623211391501825&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 7, "aff": "The Insight Centre for Data Analytics, University College Dublin + School of Computer Science, University College Dublin + School of Engineering, Westlake University + Institute of Advanced Technology, Westlake Institute for Advanced Study; School of Computer Science, University College Dublin; School of Computer Science, University College Dublin; School of Engineering, Westlake University + Institute of Advanced Technology, Westlake Institute for Advanced Study; The Insight Centre for Data Analytics, University College Dublin + School of Computer Science, University College Dublin; The Insight Centre for Data Analytics, University College Dublin + School of Computer Science, University College Dublin", "aff_domain": "insight-centre.org;ucdconnect.ie;ucd.ie;westlake.edu.cn;insight-centre.org;insight-centre.org", "email": "insight-centre.org;ucdconnect.ie;ucd.ie;westlake.edu.cn;insight-centre.org;insight-centre.org", "github": "", "project": "", "author_num": 6, "aff_unique_index": "0+0+1+2;0;0;1+2;0+0;0+0", "aff_unique_norm": "University College Dublin;Westlake University;Westlake Institute for Advanced Study", "aff_unique_dep": "The Insight Centre for Data Analytics;School of Engineering;Institute of Advanced Technology", "aff_unique_url": "https://www.ucd.ie;https://www.westlake.edu.cn;http://www.wias.org.cn/", "aff_unique_abbr": "UCD;;WIAS", "aff_campus_unique_index": "1;1;1;;1;1", "aff_campus_unique": ";Dublin", "aff_country_unique_index": "0+0+1+1;0;0;1+1;0+0;0+0", "aff_country_unique": "Ireland;China" }, { "id": "2021.acl-long.281", "title": "Exploring the Representation of Word Meanings in Context: A Case Study on Homonymy and Synonymy", "track": "main", "status": "Long", "award": false, "abstract": "This paper presents a multilingual study of word meaning representations in context. We assess the ability of both static and contextualized models to adequately represent different lexical-semantic relations, such as homonymy and synonymy. To do so, we created a new multilingual dataset that allows us to perform a controlled evaluation of several factors such as the impact of the surrounding context or the overlap between words, conveying the same or different senses. A systematic assessment on four scenarios shows that the best monolingual models based on Transformers can adequately disambiguate homonyms in context. However, as they rely heavily on context, these models fail at representing words with different senses when occurring in similar sentences. Experiments are performed in Galician, Portuguese, English, and Spanish, and both the dataset (with more than 3,000 evaluation items) and new models are freely released with this study.", "author": "Marcos Garcia", "authorids": "/m/marcos-garcia/", "bibtex": "@inproceedings{garcia-2021-exploring,\n title = \"Exploring the Representation of Word Meanings in Context: {A} Case Study on Homonymy and Synonymy\",\n author = \"Garcia, Marcos\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.281/\",\n doi = \"10.18653/v1/2021.acl-long.281\",\n pages = \"3625--3640\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.281.pdf", "site": "https://aclanthology.org/2021.acl-long.281/", "pdf_size": 1680654, "gs_citation": 29, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=12247740929399972504&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 6, "aff": "CiTIUS \u2013 Research Center in Intelligent Technologies, Universidade de Santiago de Compostela, Galiza", "aff_domain": "usc.gal", "email": "usc.gal", "github": "", "project": "", "author_num": 1, "aff_unique_index": "0", "aff_unique_norm": "Universidade de Santiago de Compostela", "aff_unique_dep": "Research Center in Intelligent Technologies", "aff_unique_url": "https://www.usc.es", "aff_unique_abbr": "USC", "aff_country_unique_index": "0", "aff_country_unique": "Spain" }, { "id": "2021.acl-short.114", "title": "Exposing the limits of Zero-shot Cross-lingual Hate Speech Detection", "track": "main", "status": "Short", "award": false, "abstract": "Reducing and counter-acting hate speech on Social Media is a significant concern. Most of the proposed automatic methods are conducted exclusively on English and very few consistently labeled, non-English resources have been proposed. Learning to detect hate speech on English and transferring to unseen languages seems an immediate solution. This work is the first to shed light on the limits of this zero-shot, cross-lingual transfer learning framework for hate speech detection. We use benchmark data sets in English, Italian, and Spanish to detect hate speech towards immigrants and women. Investigating post-hoc explanations of the model, we discover that non-hateful, language-specific taboo interjections are misinterpreted as signals of hate speech. Our findings demonstrate that zero-shot, cross-lingual models cannot be used as they are, but need to be carefully designed.", "author": "Debora Nozza", "authorids": "/d/debora-nozza/", "bibtex": "@inproceedings{nozza-2021-exposing,\n title = \"Exposing the limits of Zero-shot Cross-lingual Hate Speech Detection\",\n author = \"Nozza, Debora\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-short.114/\",\n doi = \"10.18653/v1/2021.acl-short.114\",\n pages = \"907--914\"\n}", "pdf": "https://aclanthology.org/2021.acl-short.114.pdf", "site": "https://aclanthology.org/2021.acl-short.114/", "pdf_size": 368526, "gs_citation": 97, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=8990534500441837900&as_sdt=5,44&sciodt=0,44&hl=en", "gs_version_total": 5, "aff": "Bocconi University", "aff_domain": "unibocconi.it", "email": "unibocconi.it", "github": "", "project": "", "author_num": 1, "aff_unique_index": "0", "aff_unique_norm": "Bocconi University", "aff_unique_dep": "", "aff_unique_url": "https://www.bocconi.edu", "aff_unique_abbr": "Bocconi", "aff_country_unique_index": "0", "aff_country_unique": "Italy" }, { "id": "2021.acl-long.213", "title": "Factoring Statutory Reasoning as Language Understanding Challenges", "track": "main", "status": "Long", "award": false, "abstract": "Statutory reasoning is the task of determining whether a legal statute, stated in natural language, applies to the text description of a case. Prior work introduced a resource that approached statutory reasoning as a monolithic textual entailment problem, with neural baselines performing nearly at-chance. To address this challenge, we decompose statutory reasoning into four types of language-understanding challenge problems, through the introduction of concepts and structure found in Prolog programs. Augmenting an existing benchmark, we provide annotations for the four tasks, and baselines for three of them. Models for statutory reasoning are shown to benefit from the additional structure, improving on prior baselines. Further, the decomposition into subtasks facilitates finer-grained model diagnostics and clearer incremental progress.", "author": "Nils Holzenberger; Benjamin Van Durme", "authorids": "/n/nils-holzenberger/; /b/benjamin-van-durme/", "bibtex": "@inproceedings{holzenberger-van-durme-2021-factoring,\n title = \"Factoring Statutory Reasoning as Language Understanding Challenges\",\n author = \"Holzenberger, Nils and\n Van Durme, Benjamin\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.213/\",\n doi = \"10.18653/v1/2021.acl-long.213\",\n pages = \"2742--2758\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.213.pdf", "site": "https://aclanthology.org/2021.acl-long.213/", "pdf_size": 523163, "gs_citation": 18, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=10471395796526176488&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 5, "aff": "Center for Language and Speech Processing, Johns Hopkins University, Baltimore, Maryland, USA; Center for Language and Speech Processing, Johns Hopkins University, Baltimore, Maryland, USA", "aff_domain": "jhu.edu;cs.jhu.edu", "email": "jhu.edu;cs.jhu.edu", "github": "", "project": "", "author_num": 2, "aff_unique_index": "0;0", "aff_unique_norm": "Johns Hopkins University", "aff_unique_dep": "Center for Language and Speech Processing", "aff_unique_url": "https://www.jhu.edu", "aff_unique_abbr": "JHU", "aff_campus_unique_index": "0;0", "aff_campus_unique": "Baltimore", "aff_country_unique_index": "0;0", "aff_country_unique": "United States" }, { "id": "2021.acl-long.112", "title": "Factorising Meaning and Form for Intent-Preserving Paraphrasing", "track": "main", "status": "Long", "award": false, "abstract": "We propose a method for generating paraphrases of English questions that retain the original intent but use a different surface form. Our model combines a careful choice of training objective with a principled information bottleneck, to induce a latent encoding space that disentangles meaning and form. We train an encoder-decoder model to reconstruct a question from a paraphrase with the same meaning and an exemplar with the same surface form, leading to separated encoding spaces. We use a Vector-Quantized Variational Autoencoder to represent the surface form as a set of discrete latent variables, allowing us to use a classifier to select a different surface form at test time. Crucially, our method does not require access to an external source of target exemplars. Extensive experiments and a human evaluation show that we are able to generate paraphrases with a better tradeoff between semantic preservation and syntactic novelty compared to previous methods.", "author": "Tom Hosking; Mirella Lapata", "authorids": "/t/tom-hosking/; /m/mirella-lapata/", "bibtex": "@inproceedings{hosking-lapata-2021-factorising,\n title = \"Factorising Meaning and Form for Intent-Preserving Paraphrasing\",\n author = \"Hosking, Tom and\n Lapata, Mirella\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.112/\",\n doi = \"10.18653/v1/2021.acl-long.112\",\n pages = \"1405--1418\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.112.pdf", "site": "https://aclanthology.org/2021.acl-long.112/", "pdf_size": 751011, "gs_citation": 46, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=4550394829138183115&as_sdt=5,33&sciodt=0,33&hl=en", "gs_version_total": 7, "aff": "Institute for Language, Cognition and Computation, School of Informatics, University of Edinburgh; Institute for Language, Cognition and Computation, School of Informatics, University of Edinburgh", "aff_domain": "ed.ac.uk;inf.ed.ac.uk", "email": "ed.ac.uk;inf.ed.ac.uk", "github": "", "project": "", "author_num": 2, "aff_unique_index": "0;0", "aff_unique_norm": "University of Edinburgh", "aff_unique_dep": "School of Informatics", "aff_unique_url": "https://www.ed.ac.uk", "aff_unique_abbr": "Edinburgh", "aff_campus_unique_index": "0;0", "aff_campus_unique": "Edinburgh", "aff_country_unique_index": "0;0", "aff_country_unique": "United Kingdom" }, { "id": "2021.acl-long.122", "title": "Factuality Assessment as Modal Dependency Parsing", "track": "main", "status": "Long", "award": false, "abstract": "As the sources of information that we consume everyday rapidly diversify, it is becoming increasingly important to develop NLP tools that help to evaluate the credibility of the information we receive. A critical step towards this goal is to determine the factuality of events in text. In this paper, we frame factuality assessment as a modal dependency parsing task that identifies the events and their sources, formally known as conceivers, and then determine the level of certainty that the sources are asserting with respect to the events. We crowdsource the first large-scale data set annotated with modal dependency structures that consists of 353 Covid-19 related news articles, 24,016 events, and 2,938 conceivers. We also develop the first modal dependency parser that jointly extracts events, conceivers and constructs the modal dependency structure of a text. We evaluate the joint model against a pipeline model and demonstrate the advantage of the joint model in conceiver extraction and modal dependency structure construction when events and conceivers are automatically extracted. We believe the dataset and the models will be a valuable resource for a whole host of NLP applications such as fact checking and rumor detection.", "author": "Jiarui Yao; Haoling Qiu; Jin Zhao; Bonan Min; Nianwen Xue", "authorids": "/j/jiarui-yao/; /h/haoling-qiu/; /j/jin-zhao/; /b/bonan-min/; /n/nianwen-xue/", "bibtex": "@inproceedings{yao-etal-2021-factuality,\n title = \"Factuality Assessment as Modal Dependency Parsing\",\n author = \"Yao, Jiarui and\n Qiu, Haoling and\n Zhao, Jin and\n Min, Bonan and\n Xue, Nianwen\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.122/\",\n doi = \"10.18653/v1/2021.acl-long.122\",\n pages = \"1540--1550\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.122.pdf", "site": "https://aclanthology.org/2021.acl-long.122/", "pdf_size": 420597, "gs_citation": 14, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=8415953009891779986&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 5, "aff": "Brandeis University; Raytheon BBN Technologies; Brandeis University; Raytheon BBN Technologies; Brandeis University", "aff_domain": "brandeis.edu;raytheon.com;brandeis.edu;raytheon.com;brandeis.edu", "email": "brandeis.edu;raytheon.com;brandeis.edu;raytheon.com;brandeis.edu", "github": "https://github.com/Jryao/modal_dependency", "project": "", "author_num": 5, "aff_unique_index": "0;1;0;1;0", "aff_unique_norm": "Brandeis University;Raytheon BBN Technologies", "aff_unique_dep": ";", "aff_unique_url": "https://www.brandeis.edu;https://www.raytheonbbn.com", "aff_unique_abbr": "Brandeis;Raytheon BBN", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0;0;0;0;0", "aff_country_unique": "United States" }, { "id": "2021.acl-long.246", "title": "Fast and Accurate Neural Machine Translation with Translation Memory", "track": "main", "status": "Long", "award": false, "abstract": "It is generally believed that a translation memory (TM) should be beneficial for machine translation tasks. Unfortunately, existing wisdom demonstrates the superiority of TM-based neural machine translation (NMT) only on the TM-specialized translation tasks rather than general tasks, with a non-negligible computational overhead. In this paper, we propose a fast and accurate approach to TM-based NMT within the Transformer framework: the model architecture is simple and employs a single bilingual sentence as its TM, leading to efficient training and inference; and its parameters are effectively optimized through a novel training criterion. Extensive experiments on six TM-specialized tasks show that the proposed approach substantially surpasses several strong baselines that use multiple TMs, in terms of BLEU and running time. In particular, the proposed approach also advances the strong baselines on two general tasks (WMT news Zh->En and En->De).", "author": "Qiuxiang He; Guoping Huang; Qu Cui; Li Li; Lemao Liu", "authorids": "/q/qiuxiang-he/; /g/guoping-huang/; /q/qu-cui/; /l/li-li/; /l/lemao-liu/", "bibtex": "@inproceedings{he-etal-2021-fast,\n title = \"Fast and Accurate Neural Machine Translation with Translation Memory\",\n author = \"He, Qiuxiang and\n Huang, Guoping and\n Cui, Qu and\n Li, Li and\n Liu, Lemao\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.246/\",\n doi = \"10.18653/v1/2021.acl-long.246\",\n pages = \"3170--3180\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.246.pdf", "site": "https://aclanthology.org/2021.acl-long.246/", "pdf_size": 547951, "gs_citation": 63, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=4505404670033930831&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 3, "aff": "Southwest University; Tencent AI Lab; Nanjing University; Southwest University; Tencent AI Lab", "aff_domain": "email.swu.edu.cn;tencent.com;smail.nju.edu.cn;swu.edu.cn;tencent.com", "email": "email.swu.edu.cn;tencent.com;smail.nju.edu.cn;swu.edu.cn;tencent.com", "github": "", "project": "", "author_num": 5, "aff_unique_index": "0;1;2;0;1", "aff_unique_norm": "Southwest University;Tencent;Nanjing University", "aff_unique_dep": ";Tencent AI Lab;", "aff_unique_url": "https://www.swu.edu.cn;https://ai.tencent.com;https://www.nju.edu.cn", "aff_unique_abbr": "SWU;Tencent AI Lab;Nanjing U", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0;0;0;0;0", "aff_country_unique": "China" }, { "id": "2021.acl-long.248", "title": "Few-NERD: A Few-shot Named Entity Recognition Dataset", "track": "main", "status": "Long", "award": false, "abstract": "Recently, considerable literature has grown up around the theme of few-shot named entity recognition (NER), but little published benchmark data specifically focused on the practical and challenging task. Current approaches collect existing supervised NER datasets and re-organize them to the few-shot setting for empirical study. These strategies conventionally aim to recognize coarse-grained entity types with few examples, while in practice, most unseen entity types are fine-grained. In this paper, we present Few-NERD, a large-scale human-annotated few-shot NER dataset with a hierarchy of 8 coarse-grained and 66 fine-grained entity types. Few-NERD consists of 188,238 sentences from Wikipedia, 4,601,160 words are included and each is annotated as context or a part of the two-level entity type. To the best of our knowledge, this is the first few-shot NER dataset and the largest human-crafted NER dataset. We construct benchmark tasks with different emphases to comprehensively assess the generalization capability of models. Extensive empirical results and analysis show that Few-NERD is challenging and the problem requires further research. The Few-NERD dataset and the baselines will be publicly available to facilitate the research on this problem.", "author": "Ning Ding; Guangwei Xu; Yulin Chen; Xiaobin Wang; Xu Han; Pengjun Xie; Haitao Zheng; Zhiyuan Liu", "authorids": "/n/ning-ding/; /g/guangwei-xu/; /y/yulin-chen/; /x/xiaobin-wang/; /x/xu-han/; /p/pengjun-xie/; /h/haitao-zheng/; /z/zhiyuan-liu/", "bibtex": "@inproceedings{ding-etal-2021-nerd,\n title = \"Few-{NERD}: A Few-shot Named Entity Recognition Dataset\",\n author = \"Ding, Ning and\n Xu, Guangwei and\n Chen, Yulin and\n Wang, Xiaobin and\n Han, Xu and\n Xie, Pengjun and\n Zheng, Haitao and\n Liu, Zhiyuan\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.248/\",\n doi = \"10.18653/v1/2021.acl-long.248\",\n pages = \"3198--3213\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.248.pdf", "site": "https://aclanthology.org/2021.acl-long.248/", "pdf_size": 855134, "gs_citation": 262, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=7932094027505823017&as_sdt=5,33&sciodt=0,33&hl=en", "gs_version_total": 6, "aff": "Department of Computer Science and Technology, Tsinghua University+Shenzhen International Graduate School, Tsinghua University; Alibaba Group; Shenzhen International Graduate School, Tsinghua University+Department of Computer Science and Technology, Tsinghua University; Alibaba Group; Department of Computer Science and Technology, Tsinghua University; Alibaba Group; Shenzhen International Graduate School, Tsinghua University; Department of Computer Science and Technology, Tsinghua University", "aff_domain": "mails.tsinghua.edu.cn;alibaba-inc.com;mails.tsinghua.edu.cn;alibaba-inc.com;mails.tsinghua.edu.cn;alibaba-inc.com;sz.tsinghua.edu.cn;tsinghua.edu.cn", "email": "mails.tsinghua.edu.cn;alibaba-inc.com;mails.tsinghua.edu.cn;alibaba-inc.com;mails.tsinghua.edu.cn;alibaba-inc.com;sz.tsinghua.edu.cn;tsinghua.edu.cn", "github": "https://github.com/thunlp/Few-NERD", "project": "https://ningding97.github.io/fewnerd/", "author_num": 8, "aff_unique_index": "0+0;1;0+0;1;0;1;0;0", "aff_unique_norm": "Tsinghua University;Alibaba Group", "aff_unique_dep": "Department of Computer Science and Technology;", "aff_unique_url": "https://www.tsinghua.edu.cn;https://www.alibaba.com", "aff_unique_abbr": "THU;Alibaba", "aff_campus_unique_index": "1;1;1", "aff_campus_unique": ";Shenzhen", "aff_country_unique_index": "0+0;0;0+0;0;0;0;0;0", "aff_country_unique": "China" }, { "id": "2021.acl-long.239", "title": "Few-Shot Question Answering by Pretraining Span Selection", "track": "main", "status": "Long", "award": false, "abstract": "In several question answering benchmarks, pretrained models have reached human parity through fine-tuning on an order of 100,000 annotated questions and answers. We explore the more realistic few-shot setting, where only a few hundred training examples are available, and observe that standard models perform poorly, highlighting the discrepancy between current pretraining objectives and question answering. We propose a new pretraining scheme tailored for question answering: recurring span selection. Given a passage with multiple sets of recurring spans, we mask in each set all recurring spans but one, and ask the model to select the correct span in the passage for each masked span. Masked spans are replaced with a special token, viewed as a question representation, that is later used during fine-tuning to select the answer span. The resulting model obtains surprisingly good results on multiple benchmarks (e.g., 72.7 F1 on SQuAD with only 128 training examples), while maintaining competitive performance in the high-resource setting.", "author": "Ori Ram; Yuval Kirstain; Jonathan Berant; Amir Globerson; Omer Levy", "authorids": "/o/ori-ram/; /y/yuval-kirstain/; /j/jonathan-berant/; /a/amir-globerson/; /o/omer-levy/", "bibtex": "@inproceedings{ram-etal-2021-shot,\n title = \"Few-Shot Question Answering by Pretraining Span Selection\",\n author = \"Ram, Ori and\n Kirstain, Yuval and\n Berant, Jonathan and\n Globerson, Amir and\n Levy, Omer\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.239/\",\n doi = \"10.18653/v1/2021.acl-long.239\",\n pages = \"3066--3079\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.239.pdf", "site": "https://aclanthology.org/2021.acl-long.239/", "pdf_size": 1406581, "gs_citation": 109, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=5433243132865691309&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 5, "aff": "Blavatnik School of Computer Science, Tel Aviv University1; Blavatnik School of Computer Science, Tel Aviv University1; Blavatnik School of Computer Science, Tel Aviv University1 + Allen Institute for AI2; Blavatnik School of Computer Science, Tel Aviv University1; Blavatnik School of Computer Science, Tel Aviv University1", "aff_domain": "cs.tau.ac.il;cs.tau.ac.il;cs.tau.ac.il;cs.tau.ac.il;cs.tau.ac.il", "email": "cs.tau.ac.il;cs.tau.ac.il;cs.tau.ac.il;cs.tau.ac.il;cs.tau.ac.il", "github": "https://github.com/oriram/splinter", "project": "", "author_num": 5, "aff_unique_index": "0;0;0+1;0;0", "aff_unique_norm": "Tel Aviv University;Allen Institute for AI", "aff_unique_dep": "Blavatnik School of Computer Science;", "aff_unique_url": "https://www.tau.ac.il;https://allenai.org", "aff_unique_abbr": "TAU;AI2", "aff_campus_unique_index": "0;0;0;0;0", "aff_campus_unique": "Tel Aviv;", "aff_country_unique_index": "0;0;0+1;0;0", "aff_country_unique": "Israel;United States" }, { "id": "2021.acl-long.390", "title": "Few-Shot Text Ranking with Meta Adapted Synthetic Weak Supervision", "track": "main", "status": "Long", "award": false, "abstract": "The effectiveness of Neural Information Retrieval (Neu-IR) often depends on a large scale of in-domain relevance training signals, which are not always available in real-world ranking scenarios. To democratize the benefits of Neu-IR, this paper presents MetaAdaptRank, a domain adaptive learning method that generalizes Neu-IR models from label-rich source domains to few-shot target domains. Drawing on source-domain massive relevance supervision, MetaAdaptRank contrastively synthesizes a large number of weak supervision signals for target domains and meta-learns to reweight these synthetic \u201cweak\u201d data based on their benefits to the target-domain ranking accuracy of Neu-IR models. Experiments on three TREC benchmarks in the web, news, and biomedical domains show that MetaAdaptRank significantly improves the few-shot ranking accuracy of Neu-IR models. Further analyses indicate that MetaAdaptRank thrives from both its contrastive weak data synthesis and meta-reweighted data selection. The code and data of this paper can be obtained from https://github.com/thunlp/MetaAdaptRank.", "author": "Si Sun; Yingzhuo Qian; Zhenghao Liu; Chenyan Xiong; Kaitao Zhang; Jie Bao; Zhiyuan Liu; Paul Bennett", "authorids": "/s/si-sun/; /y/yingzhuo-qian/; /z/zhenghao-liu/; /c/chenyan-xiong/; /k/kaitao-zhang/; /j/jie-bao/; /z/zhiyuan-liu/; /p/paul-bennett/", "bibtex": "@inproceedings{sun-etal-2021-shot,\n title = \"Few-Shot Text Ranking with Meta Adapted Synthetic Weak Supervision\",\n author = \"Sun, Si and\n Qian, Yingzhuo and\n Liu, Zhenghao and\n Xiong, Chenyan and\n Zhang, Kaitao and\n Bao, Jie and\n Liu, Zhiyuan and\n Bennett, Paul\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.390/\",\n doi = \"10.18653/v1/2021.acl-long.390\",\n pages = \"5030--5043\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.390.pdf", "site": "https://aclanthology.org/2021.acl-long.390/", "pdf_size": 604708, "gs_citation": 21, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=12923136677672395855&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 4, "aff": "Department of Electronic Engineering, Tsinghua University, Beijing, China; Department of Computer Science and Technology, Tsinghua University, Beijing, China + Institute for Artificial Intelligence, Tsinghua University, Beijing, China + Beijing National Research Center for Information Science and Technology, China; Department of Computer Science and Technology, Tsinghua University, Beijing, China + Institute for Artificial Intelligence, Tsinghua University, Beijing, China + Beijing National Research Center for Information Science and Technology, China; Microsoft Research, Redmond, USA; Department of Computer Science and Technology, Tsinghua University, Beijing, China + Institute for Artificial Intelligence, Tsinghua University, Beijing, China + Beijing National Research Center for Information Science and Technology, China; Department of Electronic Engineering, Tsinghua University, Beijing, China; Department of Computer Science and Technology, Tsinghua University, Beijing, China + Institute for Artificial Intelligence, Tsinghua University, Beijing, China + Beijing National Research Center for Information Science and Technology, China; Microsoft Research, Redmond, USA", "aff_domain": "mails.tsinghua.edu.cn;mails.tsinghua.edu.cn;mails.tsinghua.edu.cn;microsoft.com;mails.tsinghua.edu.cn;tsinghua.edu.cn;tsinghua.edu.cn;microsoft.com", "email": "mails.tsinghua.edu.cn;mails.tsinghua.edu.cn;mails.tsinghua.edu.cn;microsoft.com;mails.tsinghua.edu.cn;tsinghua.edu.cn;tsinghua.edu.cn;microsoft.com", "github": "https://github.com/thunlp/MetaAdaptRank", "project": "", "author_num": 8, "aff_unique_index": "0;0+0+1;0+0+1;2;0+0+1;0;0+0+1;2", "aff_unique_norm": "Tsinghua University;Beijing National Research Center for Information Science and Technology;Microsoft Research", "aff_unique_dep": "Department of Electronic Engineering;;", "aff_unique_url": "https://www.tsinghua.edu.cn;;https://www.microsoft.com/en-us/research", "aff_unique_abbr": "THU;;MSR", "aff_campus_unique_index": "0;0+0;0+0;2;0+0;0;0+0;2", "aff_campus_unique": "Beijing;;Redmond", "aff_country_unique_index": "0;0+0+0;0+0+0;1;0+0+0;0;0+0+0;1", "aff_country_unique": "China;United States" }, { "id": "2021.acl-long.489", "title": "Fine-grained Information Extraction from Biomedical Literature based on Knowledge-enriched Abstract Meaning Representation", "track": "main", "status": "Long", "award": false, "abstract": "Biomedical Information Extraction from scientific literature presents two unique and non-trivial challenges. First, compared with general natural language texts, sentences from scientific papers usually possess wider contexts between knowledge elements. Moreover, comprehending the fine-grained scientific entities and events urgently requires domain-specific background knowledge. In this paper, we propose a novel biomedical Information Extraction (IE) model to tackle these two challenges and extract scientific entities and events from English research papers. We perform Abstract Meaning Representation (AMR) to compress the wide context to uncover a clear semantic structure for each complex sentence. Besides, we construct the sentence-level knowledge graph from an external knowledge base and use it to enrich the AMR graph to improve the model\u2019s understanding of complex scientific concepts. We use an edge-conditioned graph attention network to encode the knowledge-enriched AMR graph for biomedical IE tasks. Experiments on the GENIA 2011 dataset show that the AMR and external knowledge have contributed 1.8% and 3.0% absolute F-score gains respectively. In order to evaluate the impact of our approach on real-world problems that involve topic-specific fine-grained knowledge elements, we have also created a new ontology and annotated corpus for entity and event extraction for the COVID-19 scientific literature, which can serve as a new benchmark for the biomedical IE community.", "author": "Zixuan Zhang; Nikolaus Parulian; Heng Ji; Ahmed Elsayed; Skatje Myers; Martha Palmer", "authorids": "/z/zixuan-zhang/; /n/nikolaus-parulian/; /h/heng-ji/; /a/ahmed-elsayed/; /s/skatje-myers/; /m/martha-palmer/", "bibtex": "@inproceedings{zhang-etal-2021-fine,\n title = \"Fine-grained Information Extraction from Biomedical Literature based on Knowledge-enriched {A}bstract {M}eaning {R}epresentation\",\n author = \"Zhang, Zixuan and\n Parulian, Nikolaus and\n Ji, Heng and\n Elsayed, Ahmed and\n Myers, Skatje and\n Palmer, Martha\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.489/\",\n doi = \"10.18653/v1/2021.acl-long.489\",\n pages = \"6261--6270\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.489.pdf", "site": "https://aclanthology.org/2021.acl-long.489/", "pdf_size": 1053868, "gs_citation": 35, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=11288558154923971348&as_sdt=5,33&sciodt=0,33&hl=en", "gs_version_total": 6, "aff": "University of Illinois at Urbana-Champaign; University of Illinois at Urbana-Champaign; University of Illinois at Urbana-Champaign; University of Colorado Boulder; University of Colorado Boulder; University of Colorado Boulder", "aff_domain": "illinois.edu;illinois.edu;illinois.edu;colorado.edu;colorado.edu;colorado.edu", "email": "illinois.edu;illinois.edu;illinois.edu;colorado.edu;colorado.edu;colorado.edu", "github": "https://github.com/zhangzx-uiuc/Knowledge-AMR", "project": "", "author_num": 6, "aff_unique_index": "0;0;0;1;1;1", "aff_unique_norm": "University of Illinois at Urbana-Champaign;University of Colorado", "aff_unique_dep": ";", "aff_unique_url": "https://illinois.edu;https://www.colorado.edu", "aff_unique_abbr": "UIUC;CU Boulder", "aff_campus_unique_index": "0;0;0;1;1;1", "aff_campus_unique": "Urbana-Champaign;Boulder", "aff_country_unique_index": "0;0;0;0;0;0", "aff_country_unique": "United States" }, { "id": "2021.acl-long.474", "title": "Focus Attention: Promoting Faithfulness and Diversity in Summarization", "track": "main", "status": "Long", "award": false, "abstract": "Professional summaries are written with document-level information, such as the theme of the document, in mind. This is in contrast with most seq2seq decoders which simultaneously learn to focus on salient content, while deciding what to generate, at each decoding step. With the motivation to narrow this gap, we introduce Focus Attention Mechanism, a simple yet effective method to encourage decoders to proactively generate tokens that are similar or topical to the input document. Further, we propose a Focus Sampling method to enable generation of diverse summaries, an area currently understudied in summarization. When evaluated on the BBC extreme summarization task, two state-of-the-art models augmented with Focus Attention generate summaries that are closer to the target and more faithful to their input documents, outperforming their vanilla counterparts on ROUGE and multiple faithfulness measures. We also empirically demonstrate that Focus Sampling is more effective in generating diverse and faithful summaries than top-k or nucleus sampling-based decoding methods.", "author": "Rahul Aralikatte; Shashi Narayan; Joshua Maynez; Sascha Rothe; Ryan McDonald", "authorids": "/r/rahul-aralikatte/; /s/shashi-narayan/; /j/joshua-maynez/; /s/sascha-rothe/; /r/ryan-mcdonald/", "bibtex": "@inproceedings{aralikatte-etal-2021-focus,\n title = \"Focus Attention: Promoting Faithfulness and Diversity in Summarization\",\n author = \"Aralikatte, Rahul and\n Narayan, Shashi and\n Maynez, Joshua and\n Rothe, Sascha and\n McDonald, Ryan\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.474/\",\n doi = \"10.18653/v1/2021.acl-long.474\",\n pages = \"6078--6095\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.474.pdf", "site": "https://aclanthology.org/2021.acl-long.474/", "pdf_size": 790174, "gs_citation": 55, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=14564612566155898771&as_sdt=40005&sciodt=0,10&hl=en", "gs_version_total": 6, "aff": "University of Copenhagen; Google Research; Google Research; Google Research; ASAPP", "aff_domain": "di.ku.dk;google.com;google.com;google.com;asapp.com", "email": "di.ku.dk;google.com;google.com;google.com;asapp.com", "github": "", "project": "", "author_num": 5, "aff_unique_index": "0;1;1;1;2", "aff_unique_norm": "University of Copenhagen;Google;ASAPP", "aff_unique_dep": ";Google Research;", "aff_unique_url": "https://www.ku.dk;https://research.google;https://www.asapp.com", "aff_unique_abbr": "UCPH;Google Research;ASAPP", "aff_campus_unique_index": "1;1;1", "aff_campus_unique": ";Mountain View", "aff_country_unique_index": "0;1;1;1;1", "aff_country_unique": "Denmark;United States" }, { "id": "2021.acl-long.357", "title": "ForecastQA: A Question Answering Challenge for Event Forecasting with Temporal Text Data", "track": "main", "status": "Long", "award": false, "abstract": "Event forecasting is a challenging, yet important task, as humans seek to constantly plan for the future. Existing automated forecasting studies rely mostly on structured data, such as time-series or event-based knowledge graphs, to help predict future events. In this work, we aim to formulate a task, construct a dataset, and provide benchmarks for developing methods for event forecasting with large volumes of unstructured text data. To simulate the forecasting scenario on temporal news documents, we formulate the problem as a restricted-domain, multiple-choice, question-answering (QA) task. Unlike existing QA tasks, our task limits accessible information, and thus a model has to make a forecasting judgement. To showcase the usefulness of this task formulation, we introduce ForecastQA, a question-answering dataset consisting of 10,392 event forecasting questions, which have been collected and verified via crowdsourcing efforts. We present our experiments on ForecastQA using BERTbased models and find that our best model achieves 61.0% accuracy on the dataset, which still lags behind human performance by about 19%. We hope ForecastQA will support future research efforts in bridging this gap.", "author": "Woojeong Jin; Rahul Khanna; Suji Kim; Dong-Ho Lee; Fred Morstatter; Aram Galstyan; Xiang Ren", "authorids": "/w/woojeong-jin/; /r/rahul-khanna/; /s/suji-kim/; /d/dong-ho-lee/; /f/fred-morstatter/; /a/aram-galstyan/; /x/xiang-ren/", "bibtex": "@inproceedings{jin-etal-2021-forecastqa,\n title = \"{F}orecast{QA}: A Question Answering Challenge for Event Forecasting with Temporal Text Data\",\n author = \"Jin, Woojeong and\n Khanna, Rahul and\n Kim, Suji and\n Lee, Dong-Ho and\n Morstatter, Fred and\n Galstyan, Aram and\n Ren, Xiang\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.357/\",\n doi = \"10.18653/v1/2021.acl-long.357\",\n pages = \"4636--4650\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.357.pdf", "site": "https://aclanthology.org/2021.acl-long.357/", "pdf_size": 2191550, "gs_citation": 48, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=16546120923710935277&as_sdt=5,33&sciodt=0,33&hl=en", "gs_version_total": 5, "aff": "Department of Computer Science, University of Southern California + Information Sciences Institute, University of Southern California; Department of Computer Science, University of Southern California + Information Sciences Institute, University of Southern California; Department of Computer Science, University of Southern California + Information Sciences Institute, University of Southern California; Department of Computer Science, University of Southern California + Information Sciences Institute, University of Southern California; Information Sciences Institute, University of Southern California; Information Sciences Institute, University of Southern California; Department of Computer Science, University of Southern California + Information Sciences Institute, University of Southern California", "aff_domain": "usc.edu;usc.edu;usc.edu;usc.edu;isi.edu;isi.edu;usc.edu", "email": "usc.edu;usc.edu;usc.edu;usc.edu;isi.edu;isi.edu;usc.edu", "github": "", "project": "https://inklab.usc.edu/ForecastQA/", "author_num": 7, "aff_unique_index": "0+0;0+0;0+0;0+0;0;0;0+0", "aff_unique_norm": "University of Southern California", "aff_unique_dep": "Department of Computer Science", "aff_unique_url": "https://www.usc.edu", "aff_unique_abbr": "USC", "aff_campus_unique_index": "0+0;0+0;0+0;0+0;0;0;0+0", "aff_campus_unique": "Los Angeles", "aff_country_unique_index": "0+0;0+0;0+0;0+0;0;0;0+0", "aff_country_unique": "United States" }, { "id": "2021.acl-long.60", "title": "From Discourse to Narrative: Knowledge Projection for Event Relation Extraction", "track": "main", "status": "Long", "award": false, "abstract": "Current event-centric knowledge graphs highly rely on explicit connectives to mine relations between events. Unfortunately, due to the sparsity of connectives, these methods severely undermine the coverage of EventKGs. The lack of high-quality labelled corpora further exacerbates that problem. In this paper, we propose a knowledge projection paradigm for event relation extraction: projecting discourse knowledge to narratives by exploiting the commonalities between them. Specifically, we propose Multi-tier Knowledge Projection Network (MKPNet), which can leverage multi-tier discourse knowledge effectively for event relation extraction. In this way, the labelled data requirement is significantly reduced, and implicit event relations can be effectively extracted. Intrinsic experimental results show that MKPNet achieves the new state-of-the-art performance and extrinsic experimental results verify the value of the extracted event relations.", "author": "Jialong Tang; Hongyu Lin; Meng Liao; Yaojie Lu; Xianpei Han; Le Sun; Weijian Xie; Jin Xu", "authorids": "/j/jialong-tang/; /h/hongyu-lin/; /m/meng-liao/; /y/yaojie-lu/; /x/xianpei-han/; /l/le-sun/; /w/weijian-xie/; /j/jin-xu/", "bibtex": "https://aclanthology.org/2021.acl-long.60.bib", "pdf": "https://aclanthology.org/2021.acl-long.60.pdf", "site": "https://aclanthology.org/2021.acl-long.60/", "gs_citation": 28, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=18377108473430740456&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 4, "aff": "Chinese Information Processing Laboratory + State Key Laboratory of Computer Science, Institute of Software, Chinese Academy of Sciences, Beijing, China + University of Chinese Academy of Sciences, Beijing, China; Chinese Information Processing Laboratory + State Key Laboratory of Computer Science, Institute of Software, Chinese Academy of Sciences, Beijing, China; Data Quality Team, WeChat, Tencent Inc., China; Chinese Information Processing Laboratory + State Key Laboratory of Computer Science, Institute of Software, Chinese Academy of Sciences, Beijing, China + University of Chinese Academy of Sciences, Beijing, China; Chinese Information Processing Laboratory + State Key Laboratory of Computer Science, Institute of Software, Chinese Academy of Sciences, Beijing, China; Chinese Information Processing Laboratory + State Key Laboratory of Computer Science, Institute of Software, Chinese Academy of Sciences, Beijing, China; Data Quality Team, WeChat, Tencent Inc., China; Data Quality Team, WeChat, Tencent Inc., China", "aff_domain": "iscas.ac.cn;iscas.ac.cn;tencent.com; ;iscas.ac.cn;iscas.ac.cn;tencent.com;tencent.com", "email": "iscas.ac.cn;iscas.ac.cn;tencent.com; ;iscas.ac.cn;iscas.ac.cn;tencent.com;tencent.com", "github": "", "project": "", "author_num": 8, "aff_unique_index": "0+1+2;0+1;3;0+1+2;0+1;0+1;3;3", "aff_unique_norm": "Chinese Information Processing Laboratory;Chinese Academy of Sciences;University of Chinese Academy of Sciences;Tencent Inc.", "aff_unique_dep": "Information Processing;Institute of Software;;Data Quality Team", "aff_unique_url": ";http://www.ios.ac.cn;http://www.ucas.ac.cn;https://www.tencent.com", "aff_unique_abbr": ";CAS;UCAS;Tencent", "aff_campus_unique_index": "1+1;1;1+1;1;1", "aff_campus_unique": ";Beijing", "aff_country_unique_index": "0+0+0;0+0;0;0+0+0;0+0;0+0;0;0", "aff_country_unique": "China" }, { "id": "2021.acl-long.245", "title": "From Machine Translation to Code-Switching: Generating High-Quality Code-Switched Text", "track": "main", "status": "Long", "award": false, "abstract": "Generating code-switched text is a problem of growing interest, especially given the scarcity of corpora containing large volumes of real code-switched text. In this work, we adapt a state-of-the-art neural machine translation model to generate Hindi-English code-switched sentences starting from monolingual Hindi sentences. We outline a carefully designed curriculum of pretraining steps, including the use of synthetic code-switched text, that enable the model to generate high-quality code-switched text. Using text generated from our model as data augmentation, we show significant reductions in perplexity on a language modeling task, compared to using text from other generative models of CS text. We also show improvements using our text for a downstream code-switched natural language inference task. Our generated text is further subjected to a rigorous evaluation using a human evaluation study and a range of objective metrics, where we show performance comparable (and sometimes even superior) to code-switched text obtained via crowd workers who are native Hindi speakers.", "author": "Ishan Tarunesh; Syamantak Kumar; Preethi Jyothi", "authorids": "/i/ishan-tarunesh/; /s/syamantak-kumar/; /p/preethi-jyothi/", "bibtex": "@inproceedings{tarunesh-etal-2021-machine,\n title = \"From Machine Translation to Code-Switching: Generating High-Quality Code-Switched Text\",\n author = \"Tarunesh, Ishan and\n Kumar, Syamantak and\n Jyothi, Preethi\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.245/\",\n doi = \"10.18653/v1/2021.acl-long.245\",\n pages = \"3154--3169\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.245.pdf", "site": "https://aclanthology.org/2021.acl-long.245/", "pdf_size": 1151916, "gs_citation": 49, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=14376876114721271337&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 7, "aff": "Samsung Korea + IIT Bombay; Google India + IIT Bombay; IIT Bombay", "aff_domain": "gmail.com;gmail.com;cse.iitb.ac.in", "email": "gmail.com;gmail.com;cse.iitb.ac.in", "github": "", "project": "", "author_num": 3, "aff_unique_index": "0+1;2+1;1", "aff_unique_norm": "Samsung;Indian Institute of Technology Bombay;Google", "aff_unique_dep": ";;", "aff_unique_url": "https://www.samsung.com;https://www.iitb.ac.in;https://www.google.com", "aff_unique_abbr": "Samsung;IITB;Google India", "aff_campus_unique_index": "1;2+1;1", "aff_campus_unique": ";Mumbai;Bangalore", "aff_country_unique_index": "0+1;1+1;1", "aff_country_unique": "South Korea;India" }, { "id": "2021.acl-long.397", "title": "From Paraphrasing to Semantic Parsing: Unsupervised Semantic Parsing via Synchronous Semantic Decoding", "track": "main", "status": "Long", "award": false, "abstract": "Semantic parsing is challenging due to the structure gap and the semantic gap between utterances and logical forms. In this paper, we propose an unsupervised semantic parsing method - Synchronous Semantic Decoding (SSD), which can simultaneously resolve the semantic gap and the structure gap by jointly leveraging paraphrasing and grammar-constrained decoding. Specifically, we reformulate semantic parsing as a constrained paraphrasing problem: given an utterance, our model synchronously generates its canonical utterancel and meaning representation. During synchronously decoding: the utterance paraphrasing is constrained by the structure of the logical form, therefore the canonical utterance can be paraphrased controlledly; the semantic decoding is guided by the semantics of the canonical utterance, therefore its logical form can be generated unsupervisedly. Experimental results show that SSD is a promising approach and can achieve state-of-the-art unsupervised semantic parsing performance on multiple datasets.", "author": "Shan Wu; Bo Chen; Chunlei Xin; Xianpei Han; Le Sun; Weipeng Zhang; Jiansong Chen; Fan Yang; Xunliang Cai", "authorids": "/s/shan-wu/; /b/bo-chen/; /c/chunlei-xin/; /x/xianpei-han/; /l/le-sun/; /w/weipeng-zhang/; /j/jiansong-chen/; /f/fan-yang/; /x/xunliang-cai/", "bibtex": "@inproceedings{wu-etal-2021-paraphrasing,\n title = \"From Paraphrasing to Semantic Parsing: Unsupervised Semantic Parsing via Synchronous Semantic Decoding\",\n author = \"Wu, Shan and\n Chen, Bo and\n Xin, Chunlei and\n Han, Xianpei and\n Sun, Le and\n Zhang, Weipeng and\n Chen, Jiansong and\n Yang, Fan and\n Cai, Xunliang\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.397/\",\n doi = \"10.18653/v1/2021.acl-long.397\",\n pages = \"5110--5121\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.397.pdf", "site": "https://aclanthology.org/2021.acl-long.397/", "pdf_size": 1014797, "gs_citation": 26, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=7439574112367859280&as_sdt=5,44&sciodt=0,44&hl=en", "gs_version_total": 5, "aff": "Chinese Information Processing Laboratory+University of Chinese Academy of Sciences; Chinese Information Processing Laboratory+University of Chinese Academy of Sciences; Chinese Information Processing Laboratory+University of Chinese Academy of Sciences; Chinese Information Processing Laboratory+State Key Laboratory of Computer Science+Institute of Software, Chinese Academy of Sciences; Chinese Information Processing Laboratory+State Key Laboratory of Computer Science+Institute of Software, Chinese Academy of Sciences; Meituan; Meituan; Meituan; Meituan", "aff_domain": "iscas.ac.cn;iscas.ac.cn;mails.ucas.ac.cn;iscas.ac.cn;iscas.ac.cn;meituan.com;meituan.com;meituan.com;meituan.com", "email": "iscas.ac.cn;iscas.ac.cn;mails.ucas.ac.cn;iscas.ac.cn;iscas.ac.cn;meituan.com;meituan.com;meituan.com;meituan.com", "github": "", "project": "", "author_num": 9, "aff_unique_index": "0+1;0+1;0+1;0+2+3;0+2+3;4;4;4;4", "aff_unique_norm": "Chinese Information Processing Laboratory;University of Chinese Academy of Sciences;State Key Laboratory of Computer Science;Chinese Academy of Sciences;Meituan", "aff_unique_dep": "Information Processing;;;Institute of Software;", "aff_unique_url": ";http://www.ucas.ac.cn;;http://www.ios.ac.cn;https://www.meituan.com", "aff_unique_abbr": ";UCAS;;CAS;Meituan", "aff_campus_unique_index": ";;;;", "aff_campus_unique": "", "aff_country_unique_index": "0+0;0+0;0+0;0+0+0;0+0+0;0;0;0;0", "aff_country_unique": "China" }, { "id": "2021.acl-long.267", "title": "G-Transformer for Document-Level Machine Translation", "track": "main", "status": "Long", "award": false, "abstract": "Document-level MT models are still far from satisfactory. Existing work extend translation unit from single sentence to multiple sentences. However, study shows that when we further enlarge the translation unit to a whole document, supervised training of Transformer can fail. In this paper, we find such failure is not caused by overfitting, but by sticking around local minima during training. Our analysis shows that the increased complexity of target-to-source attention is a reason for the failure. As a solution, we propose G-Transformer, introducing locality assumption as an inductive bias into Transformer, reducing the hypothesis space of the attention from target to source. Experiments show that G-Transformer converges faster and more stably than Transformer, achieving new state-of-the-art BLEU scores for both nonpretraining and pre-training settings on three benchmark datasets.", "author": "Guangsheng Bao; Yue Zhang; Zhiyang Teng; Boxing Chen; Weihua Luo", "authorids": "/g/guangsheng-bao/; /y/yue-zhang/; /z/zhiyang-teng/; /b/boxing-chen/; /w/weihua-luo/", "bibtex": "@inproceedings{bao-etal-2021-g,\n title = \"{G}-Transformer for Document-Level Machine Translation\",\n author = \"Bao, Guangsheng and\n Zhang, Yue and\n Teng, Zhiyang and\n Chen, Boxing and\n Luo, Weihua\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.267/\",\n doi = \"10.18653/v1/2021.acl-long.267\",\n pages = \"3442--3455\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.267.pdf", "site": "https://aclanthology.org/2021.acl-long.267/", "pdf_size": 1066195, "gs_citation": 95, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=7757537960429806567&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 7, "aff": "School of Engineering, Westlake University + Institute of Advanced Technology, Westlake Institute for Advanced Study; School of Engineering, Westlake University + Institute of Advanced Technology, Westlake Institute for Advanced Study; School of Engineering, Westlake University + Institute of Advanced Technology, Westlake Institute for Advanced Study; DAMO Academy, Alibaba Group Inc.; DAMO Academy, Alibaba Group Inc.", "aff_domain": "westlake.edu.cn;westlake.edu.cn;westlake.edu.cn;alibaba-inc.com;alibaba-inc.com", "email": "westlake.edu.cn;westlake.edu.cn;westlake.edu.cn;alibaba-inc.com;alibaba-inc.com", "github": "", "project": "", "author_num": 5, "aff_unique_index": "0+1;0+1;0+1;2;2", "aff_unique_norm": "Westlake University;Westlake Institute for Advanced Study;Alibaba Group Inc.", "aff_unique_dep": "School of Engineering;Institute of Advanced Technology;DAMO Academy", "aff_unique_url": "https://www.westlake.edu.cn;http://www.wias.org.cn/;https://www.alibaba-group.com", "aff_unique_abbr": ";WIAS;Alibaba", "aff_campus_unique_index": ";;", "aff_campus_unique": "", "aff_country_unique_index": "0+0;0+0;0+0;0;0", "aff_country_unique": "China" }, { "id": "2021.acl-long.15", "title": "GL-GIN: Fast and Accurate Non-Autoregressive Model for Joint Multiple Intent Detection and Slot Filling", "track": "main", "status": "Long", "award": false, "abstract": "Multi-intent SLU can handle multiple intents in an utterance, which has attracted increasing attention. However, the state-of-the-art joint models heavily rely on autoregressive approaches, resulting in two issues: slow inference speed and information leakage. In this paper, we explore a non-autoregressive model for joint multiple intent detection and slot filling, achieving more fast and accurate. Specifically, we propose a Global-Locally Graph Interaction Network (GL-GIN) where a local slot-aware graph interaction layer is proposed to model slot dependency for alleviating uncoordinated slots problem while a global intent-slot graph interaction layer is introduced to model the interaction between multiple intents and all slots in the utterance. Experimental results on two public datasets show that our framework achieves state-of-the-art performance while being 11.5 times faster.", "author": "Libo Qin; Fuxuan Wei; Tianbao Xie; Xiao Xu; Wanxiang Che; Ting Liu", "authorids": "/l/libo-qin/; /f/fuxuan-wei/; /t/tianbao-xie/; /x/xiao-xu/; /w/wanxiang-che/; /t/ting-liu/", "bibtex": "@inproceedings{qin-etal-2021-gl,\n title = \"{GL}-{GIN}: Fast and Accurate Non-Autoregressive Model for Joint Multiple Intent Detection and Slot Filling\",\n author = \"Qin, Libo and\n Wei, Fuxuan and\n Xie, Tianbao and\n Xu, Xiao and\n Che, Wanxiang and\n Liu, Ting\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.15/\",\n doi = \"10.18653/v1/2021.acl-long.15\",\n pages = \"178--188\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.15.pdf", "site": "https://aclanthology.org/2021.acl-long.15/", "pdf_size": 630512, "gs_citation": 98, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=13662860541414645162&as_sdt=5,44&sciodt=0,44&hl=en", "gs_version_total": 6, "aff": "Research Center for Social Computing and Information Retrieval, Harbin Institute of Technology, China; Research Center for Social Computing and Information Retrieval, Harbin Institute of Technology, China; Research Center for Social Computing and Information Retrieval, Harbin Institute of Technology, China; Research Center for Social Computing and Information Retrieval, Harbin Institute of Technology, China; Research Center for Social Computing and Information Retrieval, Harbin Institute of Technology, China; Research Center for Social Computing and Information Retrieval, Harbin Institute of Technology, China", "aff_domain": "ir.hit.edu.cn;ir.hit.edu.cn;ir.hit.edu.cn;ir.hit.edu.cn;ir.hit.edu.cn;ir.hit.edu.cn", "email": "ir.hit.edu.cn;ir.hit.edu.cn;ir.hit.edu.cn;ir.hit.edu.cn;ir.hit.edu.cn;ir.hit.edu.cn", "github": "", "project": "", "author_num": 6, "aff_unique_index": "0;0;0;0;0;0", "aff_unique_norm": "Harbin Institute of Technology", "aff_unique_dep": "Research Center for Social Computing and Information Retrieval", "aff_unique_url": "http://www.hit.edu.cn/", "aff_unique_abbr": "HIT", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0;0;0;0;0;0", "aff_country_unique": "China" }, { "id": "2021.acl-long.271", "title": "GTM: A Generative Triple-wise Model for Conversational Question Generation", "track": "main", "status": "Long", "award": false, "abstract": "Generating some appealing questions in open-domain conversations is an effective way to improve human-machine interactions and lead the topic to a broader or deeper direction. To avoid dull or deviated questions, some researchers tried to utilize answer, the \u201cfuture\u201d information, to guide question generation. However, they separate a post-question-answer (PQA) triple into two parts: post-question (PQ) and question-answer (QA) pairs, which may hurt the overall coherence. Besides, the QA relationship is modeled as a one-to-one mapping that is not reasonable in open-domain conversations. To tackle these problems, we propose a generative triple-wise model with hierarchical variations for open-domain conversational question generation (CQG). Latent variables in three hierarchies are used to represent the shared background of a triple and one-to-many semantic mappings in both PQ and QA pairs. Experimental results on a large-scale CQG dataset show that our method significantly improves the quality of questions in terms of fluency, coherence and diversity over competitive baselines.", "author": "Lei Shen; Fandong Meng; Jinchao Zhang; Yang Feng; Jie Zhou", "authorids": "/l/lei-shen/; /f/fandong-meng/; /j/jinchao-zhang/; /y/yang-feng/; /j/jie-zhou/", "bibtex": "@inproceedings{shen-etal-2021-gtm,\n title = \"{GTM}: A Generative Triple-wise Model for Conversational Question Generation\",\n author = \"Shen, Lei and\n Meng, Fandong and\n Zhang, Jinchao and\n Feng, Yang and\n Zhou, Jie\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.271/\",\n doi = \"10.18653/v1/2021.acl-long.271\",\n pages = \"3495--3506\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.271.pdf", "site": "https://aclanthology.org/2021.acl-long.271/", "pdf_size": 677233, "gs_citation": 16, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=11506779289587067568&as_sdt=5,33&sciodt=0,33&hl=en", "gs_version_total": 4, "aff": "Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China+University of Chinese Academy of Sciences, Beijing, China; Pattern Recognition Center, WeChat AI, Tencent Inc, China; Pattern Recognition Center, WeChat AI, Tencent Inc, China; Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China+University of Chinese Academy of Sciences, Beijing, China; Pattern Recognition Center, WeChat AI, Tencent Inc, China", "aff_domain": "ict.ac.cn;tencent.com;tencent.com;ict.ac.cn;tencent.com", "email": "ict.ac.cn;tencent.com;tencent.com;ict.ac.cn;tencent.com", "github": "", "project": "", "author_num": 5, "aff_unique_index": "0+1;2;2;0+1;2", "aff_unique_norm": "Chinese Academy of Sciences;University of Chinese Academy of Sciences;Tencent Inc", "aff_unique_dep": "Institute of Computing Technology;;Pattern Recognition Center, WeChat AI", "aff_unique_url": "http://www.ict.ac.cn;http://www.ucas.ac.cn;https://www.tencent.com", "aff_unique_abbr": "CAS;UCAS;Tencent", "aff_campus_unique_index": "0+0;0+0", "aff_campus_unique": "Beijing;", "aff_country_unique_index": "0+0;0;0;0+0;0", "aff_country_unique": "China" }, { "id": "2021.acl-long.370", "title": "GWLAN: General Word-Level AutocompletioN for Computer-Aided Translation", "track": "main", "status": "Long", "award": false, "abstract": "Computer-aided translation (CAT), the use of software to assist a human translator in the translation process, has been proven to be useful in enhancing the productivity of human translators. Autocompletion, which suggests translation results according to the text pieces provided by human translators, is a core function of CAT. There are two limitations in previous research in this line. First, most research works on this topic focus on sentence-level autocompletion (i.e., generating the whole translation as a sentence based on human input), but word-level autocompletion is under-explored so far. Second, almost no public benchmarks are available for the autocompletion task of CAT. This might be among the reasons why research progress in CAT is much slower compared to automatic MT. In this paper, we propose the task of general word-level autocompletion (GWLAN) from a real-world CAT scenario, and construct the first public benchmark to facilitate research in this topic. In addition, we propose an effective method for GWLAN and compare it with several strong baselines. Experiments demonstrate that our proposed method can give significantly more accurate predictions than the baseline methods on our benchmark datasets.", "author": "Huayang Li; Lemao Liu; Guoping Huang; Shuming Shi", "authorids": "/h/huayang-li/; /l/lemao-liu/; /g/guoping-huang/; /s/shuming-shi/", "bibtex": "@inproceedings{li-etal-2021-gwlan,\n title = \"{GWLAN}: General Word-Level {A}utocompletio{N} for Computer-Aided Translation\",\n author = \"Li, Huayang and\n Liu, Lemao and\n Huang, Guoping and\n Shi, Shuming\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.370/\",\n doi = \"10.18653/v1/2021.acl-long.370\",\n pages = \"4792--4802\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.370.pdf", "site": "https://aclanthology.org/2021.acl-long.370/", "pdf_size": 623064, "gs_citation": 21, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=11466964973463491225&as_sdt=5,24&sciodt=0,24&hl=en", "gs_version_total": 5, "aff": "Tencent AI Lab; Tencent AI Lab; Tencent AI Lab; Tencent AI Lab", "aff_domain": "tencent.com;tencent.com;tencent.com;tencent.com", "email": "tencent.com;tencent.com;tencent.com;tencent.com", "github": "https://github.com/ghrua/gwlan", "project": "", "author_num": 4, "aff_unique_index": "0;0;0;0", "aff_unique_norm": "Tencent", "aff_unique_dep": "Tencent AI Lab", "aff_unique_url": "https://ai.tencent.com", "aff_unique_abbr": "Tencent AI Lab", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0;0;0;0", "aff_country_unique": "China" }, { "id": "2021.acl-short.15", "title": "Gender bias amplification during Speed-Quality optimization in Neural Machine Translation", "track": "main", "status": "Short", "award": false, "abstract": "Is bias amplified when neural machine translation (NMT) models are optimized for speed and evaluated on generic test sets using BLEU? We investigate architectures and techniques commonly used to speed up decoding in Transformer-based models, such as greedy search, quantization, average attention networks (AANs) and shallow decoder models and show their effect on gendered noun translation. We construct a new gender bias test set, SimpleGEN, based on gendered noun phrases in which there is a single, unambiguous, correct answer. While we find minimal overall BLEU degradation as we apply speed optimizations, we observe that gendered noun translation performance degrades at a much faster rate.", "author": "Adithya Renduchintala; Denise Diaz; Kenneth Heafield; Xian Li; Mona Diab", "authorids": "/a/adithya-renduchintala/; /d/denise-diaz/; /k/kenneth-heafield/; /x/xian-li/; /m/mona-diab/", "bibtex": "@inproceedings{renduchintala-etal-2021-gender,\n title = \"Gender bias amplification during Speed-Quality optimization in Neural Machine Translation\",\n author = \"Renduchintala, Adithya and\n Diaz, Denise and\n Heafield, Kenneth and\n Li, Xian and\n Diab, Mona\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-short.15/\",\n doi = \"10.18653/v1/2021.acl-short.15\",\n pages = \"99--109\"\n}", "pdf": "https://aclanthology.org/2021.acl-short.15.pdf", "site": "https://aclanthology.org/2021.acl-short.15/", "pdf_size": 395777, "gs_citation": 48, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=12938050928236122866&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 8, "aff": "Facebook AI; Facebook AI + Independent Researcher; Facebook AI; Facebook AI; Facebook AI", "aff_domain": "fb.com;gmail.com;fb.com;fb.com;fb.com", "email": "fb.com;gmail.com;fb.com;fb.com;fb.com", "github": "", "project": "", "author_num": 5, "aff_unique_index": "0;0+1;0;0;0", "aff_unique_norm": "Facebook;Independent Researcher", "aff_unique_dep": "Facebook AI;", "aff_unique_url": "https://www.facebook.com;", "aff_unique_abbr": "Facebook AI;", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0;0;0;0;0", "aff_country_unique": "United States;" }, { "id": "2021.acl-long.10", "title": "Generalising Multilingual Concept-to-Text NLG with Language Agnostic Delexicalisation", "track": "main", "status": "Long", "award": false, "abstract": "Concept-to-text Natural Language Generation is the task of expressing an input meaning representation in natural language. Previous approaches in this task have been able to generalise to rare or unseen instances by relying on a delexicalisation of the input. However, this often requires that the input appears verbatim in the output text. This poses challenges in multilingual settings, where the task expands to generate the output text in multiple languages given the same input. In this paper, we explore the application of multilingual models in concept-to-text and propose Language Agnostic Delexicalisation, a novel delexicalisation method that uses multilingual pretrained embeddings, and employs a character-level post-editing model to inflect words in their correct form during relexicalisation. Our experiments across five datasets and five languages show that multilingual models outperform monolingual models in concept-to-text and that our framework outperforms previous approaches, especially in low resource conditions.", "author": "Giulio Zhou; Gerasimos Lampouras", "authorids": "/g/giulio-zhou/; /g/gerasimos-lampouras/", "bibtex": "@inproceedings{zhou-lampouras-2021-generalising,\n title = \"Generalising Multilingual Concept-to-Text {NLG} with Language Agnostic Delexicalisation\",\n author = \"Zhou, Giulio and\n Lampouras, Gerasimos\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.10/\",\n doi = \"10.18653/v1/2021.acl-long.10\",\n pages = \"114--127\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.10.pdf", "site": "https://aclanthology.org/2021.acl-long.10/", "pdf_size": 1161184, "gs_citation": 4, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=14023817283855851166&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 5, "aff": "Huawei Noah\u2019s Ark Lab, London, UK; Huawei Noah\u2019s Ark Lab, London, UK", "aff_domain": "huawei.com;huawei.com", "email": "huawei.com;huawei.com", "github": "", "project": "", "author_num": 2, "aff_unique_index": "0;0", "aff_unique_norm": "Huawei Noah\u2019s Ark Lab", "aff_unique_dep": "", "aff_unique_url": "https://www.huawei.com/en/ai", "aff_unique_abbr": "HNA Lab", "aff_campus_unique_index": "0;0", "aff_campus_unique": "London", "aff_country_unique_index": "0;0", "aff_country_unique": "United Kingdom" }, { "id": "2021.acl-long.41", "title": "Generating Landmark Navigation Instructions from Maps as a Graph-to-Text Problem", "track": "main", "status": "Long", "award": false, "abstract": "Car-focused navigation services are based on turns and distances of named streets, whereas navigation instructions naturally used by humans are centered around physical objects called landmarks. We present a neural model that takes OpenStreetMap representations as input and learns to generate navigation instructions that contain visible and salient landmarks from human natural language instructions. Routes on the map are encoded in a location- and rotation-invariant graph representation that is decoded into natural language instructions. Our work is based on a novel dataset of 7,672 crowd-sourced instances that have been verified by human navigation in Street View. Our evaluation shows that the navigation instructions generated by our system have similar properties as human-generated instructions, and lead to successful human navigation in Street View.", "author": "Raphael Schumann; Stefan Riezler", "authorids": "/r/raphael-schumann/; /s/stefan-riezler/", "bibtex": "@inproceedings{schumann-riezler-2021-generating,\n title = \"Generating Landmark Navigation Instructions from Maps as a Graph-to-Text Problem\",\n author = \"Schumann, Raphael and\n Riezler, Stefan\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.41/\",\n doi = \"10.18653/v1/2021.acl-long.41\",\n pages = \"489--502\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.41.pdf", "site": "https://aclanthology.org/2021.acl-long.41/", "pdf_size": 4424960, "gs_citation": 27, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=6906526619051879055&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 7, "aff": "Heidelberg University, Germany; Heidelberg University, Germany", "aff_domain": "cl.uni-heidelberg.de;cl.uni-heidelberg.de", "email": "cl.uni-heidelberg.de;cl.uni-heidelberg.de", "github": "", "project": "www.openstreetmap.org; www.google.com/streetview", "author_num": 2, "aff_unique_index": "0;0", "aff_unique_norm": "Heidelberg University", "aff_unique_dep": "", "aff_unique_url": "https://www.uni-heidelberg.de", "aff_unique_abbr": "Uni Heidelberg", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0;0", "aff_country_unique": "Germany" }, { "id": "2021.acl-long.475", "title": "Generating Query Focused Summaries from Query-Free Resources", "track": "main", "status": "Long", "award": false, "abstract": "The availability of large-scale datasets has driven the development of neural models that create generic summaries from single or multiple documents. In this work we consider query focused summarization (QFS), a task for which training data in the form of queries, documents, and summaries is not readily available. We propose to decompose QFS into (1) query modeling (i.e., finding supportive evidence within a set of documents for a query) and (2) conditional language modeling (i.e., summary generation). We introduce MaRGE, a Masked ROUGE Regression framework for evidence estimation and ranking which relies on a unified representation for summaries and queries, so that summaries in generic data can be converted into proxy queries for learning a query model. Experiments across QFS benchmarks and query types show that our model achieves state-of-the-art performance despite learning from weak supervision.", "author": "Yumo Xu; Mirella Lapata", "authorids": "/y/yumo-xu/; /m/mirella-lapata/", "bibtex": "@inproceedings{xu-lapata-2021-generating,\n title = \"Generating Query Focused Summaries from Query-Free Resources\",\n author = \"Xu, Yumo and\n Lapata, Mirella\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.475/\",\n doi = \"10.18653/v1/2021.acl-long.475\",\n pages = \"6096--6109\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.475.pdf", "site": "https://aclanthology.org/2021.acl-long.475/", "pdf_size": 716029, "gs_citation": 45, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=14560330866430463481&as_sdt=5,33&sciodt=0,33&hl=en", "gs_version_total": 5, "aff": "Institute for Language, Cognition and Computation, School of Informatics, University of Edinburgh; Institute for Language, Cognition and Computation, School of Informatics, University of Edinburgh", "aff_domain": "ed.ac.uk;inf.ed.ac.uk", "email": "ed.ac.uk;inf.ed.ac.uk", "github": "https://github.com/yumoxu/marge", "project": "", "author_num": 2, "aff_unique_index": "0;0", "aff_unique_norm": "University of Edinburgh", "aff_unique_dep": "School of Informatics", "aff_unique_url": "https://www.ed.ac.uk", "aff_unique_abbr": "Edinburgh", "aff_campus_unique_index": "0;0", "aff_campus_unique": "Edinburgh", "aff_country_unique_index": "0;0", "aff_country_unique": "United Kingdom" }, { "id": "2021.acl-long.437", "title": "Generating Relevant and Coherent Dialogue Responses using Self-Separated Conditional Variational AutoEncoders", "track": "main", "status": "Long", "award": false, "abstract": "Conditional Variational AutoEncoder (CVAE) effectively increases the diversity and informativeness of responses in open-ended dialogue generation tasks through enriching the context vector with sampled latent variables. However, due to the inherent one-to-many and many-to-one phenomena in human dialogues, the sampled latent variables may not correctly reflect the contexts\u2019 semantics, leading to irrelevant and incoherent generated responses. To resolve this problem, we propose Self-separated Conditional Variational AutoEncoder (abbreviated as SepaCVAE) that introduces group information to regularize the latent variables, which enhances CVAE by improving the responses\u2019 relevance and coherence while maintaining their diversity and informativeness. SepaCVAE actively divides the input data into groups, and then widens the absolute difference between data pairs from distinct groups, while narrowing the relative distance between data pairs in the same group. Empirical results from automatic evaluation and detailed analysis demonstrate that SepaCVAE can significantly boost responses in well-established open-domain dialogue datasets.", "author": "Bin Sun; Shaoxiong Feng; Yiwei Li; Jiamou Liu; Kan Li", "authorids": "/b/bin-sun/; /s/shaoxiong-feng/; /y/yiwei-li/; /j/jiamou-liu/; /k/kan-li/", "bibtex": "@inproceedings{sun-etal-2021-generating,\n title = \"Generating Relevant and Coherent Dialogue Responses using Self-Separated Conditional Variational {A}uto{E}ncoders\",\n author = \"Sun, Bin and\n Feng, Shaoxiong and\n Li, Yiwei and\n Liu, Jiamou and\n Li, Kan\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.437/\",\n doi = \"10.18653/v1/2021.acl-long.437\",\n pages = \"5624--5637\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.437.pdf", "site": "https://aclanthology.org/2021.acl-long.437/", "pdf_size": 1367264, "gs_citation": 36, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=13939122646322829001&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 8, "aff": "School of Computer Science & Technology, Beijing Institute of Technology; School of Computer Science & Technology, Beijing Institute of Technology; School of Computer Science & Technology, Beijing Institute of Technology; School of Computer Science, The University of Auckland; School of Computer Science & Technology, Beijing Institute of Technology", "aff_domain": "bit.edu.cn;bit.edu.cn;bit.edu.cn;auckland.ac.nz;bit.edu.cn", "email": "bit.edu.cn;bit.edu.cn;bit.edu.cn;auckland.ac.nz;bit.edu.cn", "github": "", "project": "", "author_num": 5, "aff_unique_index": "0;0;0;1;0", "aff_unique_norm": "Beijing Institute of Technology;The University of Auckland", "aff_unique_dep": "School of Computer Science & Technology;School of Computer Science", "aff_unique_url": "http://www.bit.edu.cn/;https://www.auckland.ac.nz", "aff_unique_abbr": "BIT;UoA", "aff_campus_unique_index": "1", "aff_campus_unique": ";Auckland", "aff_country_unique_index": "0;0;0;1;0", "aff_country_unique": "China;New Zealand" }, { "id": "2021.acl-long.384", "title": "Generating SOAP Notes from Doctor-Patient Conversations Using Modular Summarization Techniques", "track": "main", "status": "Long", "award": false, "abstract": "Following each patient visit, physicians draft long semi-structured clinical summaries called SOAP notes. While invaluable to clinicians and researchers, creating digital SOAP notes is burdensome, contributing to physician burnout. In this paper, we introduce the first complete pipelines to leverage deep summarization models to generate these notes based on transcripts of conversations between physicians and patients. After exploring a spectrum of methods across the extractive-abstractive spectrum, we propose Cluster2Sent, an algorithm that (i) extracts important utterances relevant to each summary section; (ii) clusters together related utterances; and then (iii) generates one summary sentence per cluster. Cluster2Sent outperforms its purely abstractive counterpart by 8 ROUGE-1 points, and produces significantly more factual and coherent sentences as assessed by expert human evaluators. For reproducibility, we demonstrate similar benefits on the publicly available AMI dataset. Our results speak to the benefits of structuring summaries into sections and annotating supporting evidence when constructing summarization corpora.", "author": "Kundan Krishna; Sopan Khosla; Jeffrey Bigham; Zachary C. Lipton", "authorids": "/k/kundan-krishna/; /s/sopan-khosla/; /j/jeffrey-p-bigham/; /z/zachary-c-lipton/", "bibtex": "@inproceedings{krishna-etal-2021-generating,\n title = \"Generating {SOAP} Notes from Doctor-Patient Conversations Using Modular Summarization Techniques\",\n author = \"Krishna, Kundan and\n Khosla, Sopan and\n Bigham, Jeffrey and\n Lipton, Zachary C.\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.384/\",\n doi = \"10.18653/v1/2021.acl-long.384\",\n pages = \"4958--4972\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.384.pdf", "site": "https://aclanthology.org/2021.acl-long.384/", "pdf_size": 910361, "gs_citation": 132, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=15731636341071601564&as_sdt=5,33&sciodt=0,33&hl=en", "gs_version_total": 6, "aff": "Carnegie Mellon University; Carnegie Mellon University; Carnegie Mellon University; Carnegie Mellon University", "aff_domain": "andrew.cmu.edu;andrew.cmu.edu;andrew.cmu.edu;andrew.cmu.edu", "email": "andrew.cmu.edu;andrew.cmu.edu;andrew.cmu.edu;andrew.cmu.edu", "github": "", "project": "http://abridge.com", "author_num": 4, "aff_unique_index": "0;0;0;0", "aff_unique_norm": "Carnegie Mellon University", "aff_unique_dep": "", "aff_unique_url": "https://www.cmu.edu", "aff_unique_abbr": "CMU", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0;0;0;0", "aff_country_unique": "United States" }, { "id": "2021.acl-long.316", "title": "Generation-Augmented Retrieval for Open-Domain Question Answering", "track": "main", "status": "Long", "award": false, "abstract": "We propose Generation-Augmented Retrieval (GAR) for answering open-domain questions, which augments a query through text generation of heuristically discovered relevant contexts without external resources as supervision. We demonstrate that the generated contexts substantially enrich the semantics of the queries and GAR with sparse representations (BM25) achieves comparable or better performance than state-of-the-art dense retrieval methods such as DPR. We show that generating diverse contexts for a query is beneficial as fusing their results consistently yields better retrieval accuracy. Moreover, as sparse and dense representations are often complementary, GAR can be easily combined with DPR to achieve even better performance. GAR achieves state-of-the-art performance on Natural Questions and TriviaQA datasets under the extractive QA setup when equipped with an extractive reader, and consistently outperforms other retrieval methods when the same generative reader is used.", "author": "Yuning Mao; Pengcheng He; Xiaodong Liu; Yelong Shen; Jianfeng Gao; Jiawei Han; Weizhu Chen", "authorids": "/y/yuning-mao/; /p/pengcheng-he/; /x/xiaodong-liu/; /y/yelong-shen/; /j/jianfeng-gao/; /j/jiawei-han/; /w/weizhu-chen/", "bibtex": "@inproceedings{mao-etal-2021-generation,\n title = \"Generation-Augmented Retrieval for Open-Domain Question Answering\",\n author = \"Mao, Yuning and\n He, Pengcheng and\n Liu, Xiaodong and\n Shen, Yelong and\n Gao, Jianfeng and\n Han, Jiawei and\n Chen, Weizhu\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.316/\",\n doi = \"10.18653/v1/2021.acl-long.316\",\n pages = \"4089--4100\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.316.pdf", "site": "https://aclanthology.org/2021.acl-long.316/", "pdf_size": 1961088, "gs_citation": 279, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=7869750377554789353&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 7, "aff": "University of Illinois, Urbana-Champaign; Microsoft Azure AI; Microsoft Research; Microsoft Azure AI; Microsoft Research; University of Illinois, Urbana-Champaign; Microsoft Azure AI", "aff_domain": "illinois.edu;microsoft.com;microsoft.com;microsoft.com;microsoft.com;illinois.edu;microsoft.com", "email": "illinois.edu;microsoft.com;microsoft.com;microsoft.com;microsoft.com;illinois.edu;microsoft.com", "github": "https://github.com/morningmoni/GAR", "project": "", "author_num": 7, "aff_unique_index": "0;1;2;1;2;0;1", "aff_unique_norm": "University of Illinois;Microsoft;Microsoft Corporation", "aff_unique_dep": ";Azure AI;Microsoft Research", "aff_unique_url": "https://illinois.edu;https://azure.microsoft.com/en-us/ai;https://www.microsoft.com/en-us/research", "aff_unique_abbr": "UIUC;Microsoft Azure AI;MSR", "aff_campus_unique_index": "0;0", "aff_campus_unique": "Urbana-Champaign;", "aff_country_unique_index": "0;0;0;0;0;0;0", "aff_country_unique": "United States" }, { "id": "2021.acl-long.509", "title": "GhostBERT: Generate More Features with Cheap Operations for BERT", "track": "main", "status": "Long", "award": false, "abstract": "Transformer-based pre-trained language models like BERT, though powerful in many tasks, are expensive in both memory and computation, due to their large number of parameters. Previous works show that some parameters in these models can be pruned away without severe accuracy drop. However, these redundant features contribute to a comprehensive understanding of the training data and removing them weakens the model\u2019s representation ability. In this paper, we propose GhostBERT, which generates more features with very cheap operations from the remaining features. In this way, GhostBERT has similar memory and computational cost as the pruned model, but enjoys much larger representation power. The proposed ghost module can also be applied to unpruned BERT models to enhance their performance with negligible additional parameters and computation. Empirical results on the GLUE benchmark on three backbone models (i.e., BERT, RoBERTa and ELECTRA) verify the efficacy of our proposed method.", "author": "Zhiqi Huang; Lu Hou; Lifeng Shang; Xin Jiang; Xiao Chen; Qun Liu", "authorids": "/z/zhiqi-huang/; /l/lu-hou/; /l/lifeng-shang/; /x/xin-jiang/; /x/xiao-chen/; /q/qun-liu/", "bibtex": "@inproceedings{huang-etal-2021-ghostbert,\n title = \"{G}host{BERT}: Generate More Features with Cheap Operations for {BERT}\",\n author = \"Huang, Zhiqi and\n Hou, Lu and\n Shang, Lifeng and\n Jiang, Xin and\n Chen, Xiao and\n Liu, Qun\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.509/\",\n doi = \"10.18653/v1/2021.acl-long.509\",\n pages = \"6512--6523\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.509.pdf", "site": "https://aclanthology.org/2021.acl-long.509/", "pdf_size": 3100649, "gs_citation": 26, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=14919401618717975848&as_sdt=40005&sciodt=0,10&hl=en", "gs_version_total": 3, "aff": "Peking University; Huawei Noah\u2019s Ark Lab; Huawei Noah\u2019s Ark Lab; Huawei Noah\u2019s Ark Lab; Huawei Noah\u2019s Ark Lab; Huawei Noah\u2019s Ark Lab", "aff_domain": "pku.edu.cn;huawei.com;huawei.com;huawei.com;huawei.com;huawei.com", "email": "pku.edu.cn;huawei.com;huawei.com;huawei.com;huawei.com;huawei.com", "github": "", "project": "", "author_num": 6, "aff_unique_index": "0;1;1;1;1;1", "aff_unique_norm": "Peking University;Huawei", "aff_unique_dep": ";Noah\u2019s Ark Lab", "aff_unique_url": "http://www.pku.edu.cn;https://www.huawei.com", "aff_unique_abbr": "Peking U;Huawei", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0;0;0;0;0;0", "aff_country_unique": "China" }, { "id": "2021.acl-long.155", "title": "Glancing Transformer for Non-Autoregressive Neural Machine Translation", "track": "main", "status": "Long", "award": false, "abstract": "Recent work on non-autoregressive neural machine translation (NAT) aims at improving the efficiency by parallel decoding without sacrificing the quality. However, existing NAT methods are either inferior to Transformer or require multiple decoding passes, leading to reduced speedup. We propose the Glancing Language Model (GLM) for single-pass parallel generation models. With GLM, we develop Glancing Transformer (GLAT) for machine translation. With only single-pass parallel decoding, GLAT is able to generate high-quality translation with 8\u00d7-15\u00d7 speedup. Note that GLAT does not modify the network architecture, which is a training method to learn word interdependency. Experiments on multiple WMT language directions show that GLAT outperforms all previous single pass non-autoregressive methods, and is nearly comparable to Transformer, reducing the gap to 0.25-0.9 BLEU points.", "author": "Lihua Qian; Hao Zhou; Yu Bao; Mingxuan Wang; Lin Qiu; Weinan Zhang; Yong Yu; Lei Li", "authorids": "/l/lihua-qian/; /h/hao-zhou/; /y/yu-bao/; /m/mingxuan-wang/; /l/lin-qiu/; /w/weinan-zhang/; /y/yong-yu/; /l/lei-li/", "bibtex": "@inproceedings{qian-etal-2021-glancing,\n title = \"Glancing Transformer for Non-Autoregressive Neural Machine Translation\",\n author = \"Qian, Lihua and\n Zhou, Hao and\n Bao, Yu and\n Wang, Mingxuan and\n Qiu, Lin and\n Zhang, Weinan and\n Yu, Yong and\n Li, Lei\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.155/\",\n doi = \"10.18653/v1/2021.acl-long.155\",\n pages = \"1993--2003\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.155.pdf", "site": "https://aclanthology.org/2021.acl-long.155/", "pdf_size": 2418070, "gs_citation": 169, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=10731226358862054964&as_sdt=5,33&sciodt=0,33&hl=en", "gs_version_total": 9, "aff": "Shanghai Jiao Tong University+ByteDance AI Lab; ByteDance AI Lab; Nanjing University; ByteDance AI Lab; Shanghai Jiao Tong University; Shanghai Jiao Tong University; Shanghai Jiao Tong University; ByteDance AI Lab", "aff_domain": "apex.sjtu.edu.cn;bytedance.com;smail.nju.edu.cn;bytedance.com;apex.sjtu.edu.cn;apex.sjtu.edu.cn;apex.sjtu.edu.cn;bytedance.com", "email": "apex.sjtu.edu.cn;bytedance.com;smail.nju.edu.cn;bytedance.com;apex.sjtu.edu.cn;apex.sjtu.edu.cn;apex.sjtu.edu.cn;bytedance.com", "github": "", "project": "", "author_num": 8, "aff_unique_index": "0+1;1;2;1;0;0;0;1", "aff_unique_norm": "Shanghai Jiao Tong University;ByteDance;Nanjing University", "aff_unique_dep": ";AI Lab;", "aff_unique_url": "https://www.sjtu.edu.cn;https://www.bytedance.com;https://www.nju.edu.cn", "aff_unique_abbr": "SJTU;ByteDance;Nanjing U", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0+0;0;0;0;0;0;0;0", "aff_country_unique": "China" }, { "id": "2021.acl-long.480", "title": "Good for Misconceived Reasons: An Empirical Revisiting on the Need for Visual Context in Multimodal Machine Translation", "track": "main", "status": "Long", "award": false, "abstract": "A neural multimodal machine translation (MMT) system is one that aims to perform better translation by extending conventional text-only translation models with multimodal information. Many recent studies report improvements when equipping their models with the multimodal module, despite the controversy of whether such improvements indeed come from the multimodal part. We revisit the contribution of multimodal information in MMT by devising two interpretable MMT models. To our surprise, although our models replicate similar gains as recently developed multimodal-integrated systems achieved, our models learn to ignore the multimodal information. Upon further investigation, we discover that the improvements achieved by the multimodal models over text-only counterparts are in fact results of the regularization effect. We report empirical findings that highlight the importance of MMT models\u2019 interpretability, and discuss how our findings will benefit future research.", "author": "Zhiyong Wu; Lingpeng Kong; Wei Bi; Xiang Li; Ben Kao", "authorids": "/z/zhiyong-wu/; /l/lingpeng-kong/; /w/wei-bi/; /x/xiang-li/; /b/ben-kao/", "bibtex": "@inproceedings{wu-etal-2021-good,\n title = \"Good for Misconceived Reasons: An Empirical Revisiting on the Need for Visual Context in Multimodal Machine Translation\",\n author = \"Wu, Zhiyong and\n Kong, Lingpeng and\n Bi, Wei and\n Li, Xiang and\n Kao, Ben\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.480/\",\n doi = \"10.18653/v1/2021.acl-long.480\",\n pages = \"6153--6166\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.480.pdf", "site": "https://aclanthology.org/2021.acl-long.480/", "pdf_size": 635553, "gs_citation": 36, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=12687474174541161082&as_sdt=5,44&sciodt=0,44&hl=en", "gs_version_total": 6, "aff": "The University of Hong Kong + Shanghai Artificial Intelligence Laboratory; The University of Hong Kong + Tencent AI Lab; Tencent AI Lab; East China Normal University; The University of Hong Kong", "aff_domain": "cs.hku.hk;cs.hku.hk;tencent.com;dase.ecnu.edu.cn;cs.hku.hk", "email": "cs.hku.hk;cs.hku.hk;tencent.com;dase.ecnu.edu.cn;cs.hku.hk", "github": "", "project": "", "author_num": 5, "aff_unique_index": "0+1;0+2;2;3;0", "aff_unique_norm": "The University of Hong Kong;Shanghai Artificial Intelligence Laboratory;Tencent;East China Normal University", "aff_unique_dep": ";;Tencent AI Lab;", "aff_unique_url": "https://www.hku.hk;http://www.shailab.org/;https://ai.tencent.com;http://www.ecnu.edu.cn", "aff_unique_abbr": "HKU;Shanghai AI Lab;Tencent AI Lab;ECNU", "aff_campus_unique_index": ";", "aff_campus_unique": "", "aff_country_unique_index": "0+0;0+0;0;0;0", "aff_country_unique": "China" }, { "id": "2021.acl-long.223", "title": "Guiding Teacher Forcing with Seer Forcing for Neural Machine Translation", "track": "main", "status": "Long", "award": false, "abstract": "Although teacher forcing has become the main training paradigm for neural machine translation, it usually makes predictions only conditioned on past information, and hence lacks global planning for the future. To address this problem, we introduce another decoder, called seer decoder, into the encoder-decoder framework during training, which involves future information in target predictions. Meanwhile, we force the conventional decoder to simulate the behaviors of the seer decoder via knowledge distillation. In this way, at test the conventional decoder can perform like the seer decoder without the attendance of it. Experiment results on the Chinese-English, English-German and English-Romanian translation tasks show our method can outperform competitive baselines significantly and achieves greater improvements on the bigger data sets. Besides, the experiments also prove knowledge distillation the best way to transfer knowledge from the seer decoder to the conventional decoder compared to adversarial learning and L2 regularization.", "author": "Yang Feng; Shuhao Gu; Dengji Guo; Zhengxin Yang; Chenze Shao", "authorids": "/y/yang-feng/; /s/shuhao-gu/; /d/dengji-guo/; /z/zhengxin-yang/; /c/chenze-shao/", "bibtex": "@inproceedings{feng-etal-2021-guiding,\n title = \"Guiding Teacher Forcing with Seer Forcing for Neural Machine Translation\",\n author = \"Feng, Yang and\n Gu, Shuhao and\n Guo, Dengji and\n Yang, Zhengxin and\n Shao, Chenze\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.223/\",\n doi = \"10.18653/v1/2021.acl-long.223\",\n pages = \"2862--2872\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.223.pdf", "site": "https://aclanthology.org/2021.acl-long.223/", "pdf_size": 1385214, "gs_citation": 16, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=6949285758689738756&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 4, "aff": "Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences (ICT/CAS) + University of Chinese Academy of Sciences, Beijing, China; Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences (ICT/CAS) + University of Chinese Academy of Sciences, Beijing, China; Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences (ICT/CAS) + University of Chinese Academy of Sciences, Beijing, China; Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences (ICT/CAS) + University of Chinese Academy of Sciences, Beijing, China; Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences (ICT/CAS) + University of Chinese Academy of Sciences, Beijing, China", "aff_domain": "ict.ac.cn;ict.ac.cn;ict.ac.cn;ict.ac.cn;ict.ac.cn", "email": "ict.ac.cn;ict.ac.cn;ict.ac.cn;ict.ac.cn;ict.ac.cn", "github": "https://github.com/ictnlp/SeerForcingNMT", "project": "", "author_num": 5, "aff_unique_index": "0+1;0+1;0+1;0+1;0+1", "aff_unique_norm": "Chinese Academy of Sciences;University of Chinese Academy of Sciences", "aff_unique_dep": "Institute of Computing Technology;", "aff_unique_url": "http://www.cas.cn;http://www.ucas.ac.cn", "aff_unique_abbr": "CAS;UCAS", "aff_campus_unique_index": "1;1;1;1;1", "aff_campus_unique": ";Beijing", "aff_country_unique_index": "0+0;0+0;0+0;0+0;0+0", "aff_country_unique": "China" }, { "id": "2021.acl-long.465", "title": "Guiding the Growth: Difficulty-Controllable Question Generation through Step-by-Step Rewriting", "track": "main", "status": "Long", "award": false, "abstract": "This paper explores the task of Difficulty-Controllable Question Generation (DCQG), which aims at generating questions with required difficulty levels. Previous research on this task mainly defines the difficulty of a question as whether it can be correctly answered by a Question Answering (QA) system, lacking interpretability and controllability. In our work, we redefine question difficulty as the number of inference steps required to answer it and argue that Question Generation (QG) systems should have stronger control over the logic of generated questions. To this end, we propose a novel framework that progressively increases question difficulty through step-by-step rewriting under the guidance of an extracted reasoning chain. A dataset is automatically constructed to facilitate the research, on which extensive experiments are conducted to test the performance of our method.", "author": "Yi Cheng; Siyao Li; Bang Liu; Ruihui Zhao; Sujian Li; Chenghua Lin; Yefeng Zheng", "authorids": "/y/yi-cheng/; /s/siyao-li/; /b/bang-liu/; /r/ruihui-zhao/; /s/sujian-li/; /c/chenghua-lin/; /y/yefeng-zheng/", "bibtex": "@inproceedings{cheng-etal-2021-guiding,\n title = \"Guiding the Growth: Difficulty-Controllable Question Generation through Step-by-Step Rewriting\",\n author = \"Cheng, Yi and\n Li, Siyao and\n Liu, Bang and\n Zhao, Ruihui and\n Li, Sujian and\n Lin, Chenghua and\n Zheng, Yefeng\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.465/\",\n doi = \"10.18653/v1/2021.acl-long.465\",\n pages = \"5968--5978\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.465.pdf", "site": "https://aclanthology.org/2021.acl-long.465/", "pdf_size": 1182518, "gs_citation": 54, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=6328414841662453496&as_sdt=5,44&sciodt=0,44&hl=en", "gs_version_total": 8, "aff": "Tencent Jarvis Lab, China; LTI, Carnegie Mellon University; RALI & Mila, Universit\u00e9 de Montr\u00e9al; Peking University; The University of Sheffield; Tencent Jarvis Lab, China; Tencent Jarvis Lab, China", "aff_domain": "tencent.com;andrew.cmu.edu;umontreal.ca;tencent.com;pku.edu.cn;shef.ac.uk;tencent.com", "email": "tencent.com;andrew.cmu.edu;umontreal.ca;tencent.com;pku.edu.cn;shef.ac.uk;tencent.com", "github": "", "project": "", "author_num": 7, "aff_unique_index": "0;1;2;3;4;0;0", "aff_unique_norm": "Tencent;Carnegie Mellon University;Universit\u00e9 de Montr\u00e9al;Peking University;University of Sheffield", "aff_unique_dep": "Jarvis Lab;Language Technologies Institute;RALI & Mila;;", "aff_unique_url": "https://www.tencent.com;https://www.cmu.edu;https://www.umontreal.ca;http://www.pku.edu.cn;https://www.sheffield.ac.uk", "aff_unique_abbr": "Tencent;CMU;UdeM;Peking U;Sheffield", "aff_campus_unique_index": "1", "aff_campus_unique": ";Montr\u00e9al", "aff_country_unique_index": "0;1;2;0;3;0;0", "aff_country_unique": "China;United States;Canada;United Kingdom" }, { "id": "2021.acl-long.294", "title": "H-Transformer-1D: Fast One-Dimensional Hierarchical Attention for Sequences", "track": "main", "status": "Long", "award": false, "abstract": "We describe an efficient hierarchical method to compute attention in the Transformer architecture. The proposed attention mechanism exploits a matrix structure similar to the Hierarchical Matrix (H-Matrix) developed by the numerical analysis community, and has linear run time and memory complexity. We perform extensive experiments to show that the inductive bias embodied by our hierarchical attention is effective in capturing the hierarchical structure in the sequences typical for natural language and vision tasks. Our method is superior to alternative sub-quadratic proposals by over +6 points on average on the Long Range Arena benchmark. It also sets a new SOTA test perplexity on One-Billion Word dataset with 5x fewer model parameters than that of the previous-best Transformer-based models.", "author": "Zhenhai Zhu; Radu Soricut", "authorids": "/z/zhenhai-zhu/; /r/radu-soricut/", "bibtex": "@inproceedings{zhu-soricut-2021-h,\n title = \"{H}-Transformer-1{D}: Fast One-Dimensional Hierarchical Attention for Sequences\",\n author = \"Zhu, Zhenhai and\n Soricut, Radu\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.294/\",\n doi = \"10.18653/v1/2021.acl-long.294\",\n pages = \"3801--3815\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.294.pdf", "site": "https://aclanthology.org/2021.acl-long.294/", "pdf_size": 430506, "gs_citation": 47, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=5796586367798778473&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 4, "aff": "Google Research; Google Research", "aff_domain": "google.com;google.com", "email": "google.com;google.com", "github": "", "project": "", "author_num": 2, "aff_unique_index": "0;0", "aff_unique_norm": "Google", "aff_unique_dep": "Google Research", "aff_unique_url": "https://research.google", "aff_unique_abbr": "Google Research", "aff_campus_unique_index": "0;0", "aff_campus_unique": "Mountain View", "aff_country_unique_index": "0;0", "aff_country_unique": "United States" }, { "id": "2021.acl-long.283", "title": "HERALD: An Annotation Efficient Method to Detect User Disengagement in Social Conversations", "track": "main", "status": "Long", "award": false, "abstract": "Open-domain dialog systems have a user-centric goal: to provide humans with an engaging conversation experience. User engagement is one of the most important metrics for evaluating open-domain dialog systems, and could also be used as real-time feedback to benefit dialog policy learning. Existing work on detecting user disengagement typically requires hand-labeling many dialog samples. We propose HERALD, an efficient annotation framework that reframes the training data annotation process as a denoising problem. Specifically, instead of manually labeling training samples, we first use a set of labeling heuristics to label training samples automatically. We then denoise the weakly labeled data using the Shapley algorithm. Finally, we use the denoised data to train a user engagement detector. Our experiments show that HERALD improves annotation efficiency significantly and achieves 86% user disengagement detection accuracy in two dialog corpora.", "author": "Weixin Liang; Kai-Hui Liang; Zhou Yu", "authorids": "/w/weixin-liang/; /k/kai-hui-liang/; /z/zhou-yu/", "bibtex": "@inproceedings{liang-etal-2021-herald,\n title = \"{HERALD}: An Annotation Efficient Method to Detect User Disengagement in Social Conversations\",\n author = \"Liang, Weixin and\n Liang, Kai-Hui and\n Yu, Zhou\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.283/\",\n doi = \"10.18653/v1/2021.acl-long.283\",\n pages = \"3652--3665\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.283.pdf", "site": "https://aclanthology.org/2021.acl-long.283/", "pdf_size": 962406, "gs_citation": 17, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=9487644200171154563&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 7, "aff": "Stanford University; Columbia University; Columbia University", "aff_domain": "stanford.edu;columbia.edu;columbia.edu", "email": "stanford.edu;columbia.edu;columbia.edu", "github": "https://github.com/Weixin-Liang/HERALD/", "project": "", "author_num": 3, "aff_unique_index": "0;1;1", "aff_unique_norm": "Stanford University;Columbia University", "aff_unique_dep": ";", "aff_unique_url": "https://www.stanford.edu;https://www.columbia.edu", "aff_unique_abbr": "Stanford;Columbia", "aff_campus_unique_index": "0", "aff_campus_unique": "Stanford;", "aff_country_unique_index": "0;0;0", "aff_country_unique": "United States" }, { "id": "2021.acl-long.312", "title": "Handling Extreme Class Imbalance in Technical Logbook Datasets", "track": "main", "status": "Long", "award": false, "abstract": "Technical logbooks are a challenging and under-explored text type in automated event identification. These texts are typically short and written in non-standard yet technical language, posing challenges to off-the-shelf NLP pipelines. The granularity of issue types described in these datasets additionally leads to class imbalance, making it challenging for models to accurately predict which issue each logbook entry describes. In this paper we focus on the problem of technical issue classification by considering logbook datasets from the automotive, aviation, and facilities maintenance domains. We adapt a feedback strategy from computer vision for handling extreme class imbalance, which resamples the training data based on its error in the prediction process. Our experiments show that with statistical significance this feedback strategy provides the best results for four different neural network models trained across a suite of seven different technical logbook datasets from distinct technical domains. The feedback strategy is also generic and could be applied to any learning problem with substantial class imbalances.", "author": "Farhad Akhbardeh; Cecilia Ovesdotter Alm; Marcos Zampieri; Travis Desell", "authorids": "/f/farhad-akhbardeh/; /c/cecilia-ovesdotter-alm/; /m/marcos-zampieri/; /t/travis-desell/", "bibtex": "@inproceedings{akhbardeh-etal-2021-handling,\n title = \"Handling Extreme Class Imbalance in Technical Logbook Datasets\",\n author = \"Akhbardeh, Farhad and\n Alm, Cecilia Ovesdotter and\n Zampieri, Marcos and\n Desell, Travis\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.312/\",\n doi = \"10.18653/v1/2021.acl-long.312\",\n pages = \"4034--4045\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.312.pdf", "site": "https://aclanthology.org/2021.acl-long.312/", "pdf_size": 321789, "gs_citation": 14, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=10755049744210981068&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 4, "aff": "Rochester Institute of Technology; Rochester Institute of Technology; Rochester Institute of Technology; Rochester Institute of Technology", "aff_domain": "rit.edu;rit.edu;rit.edu;rit.edu", "email": "rit.edu;rit.edu;rit.edu;rit.edu", "github": "", "project": "", "author_num": 4, "aff_unique_index": "0;0;0;0", "aff_unique_norm": "Rochester Institute of Technology", "aff_unique_dep": "", "aff_unique_url": "https://www.rit.edu", "aff_unique_abbr": "RIT", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0;0;0;0", "aff_country_unique": "United States" }, { "id": "2021.acl-short.50", "title": "Happy Dance, Slow Clap: Using Reaction GIFs to Predict Induced Affect on Twitter", "track": "main", "status": "Short", "award": false, "abstract": "Datasets with induced emotion labels are scarce but of utmost importance for many NLP tasks. We present a new, automated method for collecting texts along with their induced reaction labels. The method exploits the online use of reaction GIFs, which capture complex affective states. We show how to augment the data with induced emotion and induced sentiment labels. We use our method to create and publish ReactionGIF, a first-of-its-kind affective dataset of 30K tweets. We provide baselines for three new tasks, including induced sentiment prediction and multilabel classification of induced emotions. Our method and dataset open new research opportunities in emotion detection and affective computing.", "author": "Boaz Shmueli; Soumya Ray; Lun-Wei Ku", "authorids": "/b/boaz-shmueli/; /s/soumya-ray/; /l/lun-wei-ku/", "bibtex": "@inproceedings{shmueli-etal-2021-happy,\n title = \"Happy Dance, Slow Clap: {Using} Reaction {GIFs} to Predict Induced Affect on {Twitter}\",\n author = \"Shmueli, Boaz and\n Ray, Soumya and\n Ku, Lun-Wei\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-short.50/\",\n doi = \"10.18653/v1/2021.acl-short.50\",\n pages = \"395--401\"\n}", "pdf": "https://aclanthology.org/2021.acl-short.50.pdf", "site": "https://aclanthology.org/2021.acl-short.50/", "pdf_size": 1894475, "gs_citation": 4, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=7807419948210733768&as_sdt=4005&sciodt=0,6&hl=en", "gs_version_total": 8, "aff": "Social Networks and Human-Centered Computing, TIGP, Academia Sinica + Institute of Service Science, National Tsing Hua University + Institute of Information Science, Academia Sinica; Institute of Service Science, National Tsing Hua University; Institute of Information Science, Academia Sinica", "aff_domain": "iis.sinica.edu.tw;iss.nthu.edu.tw;iis.sinica.edu.tw", "email": "iis.sinica.edu.tw;iss.nthu.edu.tw;iis.sinica.edu.tw", "github": "github.com/bshmueli/ReactionGIF", "project": "", "author_num": 3, "aff_unique_index": "0+1+0;1;0", "aff_unique_norm": "Academia Sinica;National Tsing Hua University", "aff_unique_dep": "Social Networks and Human-Centered Computing, TIGP;Institute of Service Science", "aff_unique_url": "https://www.sinica.edu.tw;https://www.nthu.edu.tw", "aff_unique_abbr": "Academia Sinica;NTHU", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0+0+0;0;0", "aff_country_unique": "Taiwan, China" }, { "id": "2021.acl-long.556", "title": "Hate Speech Detection Based on Sentiment Knowledge Sharing", "track": "main", "status": "Long", "award": false, "abstract": "The wanton spread of hate speech on the internet brings great harm to society and families. It is urgent to establish and improve automatic detection and active avoidance mechanisms for hate speech. While there exist methods for hate speech detection, they stereotype words and hence suffer from inherently biased training. In other words, getting more affective features from other affective resources will significantly affect the performance of hate speech detection. In this paper, we propose a hate speech detection framework based on sentiment knowledge sharing. While extracting the affective features of the target sentence itself, we make better use of the sentiment features from external resources, and finally fuse features from different feature extraction units to detect hate speech. Experimental results on two public datasets demonstrate the effectiveness of our model.", "author": "Xianbing Zhou; Yang Yong; Xiaochao Fan; Ge Ren; Yunfeng Song; Yufeng Diao; Liang Yang; Hongfei Lin", "authorids": "/x/xianbing-zhou/; /y/yang-yong/; /x/xiaochao-fan/; /g/ge-ren/; /y/yunfeng-song/; /y/yufeng-diao/; /l/liang-yang/; /h/hongfei-lin/", "bibtex": "@inproceedings{zhou-etal-2021-hate,\n title = \"Hate Speech Detection Based on Sentiment Knowledge Sharing\",\n author = \"Zhou, Xianbing and\n Yong, Yang and\n Fan, Xiaochao and\n Ren, Ge and\n Song, Yunfeng and\n Diao, Yufeng and\n Yang, Liang and\n Lin, Hongfei\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.556/\",\n doi = \"10.18653/v1/2021.acl-long.556\",\n pages = \"7158--7166\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.556.pdf", "site": "https://aclanthology.org/2021.acl-long.556/", "pdf_size": 378277, "gs_citation": 48, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=13562048273332453907&as_sdt=5,33&sciodt=0,33&hl=en", "gs_version_total": 3, "aff": "School of Computer Science and Technology, Xinjiang Normal University, China; School of Computer Science and Technology, Xinjiang Normal University, China; School of Computer Science and Technology, Xinjiang Normal University, China; School of Computer Science and Technology, Xinjiang Normal University, China; School of Computer Science and Technology, Xinjiang Normal University, China; Department of Computer Science and Technology, Dalian University of Technology, China; Department of Computer Science and Technology, Dalian University of Technology, China; Department of Computer Science and Technology, Dalian University of Technology, China", "aff_domain": "qq.com;qq.com;qq.com;qq.com;qq.com;mail.dlut.edu.cn;mail.dlut.edu.cn;dlut.edu.cn", "email": "qq.com;qq.com;qq.com;qq.com;qq.com;mail.dlut.edu.cn;mail.dlut.edu.cn;dlut.edu.cn", "github": "", "project": "", "author_num": 8, "aff_unique_index": "0;0;0;0;0;1;1;1", "aff_unique_norm": "Xinjiang Normal University;Dalian University of Technology", "aff_unique_dep": "School of Computer Science and Technology;Department of Computer Science and Technology", "aff_unique_url": ";http://en.dlut.edu.cn/", "aff_unique_abbr": ";DUT", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0;0;0;0;0;0;0;0", "aff_country_unique": "China" }, { "id": "2021.acl-long.4", "title": "HateCheck: Functional Tests for Hate Speech Detection Models", "track": "main", "status": "Long", "award": false, "abstract": "Detecting online hate is a difficult task that even state-of-the-art models struggle with. Typically, hate speech detection models are evaluated by measuring their performance on held-out test data using metrics such as accuracy and F1 score. However, this approach makes it difficult to identify specific model weak points. It also risks overestimating generalisable model performance due to increasingly well-evidenced systematic gaps and biases in hate speech datasets. To enable more targeted diagnostic insights, we introduce HateCheck, a suite of functional tests for hate speech detection models. We specify 29 model functionalities motivated by a review of previous research and a series of interviews with civil society stakeholders. We craft test cases for each functionality and validate their quality through a structured annotation process. To illustrate HateCheck\u2019s utility, we test near-state-of-the-art transformer models as well as two popular commercial models, revealing critical model weaknesses.", "author": "Paul R\u00f6ttger; Bertie Vidgen; Dong Nguyen; Zeerak Waseem; Helen Margetts; Janet Pierrehumbert", "authorids": "/p/paul-rottger/; /b/bertie-vidgen/; /d/dong-nguyen/; /z/zeerak-talat/; /h/helen-margetts/; /j/janet-pierrehumbert/", "bibtex": "@inproceedings{rottger-etal-2021-hatecheck,\n title = \"{H}ate{C}heck: Functional Tests for Hate Speech Detection Models\",\n author = {R{\\\"o}ttger, Paul and\n Vidgen, Bertie and\n Nguyen, Dong and\n Waseem, Zeerak and\n Margetts, Helen and\n Pierrehumbert, Janet},\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.4/\",\n doi = \"10.18653/v1/2021.acl-long.4\",\n pages = \"41--58\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.4.pdf", "site": "https://aclanthology.org/2021.acl-long.4/", "pdf_size": 355693, "gs_citation": 287, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=7387622423207082201&as_sdt=5,33&sciodt=0,33&hl=en", "gs_version_total": 8, "aff": "University of Oxford + The Alan Turing Institute; University of Oxford + The Alan Turing Institute; Utrecht University; University of Sheffield; University of Oxford + The Alan Turing Institute; University of Oxford", "aff_domain": "ox.ac.uk;turing.ac.uk;uu.nl;sheffield.ac.uk;oii.ox.ac.uk;cs.ox.ac.uk", "email": "ox.ac.uk;turing.ac.uk;uu.nl;sheffield.ac.uk;oii.ox.ac.uk;cs.ox.ac.uk", "github": "https://github.com/thunlp/MetaAdaptRank", "project": "", "author_num": 6, "aff_unique_index": "0+1;0+1;2;3;0+1;0", "aff_unique_norm": "University of Oxford;The Alan Turing Institute;Utrecht University;University of Sheffield", "aff_unique_dep": ";;;", "aff_unique_url": "https://www.ox.ac.uk;https://www.turing.ac.uk;https://www.uu.nl;https://www.sheffield.ac.uk", "aff_unique_abbr": "Oxford;ATI;UU;Sheffield", "aff_campus_unique_index": ";;", "aff_campus_unique": "", "aff_country_unique_index": "0+0;0+0;1;0;0+0;0", "aff_country_unique": "United Kingdom;Netherlands" }, { "id": "2021.acl-short.107", "title": "Hi-Transformer: Hierarchical Interactive Transformer for Efficient and Effective Long Document Modeling", "track": "main", "status": "Short", "award": false, "abstract": "Transformer is important for text modeling. However, it has difficulty in handling long documents due to the quadratic complexity with input text length. In order to handle this problem, we propose a hierarchical interactive Transformer (Hi-Transformer) for efficient and effective long document modeling. Hi-Transformer models documents in a hierarchical way, i.e., first learns sentence representations and then learns document representations. It can effectively reduce the complexity and meanwhile capture global document context in the modeling of each sentence. More specifically, we first use a sentence Transformer to learn the representations of each sentence. Then we use a document Transformer to model the global document context from these sentence representations. Next, we use another sentence Transformer to enhance sentence modeling using the global document context. Finally, we use hierarchical pooling method to obtain document embedding. Extensive experiments on three benchmark datasets validate the efficiency and effectiveness of Hi-Transformer in long document modeling.", "author": "Chuhan Wu; Fangzhao Wu; Tao Qi; Yongfeng Huang", "authorids": "/c/chuhan-wu/; /f/fangzhao-wu/; /t/tao-qi/; /y/yongfeng-huang/", "bibtex": "@inproceedings{wu-etal-2021-hi,\n title = \"Hi-Transformer: Hierarchical Interactive Transformer for Efficient and Effective Long Document Modeling\",\n author = \"Wu, Chuhan and\n Wu, Fangzhao and\n Qi, Tao and\n Huang, Yongfeng\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-short.107/\",\n doi = \"10.18653/v1/2021.acl-short.107\",\n pages = \"848--853\"\n}", "pdf": "https://aclanthology.org/2021.acl-short.107.pdf", "site": "https://aclanthology.org/2021.acl-short.107/", "pdf_size": 704466, "gs_citation": 76, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=868235387738103606&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 4, "aff": "Department of Electronic Engineering & BNRist, Tsinghua University, Beijing 100084, China; Microsoft Research Asia, Beijing 100080, China; Department of Electronic Engineering & BNRist, Tsinghua University, Beijing 100084, China; Department of Electronic Engineering & BNRist, Tsinghua University, Beijing 100084, China", "aff_domain": "gmail.com;gmail.com;gmail.com;tsinghua.edu.cn", "email": "gmail.com;gmail.com;gmail.com;tsinghua.edu.cn", "github": "https://github.com/wuch15/HiTransformer", "project": "", "author_num": 4, "aff_unique_index": "0;1;0;0", "aff_unique_norm": "Tsinghua University;Microsoft Research Asia", "aff_unique_dep": "Department of Electronic Engineering;Research", "aff_unique_url": "https://www.tsinghua.edu.cn;https://www.microsoft.com/en-us/research/group/asia", "aff_unique_abbr": "THU;MSRA", "aff_campus_unique_index": "0;0;0;0", "aff_campus_unique": "Beijing", "aff_country_unique_index": "0;0;0;0", "aff_country_unique": "China" }, { "id": "2021.acl-long.37", "title": "Hidden Killer: Invisible Textual Backdoor Attacks with Syntactic Trigger", "track": "main", "status": "Long", "award": false, "abstract": "Backdoor attacks are a kind of insidious security threat against machine learning models. After being injected with a backdoor in training, the victim model will produce adversary-specified outputs on the inputs embedded with predesigned triggers but behave properly on normal inputs during inference. As a sort of emergent attack, backdoor attacks in natural language processing (NLP) are investigated insufficiently. As far as we know, almost all existing textual backdoor attack methods insert additional contents into normal samples as triggers, which causes the trigger-embedded samples to be detected and the backdoor attacks to be blocked without much effort. In this paper, we propose to use the syntactic structure as the trigger in textual backdoor attacks. We conduct extensive experiments to demonstrate that the syntactic trigger-based attack method can achieve comparable attack performance (almost 100% success rate) to the insertion-based methods but possesses much higher invisibility and stronger resistance to defenses. These results also reveal the significant insidiousness and harmfulness of textual backdoor attacks. All the code and data of this paper can be obtained at https://github.com/thunlp/HiddenKiller.", "author": "Fanchao Qi; Mukai Li; Yangyi Chen; Zhengyan Zhang; Zhiyuan Liu; Yasheng Wang; Maosong Sun", "authorids": "/f/fanchao-qi/; /m/mukai-li/; /y/yangyi-chen/; /z/zhengyan-zhang/; /z/zhiyuan-liu/; /y/yasheng-wang/; /m/maosong-sun/", "bibtex": "@inproceedings{qi-etal-2021-hidden,\n title = \"Hidden Killer: Invisible Textual Backdoor Attacks with Syntactic Trigger\",\n author = \"Qi, Fanchao and\n Li, Mukai and\n Chen, Yangyi and\n Zhang, Zhengyan and\n Liu, Zhiyuan and\n Wang, Yasheng and\n Sun, Maosong\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.37/\",\n doi = \"10.18653/v1/2021.acl-long.37\",\n pages = \"443--453\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.37.pdf", "site": "https://aclanthology.org/2021.acl-long.37/", "pdf_size": 391715, "gs_citation": 249, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=15834382760866540239&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 5, "aff": "Department of Computer Science and Technology, Tsinghua University, Beijing, China+Beijing National Research Center for Information Science and Technology+Institute for Artificial Intelligence, Tsinghua University, Beijing, China; Beijing National Research Center for Information Science and Technology+Beihang University; Beijing National Research Center for Information Science and Technology+Huazhong University of Science and Technology; Department of Computer Science and Technology, Tsinghua University, Beijing, China+Beijing National Research Center for Information Science and Technology+Institute for Artificial Intelligence, Tsinghua University, Beijing, China; Department of Computer Science and Technology, Tsinghua University, Beijing, China+Beijing National Research Center for Information Science and Technology+Institute for Artificial Intelligence, Tsinghua University, Beijing, China; Huawei Noah\u2019s Ark Lab; Department of Computer Science and Technology, Tsinghua University, Beijing, China+Beijing National Research Center for Information Science and Technology+Institute for Artificial Intelligence, Tsinghua University, Beijing, China", "aff_domain": "mails.tsinghua.edu.cn; ; ; ; ; ;tsinghua.edu.cn", "email": "mails.tsinghua.edu.cn; ; ; ; ; ;tsinghua.edu.cn", "github": "https://github.com/thunlp/HiddenKiller", "project": "", "author_num": 7, "aff_unique_index": "0+1+0;1+2;1+3;0+1+0;0+1+0;4;0+1+0", "aff_unique_norm": "Tsinghua University;Beijing National Research Center for Information Science and Technology;Beihang University;Huazhong University of Science and Technology;Huawei", "aff_unique_dep": "Department of Computer Science and Technology;;;;Noah\u2019s Ark Lab", "aff_unique_url": "https://www.tsinghua.edu.cn;;http://www.buaa.edu.cn/;http://www.hust.edu.cn;https://www.huawei.com", "aff_unique_abbr": "THU;;BUAA;HUST;Huawei", "aff_campus_unique_index": "0+0;;;0+0;0+0;0+0", "aff_campus_unique": "Beijing;", "aff_country_unique_index": "0+0+0;0+0;0+0;0+0+0;0+0+0;0;0+0+0", "aff_country_unique": "China" }, { "id": "2021.acl-long.338", "title": "HiddenCut: Simple Data Augmentation for Natural Language Understanding with Better Generalizability", "track": "main", "status": "Long", "award": false, "abstract": "Fine-tuning large pre-trained models with task-specific data has achieved great success in NLP. However, it has been demonstrated that the majority of information within the self-attention networks is redundant and not utilized effectively during the fine-tuning stage. This leads to inferior results when generalizing the obtained models to out-of-domain distributions. To this end, we propose a simple yet effective data augmentation technique, HiddenCut, to better regularize the model and encourage it to learn more generalizable features. Specifically, contiguous spans within the hidden space are dynamically and strategically dropped during training. Experiments show that our HiddenCut method outperforms the state-of-the-art augmentation methods on the GLUE benchmark, and consistently exhibits superior generalization performances on out-of-distribution and challenging counterexamples. We have publicly released our code at https://github.com/GT-SALT/HiddenCut.", "author": "Jiaao Chen; Dinghan Shen; Weizhu Chen; Diyi Yang", "authorids": "/j/jiaao-chen/; /d/dinghan-shen/; /w/weizhu-chen/; /d/diyi-yang/", "bibtex": "@inproceedings{chen-etal-2021-hiddencut,\n title = \"{H}idden{C}ut: Simple Data Augmentation for Natural Language Understanding with Better Generalizability\",\n author = \"Chen, Jiaao and\n Shen, Dinghan and\n Chen, Weizhu and\n Yang, Diyi\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.338/\",\n doi = \"10.18653/v1/2021.acl-long.338\",\n pages = \"4380--4390\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.338.pdf", "site": "https://aclanthology.org/2021.acl-long.338/", "pdf_size": 409951, "gs_citation": 47, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=11864006011193614546&as_sdt=5,44&sciodt=0,44&hl=en", "gs_version_total": 0, "aff": "Georgia Institute of Technology; Georgia Institute of Technology + Microsoft Dynamics 365 AI; Microsoft Dynamics 365 AI; Georgia Institute of Technology", "aff_domain": "gatech.edu;microsoft.com;microsoft.com;gatech.edu", "email": "gatech.edu;microsoft.com;microsoft.com;gatech.edu", "github": "https://github.com/GT-SALT/HiddenCut", "project": "", "author_num": 4, "aff_unique_index": "0;0+1;1;0", "aff_unique_norm": "Georgia Institute of Technology;Microsoft", "aff_unique_dep": ";Microsoft Dynamics 365 AI", "aff_unique_url": "https://www.gatech.edu;https://www.microsoft.com/en-us/dynamics/365", "aff_unique_abbr": "Georgia Tech;Microsoft", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0;0+0;0;0", "aff_country_unique": "United States" }, { "id": "2021.acl-long.423", "title": "HieRec: Hierarchical User Interest Modeling for Personalized News Recommendation", "track": "main", "status": "Long", "award": false, "abstract": "User interest modeling is critical for personalized news recommendation. Existing news recommendation methods usually learn a single user embedding for each user from their previous behaviors to represent their overall interest. However, user interest is usually diverse and multi-grained, which is difficult to be accurately modeled by a single user embedding. In this paper, we propose a news recommendation method with hierarchical user interest modeling, named HieRec. Instead of a single user embedding, in our method each user is represented in a hierarchical interest tree to better capture their diverse and multi-grained interest in news. We use a three-level hierarchy to represent 1) overall user interest; 2) user interest in coarse-grained topics like sports; and 3) user interest in fine-grained topics like football. Moreover, we propose a hierarchical user interest matching framework to match candidate news with different levels of user interest for more accurate user interest targeting. Extensive experiments on two real-world datasets validate our method can effectively improve the performance of user modeling for personalized news recommendation.", "author": "Tao Qi; Fangzhao Wu; Chuhan Wu; Peiru Yang; Yang Yu; Xing Xie; Yongfeng Huang", "authorids": "/t/tao-qi/; /f/fangzhao-wu/; /c/chuhan-wu/; /p/peiru-yang/; /y/yang-yu/; /x/xing-xie/; /y/yongfeng-huang/", "bibtex": "@inproceedings{qi-etal-2021-hierec,\n title = \"{H}ie{R}ec: Hierarchical User Interest Modeling for Personalized News Recommendation\",\n author = \"Qi, Tao and\n Wu, Fangzhao and\n Wu, Chuhan and\n Yang, Peiru and\n Yu, Yang and\n Xie, Xing and\n Huang, Yongfeng\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.423/\",\n doi = \"10.18653/v1/2021.acl-long.423\",\n pages = \"5446--5456\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.423.pdf", "site": "https://aclanthology.org/2021.acl-long.423/", "pdf_size": 2273123, "gs_citation": 99, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=6517299847065301220&as_sdt=5,33&sciodt=0,33&hl=en", "gs_version_total": 6, "aff": "Department of Electronic Engineering & BNRist, Tsinghua University; Microsoft Research Asia; Department of Electronic Engineering & BNRist, Tsinghua University; Department of Electronic Engineering & BNRist, Tsinghua University; Microsoft Research Asia; Microsoft Research Asia; Department of Electronic Engineering & BNRist, Tsinghua University", "aff_domain": "gmail.com;gmail.com;gmail.com;gmail.com;microsoft.com;microsoft.com;mail.tsinghua.edu.cn", "email": "gmail.com;gmail.com;gmail.com;gmail.com;microsoft.com;microsoft.com;mail.tsinghua.edu.cn", "github": "", "project": "", "author_num": 7, "aff_unique_index": "0;1;0;0;1;1;0", "aff_unique_norm": "Tsinghua University;Microsoft Research", "aff_unique_dep": "Department of Electronic Engineering;Research", "aff_unique_url": "https://www.tsinghua.edu.cn;https://www.microsoft.com/en-us/research/group/asia", "aff_unique_abbr": "THU;MSR Asia", "aff_campus_unique_index": "1;1;1", "aff_campus_unique": ";Asia", "aff_country_unique_index": "0;0;0;0;0;0;0", "aff_country_unique": "China" }, { "id": "2021.acl-long.156", "title": "Hierarchical Context-aware Network for Dense Video Event Captioning", "track": "main", "status": "Long", "award": false, "abstract": "Dense video event captioning aims to generate a sequence of descriptive captions for each event in a long untrimmed video. Video-level context provides important information and facilities the model to generate consistent and less redundant captions between events. In this paper, we introduce a novel Hierarchical Context-aware Network for dense video event captioning (HCN) to capture context from various aspects. In detail, the model leverages local and global context with different mechanisms to jointly learn to generate coherent captions. The local context module performs full interaction between neighbor frames and the global context module selectively attends to previous or future events. According to our extensive experiment on both Youcook2 and Activitynet Captioning datasets, the video-level HCN model outperforms the event-level context-agnostic model by a large margin. The code is available at https://github.com/KirkGuo/HCN.", "author": "Lei Ji; Xianglin Guo; Haoyang Huang; Xilin Chen", "authorids": "/l/lei-ji/; /x/xianglin-guo/; /h/haoyang-huang/; /x/xilin-chen/", "bibtex": "@inproceedings{ji-etal-2021-hierarchical,\n title = \"Hierarchical Context-aware Network for Dense Video Event Captioning\",\n author = \"Ji, Lei and\n Guo, Xianglin and\n Huang, Haoyang and\n Chen, Xilin\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.156/\",\n doi = \"10.18653/v1/2021.acl-long.156\",\n pages = \"2004--2013\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.156.pdf", "site": "https://aclanthology.org/2021.acl-long.156/", "pdf_size": 1153186, "gs_citation": 14, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=2707519283316322900&as_sdt=5,44&sciodt=0,44&hl=en", "gs_version_total": 2, "aff": "Institute of Computing Technology, CAS, Beijing, China + University of Chinese Academy of Sciences, Beijing, China + Microsoft Research Asia, Beijing, China; New York University, New York, USA + Microsoft Research Asia, Beijing, China; Microsoft Research Asia, Beijing, China; Institute of Computing Technology, CAS, Beijing, China + University of Chinese Academy of Sciences, Beijing, China", "aff_domain": "microsoft.com;nyu.edu;microsoft.com;ict.ac.cn", "email": "microsoft.com;nyu.edu;microsoft.com;ict.ac.cn", "github": "https://github.com/KirkGuo/HCN", "project": "", "author_num": 4, "aff_unique_index": "0+1+2;3+2;2;0+1", "aff_unique_norm": "Chinese Academy of Sciences;University of Chinese Academy of Sciences;Microsoft Research Asia;New York University", "aff_unique_dep": "Institute of Computing Technology;;Research;", "aff_unique_url": "http://www.ict.cas.cn;http://www.ucas.ac.cn;https://www.microsoft.com/en-us/research/group/asia;https://www.nyu.edu", "aff_unique_abbr": "CAS;UCAS;MSRA;NYU", "aff_campus_unique_index": "0+0+0;1+0;0;0+0", "aff_campus_unique": "Beijing;New York", "aff_country_unique_index": "0+0+0;1+0;0;0+0", "aff_country_unique": "China;United States" }, { "id": "2021.acl-long.337", "title": "Hierarchy-aware Label Semantics Matching Network for Hierarchical Text Classification", "track": "main", "status": "Long", "award": false, "abstract": "Hierarchical text classification is an important yet challenging task due to the complex structure of the label hierarchy. Existing methods ignore the semantic relationship between text and labels, so they cannot make full use of the hierarchical information. To this end, we formulate the text-label semantics relationship as a semantic matching problem and thus propose a hierarchy-aware label semantics matching network (HiMatch). First, we project text semantics and label semantics into a joint embedding space. We then introduce a joint embedding loss and a matching learning loss to model the matching relationship between the text semantics and the label semantics. Our model captures the text-label semantics matching relationship among coarse-grained labels and fine-grained labels in a hierarchy-aware manner. The experimental results on various benchmark datasets verify that our model achieves state-of-the-art results.", "author": "Haibin Chen; Qianli Ma; Zhenxi Lin; Jiangyue Yan", "authorids": "/h/haibin-chen/; /q/qianli-ma/; /z/zhenxi-lin/; /j/jiangyue-yan/", "bibtex": "@inproceedings{chen-etal-2021-hierarchy,\n title = \"Hierarchy-aware Label Semantics Matching Network for Hierarchical Text Classification\",\n author = \"Chen, Haibin and\n Ma, Qianli and\n Lin, Zhenxi and\n Yan, Jiangyue\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.337/\",\n doi = \"10.18653/v1/2021.acl-long.337\",\n pages = \"4370--4379\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.337.pdf", "site": "https://aclanthology.org/2021.acl-long.337/", "pdf_size": 1875074, "gs_citation": 127, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=5090122149340745434&as_sdt=400005&sciodt=0,14&hl=en", "gs_version_total": 3, "aff": "School of Computer Science and Engineering, South China University of Technology, Guangzhou, China; School of Computer Science and Engineering, South China University of Technology, Guangzhou, China; School of Computer Science and Engineering, South China University of Technology, Guangzhou, China; School of Computer Science and Engineering, South China University of Technology, Guangzhou, China", "aff_domain": "foxmail.com;scut.edu.cn; ; ", "email": "foxmail.com;scut.edu.cn; ; ", "github": "", "project": "", "author_num": 4, "aff_unique_index": "0;0;0;0", "aff_unique_norm": "South China University of Technology", "aff_unique_dep": "School of Computer Science and Engineering", "aff_unique_url": "https://www.scut.edu.cn", "aff_unique_abbr": "SCUT", "aff_campus_unique_index": "0;0;0;0", "aff_campus_unique": "Guangzhou", "aff_country_unique_index": "0;0;0;0", "aff_country_unique": "China" }, { "id": "2021.acl-short.32", "title": "Higher-order Derivatives of Weighted Finite-state Machines", "track": "main", "status": "Short", "award": false, "abstract": "Weighted finite-state machines are a fundamental building block of NLP systems. They have withstood the test of time\u2014from their early use in noisy channel models in the 1990s up to modern-day neurally parameterized conditional random fields. This work examines the computation of higher-order derivatives with respect to the normalization constant for weighted finite-state machines. We provide a general algorithm for evaluating derivatives of all orders, which has not been previously described in the literature. In the case of second-order derivatives, our scheme runs in the optimal O(A\u02c62 N\u02c64) time where A is the alphabet size and N is the number of states. Our algorithm is significantly faster than prior algorithms. Additionally, our approach leads to a significantly faster algorithm for computing second-order expectations, such as covariance matrices and gradients of first-order expectations.", "author": "Ran Zmigrod; Tim Vieira; Ryan Cotterell", "authorids": "/r/ran-zmigrod/; /t/tim-vieira/; /r/ryan-cotterell/", "bibtex": "@inproceedings{zmigrod-etal-2021-higher,\n title = \"Higher-order Derivatives of Weighted Finite-state Machines\",\n author = \"Zmigrod, Ran and\n Vieira, Tim and\n Cotterell, Ryan\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-short.32/\",\n doi = \"10.18653/v1/2021.acl-short.32\",\n pages = \"240--248\"\n}", "pdf": "https://aclanthology.org/2021.acl-short.32.pdf", "site": "https://aclanthology.org/2021.acl-short.32/", "pdf_size": 476962, "gs_citation": 0, "gs_cited_by_link": "https://scholar.google.com/scholar?q=related:Mqgb6khFCMAJ:scholar.google.com/&scioq=Higher-order+Derivatives+of+Weighted+Finite-state+Machines&hl=en&as_sdt=0,33", "gs_version_total": 8, "aff": "University of Cambridge; Johns Hopkins University; ETH Z\u00fcrich", "aff_domain": "cam.ac.uk;gmail.com;inf.ethz.ch", "email": "cam.ac.uk;gmail.com;inf.ethz.ch", "github": "https://github.com/rycolab/wfsm", "project": "", "author_num": 3, "aff_unique_index": "0;1;2", "aff_unique_norm": "University of Cambridge;Johns Hopkins University;ETH Z\u00fcrich", "aff_unique_dep": ";;", "aff_unique_url": "https://www.cam.ac.uk;https://www.jhu.edu;https://www.ethz.ch", "aff_unique_abbr": "Cambridge;JHU;ETHZ", "aff_campus_unique_index": "0", "aff_campus_unique": "Cambridge;", "aff_country_unique_index": "0;1;2", "aff_country_unique": "United Kingdom;United States;Switzerland" }, { "id": "2021.acl-long.2", "title": "How Did This Get Funded?! Automatically Identifying Quirky Scientific Achievements", "track": "main", "status": "Long", "award": false, "abstract": "Humor is an important social phenomenon, serving complex social and psychological functions. However, despite being studied for millennia humor is computationally not well understood, often considered an AI-complete problem. In this work, we introduce a novel setting in humor mining: automatically detecting funny and unusual scientific papers. We are inspired by the Ig Nobel prize, a satirical prize awarded annually to celebrate funny scientific achievements (example past winner: \u201cAre cows more likely to lie down the longer they stand?\u201d). This challenging task has unique characteristics that make it particularly suitable for automatic learning. We construct a dataset containing thousands of funny papers and use it to learn classifiers, combining findings from psychology and linguistics with recent advances in NLP. We use our models to identify potentially funny papers in a large dataset of over 630,000 articles. The results demonstrate the potential of our methods, and more broadly the utility of integrating state-of-the-art NLP methods with insights from more traditional disciplines", "author": "Chen Shani; Nadav Borenstein; Dafna Shahaf", "authorids": "/c/chen-shani/; /n/nadav-borenstein/; /d/dafna-shahaf/", "bibtex": "@inproceedings{shani-etal-2021-get,\n title = \"{H}ow Did This Get Funded?! {A}utomatically Identifying Quirky Scientific Achievements\",\n author = \"Shani, Chen and\n Borenstein, Nadav and\n Shahaf, Dafna\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.2/\",\n doi = \"10.18653/v1/2021.acl-long.2\",\n pages = \"14--28\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.2.pdf", "site": "https://aclanthology.org/2021.acl-long.2/", "pdf_size": 1338880, "gs_citation": 2, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=15568623786390539523&as_sdt=5,44&sciodt=0,44&hl=en", "gs_version_total": 8, "aff": "The Hebrew University of Jerusalem; The Hebrew University of Jerusalem; The Hebrew University of Jerusalem", "aff_domain": "cs.huji.ac.il;cs.huji.ac.il;cs.huji.ac.il", "email": "cs.huji.ac.il;cs.huji.ac.il;cs.huji.ac.il", "github": "", "project": "improbable.com/ig-about", "author_num": 3, "aff_unique_index": "0;0;0", "aff_unique_norm": "The Hebrew University of Jerusalem", "aff_unique_dep": "", "aff_unique_url": "https://www.huji.ac.il", "aff_unique_abbr": "HUJI", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0;0;0", "aff_country_unique": "Israel" }, { "id": "2021.acl-long.243", "title": "How Good is Your Tokenizer? On the Monolingual Performance of Multilingual Language Models", "track": "main", "status": "Long", "award": false, "abstract": "In this work, we provide a systematic and comprehensive empirical comparison of pretrained multilingual language models versus their monolingual counterparts with regard to their monolingual task performance. We study a set of nine typologically diverse languages with readily available pretrained monolingual models on a set of five diverse monolingual downstream tasks. We first aim to establish, via fair and controlled comparisons, if a gap between the multilingual and the corresponding monolingual representation of that language exists, and subsequently investigate the reason for any performance difference. To disentangle conflating factors, we train new monolingual models on the same data, with monolingually and multilingually trained tokenizers. We find that while the pretraining data size is an important factor, a designated monolingual tokenizer plays an equally important role in the downstream performance. Our results show that languages that are adequately represented in the multilingual model\u2019s vocabulary exhibit negligible performance decreases over their monolingual counterparts. We further find that replacing the original multilingual tokenizer with the specialized monolingual tokenizer improves the downstream performance of the multilingual model for almost every task and language.", "author": "Phillip Rust; Jonas Pfeiffer; Ivan Vuli\u0107; Sebastian Ruder; Iryna Gurevych", "authorids": "/p/phillip-rust/; /j/jonas-pfeiffer/; /i/ivan-vulic/; /s/sebastian-ruder/; /i/iryna-gurevych/", "bibtex": "@inproceedings{rust-etal-2021-good,\n title = \"How Good is Your Tokenizer? On the Monolingual Performance of Multilingual Language Models\",\n author = \"Rust, Phillip and\n Pfeiffer, Jonas and\n Vuli{\\'c}, Ivan and\n Ruder, Sebastian and\n Gurevych, Iryna\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.243/\",\n doi = \"10.18653/v1/2021.acl-long.243\",\n pages = \"3118--3135\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.243.pdf", "site": "https://aclanthology.org/2021.acl-long.243/", "pdf_size": 837525, "gs_citation": 270, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=7557106659933067223&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 9, "aff": "Ubiquitous Knowledge Processing Lab, Technical University of Darmstadt + University of Copenhagen; Ubiquitous Knowledge Processing Lab, Technical University of Darmstadt; Language Technology Lab, University of Cambridge; DeepMind; Ubiquitous Knowledge Processing Lab, Technical University of Darmstadt", "aff_domain": ";;;;", "email": ";;;;", "github": "https://github.com/Adapter-Hub/hgiyt", "project": "", "author_num": 5, "aff_unique_index": "0+1;0;2;3;0", "aff_unique_norm": "Technical University of Darmstadt;University of Copenhagen;University of Cambridge;DeepMind", "aff_unique_dep": "Ubiquitous Knowledge Processing Lab;;Language Technology Lab;", "aff_unique_url": "https://www.tu-darmstadt.de;https://www.ku.dk;https://www.cam.ac.uk;https://deepmind.com", "aff_unique_abbr": "TUD;UCPH;Cambridge;DeepMind", "aff_campus_unique_index": ";1", "aff_campus_unique": ";Cambridge", "aff_country_unique_index": "0+1;0;2;2;0", "aff_country_unique": "Germany;Denmark;United Kingdom" }, { "id": "2021.acl-short.11", "title": "How Helpful is Inverse Reinforcement Learning for Table-to-Text Generation?", "track": "main", "status": "Short", "award": false, "abstract": "Existing approaches for the Table-to-Text task suffer from issues such as missing information, hallucination and repetition. Many approaches to this problem use Reinforcement Learning (RL), which maximizes a single manually defined reward, such as BLEU. In this work, we instead pose the Table-to-Text task as Inverse Reinforcement Learning (IRL) problem. We explore using multiple interpretable unsupervised reward components that are combined linearly to form a composite reward function. The composite reward function and the description generator are learned jointly. We find that IRL outperforms strong RL baselines marginally. We further study the generalization of learned IRL rewards in scenarios involving domain adaptation. Our experiments reveal significant challenges in using IRL for this task.", "author": "Sayan Ghosh; Zheng Qi; Snigdha Chaturvedi; Shashank Srivastava", "authorids": "/s/sayan-ghosh/; /z/zheng-qi/; /s/snigdha-chaturvedi/; /s/shashank-srivastava/", "bibtex": "@inproceedings{ghosh-etal-2021-helpful,\n title = \"How Helpful is Inverse Reinforcement Learning for Table-to-Text Generation?\",\n author = \"Ghosh, Sayan and\n Qi, Zheng and\n Chaturvedi, Snigdha and\n Srivastava, Shashank\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-short.11/\",\n doi = \"10.18653/v1/2021.acl-short.11\",\n pages = \"71--79\"\n}", "pdf": "https://aclanthology.org/2021.acl-short.11.pdf", "site": "https://aclanthology.org/2021.acl-short.11/", "pdf_size": 470906, "gs_citation": 15, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=16441769088307673690&as_sdt=5,33&sciodt=0,33&hl=en", "gs_version_total": 4, "aff": "UNC Chapel Hill; University of Pennsylvania; UNC Chapel Hill; UNC Chapel Hill", "aff_domain": "cs.unc.edu;seas.upenn.edu;cs.unc.edu;cs.unc.edu", "email": "cs.unc.edu;seas.upenn.edu;cs.unc.edu;cs.unc.edu", "github": "", "project": "", "author_num": 4, "aff_unique_index": "0;1;0;0", "aff_unique_norm": "University of North Carolina at Chapel Hill;University of Pennsylvania", "aff_unique_dep": ";", "aff_unique_url": "https://www.unc.edu;https://www.upenn.edu", "aff_unique_abbr": "UNC;UPenn", "aff_campus_unique_index": "0;0;0", "aff_campus_unique": "Chapel Hill;", "aff_country_unique_index": "0;0;0;0", "aff_country_unique": "United States" }, { "id": "2021.acl-long.359", "title": "How Knowledge Graph and Attention Help? A Qualitative Analysis into Bag-level Relation Extraction", "track": "main", "status": "Long", "award": false, "abstract": "Knowledge Graph (KG) and attention mechanism have been demonstrated effective in introducing and selecting useful information for weakly supervised methods. However, only qualitative analysis and ablation study are provided as evidence. In this paper, we contribute a dataset and propose a paradigm to quantitatively evaluate the effect of attention and KG on bag-level relation extraction (RE). We find that (1) higher attention accuracy may lead to worse performance as it may harm the model\u2019s ability to extract entity mention features; (2) the performance of attention is largely influenced by various noise distribution patterns, which is closely related to real-world datasets; (3) KG-enhanced attention indeed improves RE performance, while not through enhanced attention but by incorporating entity prior; and (4) attention mechanism may exacerbate the issue of insufficient training data. Based on these findings, we show that a straightforward variant of RE model can achieve significant improvements (6% AUC on average) on two real-world datasets as compared with three state-of-the-art baselines. Our codes and datasets are available at https://github.com/zig-kwin-hu/how-KG-ATT-help.", "author": "Zikun Hu; Yixin Cao; Lifu Huang; Tat-Seng Chua", "authorids": "/z/zikun-hu/; /y/yixin-cao/; /l/lifu-huang/; /t/tat-seng-chua/", "bibtex": "@inproceedings{hu-etal-2021-knowledge,\n title = \"How Knowledge Graph and Attention Help? A Qualitative Analysis into Bag-level Relation Extraction\",\n author = \"Hu, Zikun and\n Cao, Yixin and\n Huang, Lifu and\n Chua, Tat-Seng\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.359/\",\n doi = \"10.18653/v1/2021.acl-long.359\",\n pages = \"4662--4671\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.359.pdf", "site": "https://aclanthology.org/2021.acl-long.359/", "pdf_size": 875807, "gs_citation": 18, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=7993096750233343556&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 5, "aff": "National University of Singapore; S-Lab, Nanyang Technological University; Computer Science Department, Virginia Tech; National University of Singapore", "aff_domain": "nus.edu.sg;ntu.edu.sg;vt.edu;comp.nus.edu.sg", "email": "nus.edu.sg;ntu.edu.sg;vt.edu;comp.nus.edu.sg", "github": "https://github.com/zig-kwin-hu/how-KG-ATT-help", "project": "", "author_num": 4, "aff_unique_index": "0;1;2;0", "aff_unique_norm": "National University of Singapore;Nanyang Technological University;Virginia Tech", "aff_unique_dep": ";S-Lab;Computer Science Department", "aff_unique_url": "https://www.nus.edu.sg;https://www.ntu.edu.sg;https://www.vt.edu", "aff_unique_abbr": "NUS;NTU;VT", "aff_campus_unique_index": "1", "aff_campus_unique": ";Blacksburg", "aff_country_unique_index": "0;0;1;0", "aff_country_unique": "Singapore;United States" }, { "id": "2021.acl-short.27", "title": "How effective is BERT without word ordering? Implications for language understanding and data privacy", "track": "main", "status": "Short", "award": false, "abstract": "Ordered word sequences contain the rich structures that define language. However, it\u2019s often not clear if or how modern pretrained language models utilize these structures. We show that the token representations and self-attention activations within BERT are surprisingly resilient to shuffling the order of input tokens, and that for several GLUE language understanding tasks, shuffling only minimally degrades performance, e.g., by 4% for QNLI. While bleak from the perspective of language understanding, our results have positive implications for cases where copyright or ethics necessitates the consideration of bag-of-words data (vs. full documents). We simulate such a scenario for three sensitive classification tasks, demonstrating minimal performance degradation vs. releasing full language sequences.", "author": "Jack Hessel; Alexandra Schofield", "authorids": "/j/jack-hessel/; /a/alexandra-schofield/", "bibtex": "@inproceedings{hessel-schofield-2021-effective,\n title = \"How effective is {BERT} without word ordering? Implications for language understanding and data privacy\",\n author = \"Hessel, Jack and\n Schofield, Alexandra\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-short.27/\",\n doi = \"10.18653/v1/2021.acl-short.27\",\n pages = \"204--211\"\n}", "pdf": "https://aclanthology.org/2021.acl-short.27.pdf", "site": "https://aclanthology.org/2021.acl-short.27/", "pdf_size": 351523, "gs_citation": 50, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=17337826434287416726&as_sdt=5,33&sciodt=0,33&hl=en", "gs_version_total": 3, "aff": "Allen Institute for AI; Harvey Mudd College", "aff_domain": "allenai.org;cs.hmc.edu", "email": "allenai.org;cs.hmc.edu", "github": "", "project": "", "author_num": 2, "aff_unique_index": "0;1", "aff_unique_norm": "Allen Institute for AI;Harvey Mudd College", "aff_unique_dep": ";", "aff_unique_url": "https://allenai.org;https://www.hmc.edu", "aff_unique_abbr": "AI2;HMC", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0;0", "aff_country_unique": "United States" }, { "id": "2021.acl-long.325", "title": "How is BERT surprised? Layerwise detection of linguistic anomalies", "track": "main", "status": "Long", "award": false, "abstract": "Transformer language models have shown remarkable ability in detecting when a word is anomalous in context, but likelihood scores offer no information about the cause of the anomaly. In this work, we use Gaussian models for density estimation at intermediate layers of three language models (BERT, RoBERTa, and XLNet), and evaluate our method on BLiMP, a grammaticality judgement benchmark. In lower layers, surprisal is highly correlated to low token frequency, but this correlation diminishes in upper layers. Next, we gather datasets of morphosyntactic, semantic, and commonsense anomalies from psycholinguistic studies; we find that the best performing model RoBERTa exhibits surprisal in earlier layers when the anomaly is morphosyntactic than when it is semantic, while commonsense anomalies do not exhibit surprisal at any intermediate layer. These results suggest that language models employ separate mechanisms to detect different types of linguistic anomalies.", "author": "Bai Li; Zining Zhu; Guillaume Thomas; Yang Xu; Frank Rudzicz", "authorids": "/b/bai-li/; /z/zining-zhu/; /g/guillaume-thomas/; /y/yang-xu/; /f/frank-rudzicz/", "bibtex": "@inproceedings{li-etal-2021-bert,\n title = \"How is {BERT} surprised? Layerwise detection of linguistic anomalies\",\n author = \"Li, Bai and\n Zhu, Zining and\n Thomas, Guillaume and\n Xu, Yang and\n Rudzicz, Frank\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.325/\",\n doi = \"10.18653/v1/2021.acl-long.325\",\n pages = \"4215--4228\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.325.pdf", "site": "https://aclanthology.org/2021.acl-long.325/", "pdf_size": 1026274, "gs_citation": 38, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=6126782612938932074&as_sdt=5,44&sciodt=0,44&hl=en", "gs_version_total": 9, "aff": "University of Toronto, Department of Computer Science+Vector Institute for Artificial Intelligence+Unity Health Toronto; University of Toronto, Department of Computer Science+Vector Institute for Artificial Intelligence+Unity Health Toronto; University of Toronto, Department of Linguistics; University of Toronto, Department of Computer Science+University of Toronto, Cognitive Science Program+Vector Institute for Artificial Intelligence+Unity Health Toronto; University of Toronto, Department of Computer Science+Vector Institute for Artificial Intelligence+Unity Health Toronto", "aff_domain": "cs.toronto.edu;cs.toronto.edu;utoronto.ca;cs.toronto.edu;cs.toronto.edu", "email": "cs.toronto.edu;cs.toronto.edu;utoronto.ca;cs.toronto.edu;cs.toronto.edu", "github": "", "project": "", "author_num": 5, "aff_unique_index": "0+1+2;0+1+2;0;0+0+1+2;0+1+2", "aff_unique_norm": "University of Toronto;Vector Institute for Artificial Intelligence;Unity Health Toronto", "aff_unique_dep": "Department of Computer Science;;", "aff_unique_url": "https://www.utoronto.ca;https://vectorinstitute.ai/;https://www.unityhealth.to", "aff_unique_abbr": "U of T;Vector Institute;", "aff_campus_unique_index": "0;0;0;0", "aff_campus_unique": "Toronto;", "aff_country_unique_index": "0+0+0;0+0+0;0;0+0+0+0;0+0+0", "aff_country_unique": "Canada" }, { "id": "2021.acl-long.351", "title": "How to Adapt Your Pretrained Multilingual Model to 1600 Languages", "track": "main", "status": "Long", "award": false, "abstract": "Pretrained multilingual models (PMMs) enable zero-shot learning via cross-lingual transfer, performing best for languages seen during pretraining. While methods exist to improve performance for unseen languages, they have almost exclusively been evaluated using amounts of raw text only available for a small fraction of the world\u2019s languages. In this paper, we evaluate the performance of existing methods to adapt PMMs to new languages using a resource available for close to 1600 languages: the New Testament. This is challenging for two reasons: (1) the small corpus size, and (2) the narrow domain. While performance drops for all approaches, we surprisingly still see gains of up to 17.69% accuracy for part-of-speech tagging and 6.29 F1 for NER on average over all languages as compared to XLM-R. Another unexpected finding is that continued pretraining, the simplest approach, performs best. Finally, we perform a case study to disentangle the effects of domain and size and to shed light on the influence of the finetuning source language.", "author": "Abteen Ebrahimi; Katharina Kann", "authorids": "/a/abteen-ebrahimi/; /k/katharina-von-der-wense/", "bibtex": "@inproceedings{ebrahimi-kann-2021-adapt,\n title = \"How to Adapt Your Pretrained Multilingual Model to 1600 Languages\",\n author = \"Ebrahimi, Abteen and\n Kann, Katharina\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.351/\",\n doi = \"10.18653/v1/2021.acl-long.351\",\n pages = \"4555--4567\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.351.pdf", "site": "https://aclanthology.org/2021.acl-long.351/", "pdf_size": 430096, "gs_citation": 60, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=17558263769094207044&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 5, "aff": "University of Colorado Boulder; University of Colorado Boulder", "aff_domain": "colorado.edu;colorado.edu", "email": "colorado.edu;colorado.edu", "github": "", "project": "", "author_num": 2, "aff_unique_index": "0;0", "aff_unique_norm": "University of Colorado", "aff_unique_dep": "", "aff_unique_url": "https://www.colorado.edu", "aff_unique_abbr": "CU Boulder", "aff_campus_unique_index": "0;0", "aff_campus_unique": "Boulder", "aff_country_unique_index": "0;0", "aff_country_unique": "United States" }, { "id": "2021.acl-long.250", "title": "Human-in-the-Loop for Data Collection: a Multi-Target Counter Narrative Dataset to Fight Online Hate Speech", "track": "main", "status": "Long", "award": false, "abstract": "Undermining the impact of hateful content with informed and non-aggressive responses, called counter narratives, has emerged as a possible solution for having healthier online communities. Thus, some NLP studies have started addressing the task of counter narrative generation. Although such studies have made an effort to build hate speech / counter narrative (HS/CN) datasets for neural generation, they fall short in reaching either high-quality and/or high-quantity. In this paper, we propose a novel human-in-the-loop data collection methodology in which a generative language model is refined iteratively by using its own data from the previous loops to generate new training samples that experts review and/or post-edit. Our experiments comprised several loops including diverse dynamic variations. Results show that the methodology is scalable and facilitates diverse, novel, and cost-effective data collection. To our knowledge, the resulting dataset is the only expert-based multi-target HS/CN dataset available to the community.", "author": "Margherita Fanton; Helena Bonaldi; Serra Sinem Tekiro\u011flu; Marco Guerini", "authorids": "/m/margherita-fanton/; /h/helena-bonaldi/; /s/serra-sinem-tekiroglu/; /m/marco-guerini/", "bibtex": "@inproceedings{fanton-etal-2021-human,\n title = \"Human-in-the-Loop for Data Collection: a Multi-Target Counter Narrative Dataset to Fight Online Hate Speech\",\n author = \"Fanton, Margherita and\n Bonaldi, Helena and\n Tekiro{\\u{g}}lu, Serra Sinem and\n Guerini, Marco\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.250/\",\n doi = \"10.18653/v1/2021.acl-long.250\",\n pages = \"3226--3240\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.250.pdf", "site": "https://aclanthology.org/2021.acl-long.250/", "pdf_size": 1155374, "gs_citation": 115, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=17074570632395110018&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 8, "aff": "University of Trento, Italy + Fondazione Bruno Kessler, Via Sommarive 18, Povo, Trento, Italy; University of Trento, Italy + Fondazione Bruno Kessler, Via Sommarive 18, Povo, Trento, Italy; Fondazione Bruno Kessler, Via Sommarive 18, Povo, Trento, Italy; Fondazione Bruno Kessler, Via Sommarive 18, Povo, Trento, Italy", "aff_domain": "fbk.eu;fbk.eu;fbk.eu;fbk.eu", "email": "fbk.eu;fbk.eu;fbk.eu;fbk.eu", "github": "", "project": "", "author_num": 4, "aff_unique_index": "0+1;0+1;1;1", "aff_unique_norm": "University of Trento;Fondazione Bruno Kessler", "aff_unique_dep": ";", "aff_unique_url": "https://www.unitn.it;https://www.fbk.eu", "aff_unique_abbr": "UniTN;FBK", "aff_campus_unique_index": ";", "aff_campus_unique": "", "aff_country_unique_index": "0+0;0+0;0;0", "aff_country_unique": "Italy" }, { "id": "2021.acl-long.134", "title": "I like fish, especially dolphins: Addressing Contradictions in Dialogue Modeling", "track": "main", "status": "Long", "award": false, "abstract": "To quantify how well natural language understanding models can capture consistency in a general conversation, we introduce the DialoguE COntradiction DEtection task (DECODE) and a new conversational dataset containing both human-human and human-bot contradictory dialogues. We show that: (i) our newly collected dataset is notably more effective at providing supervision for the dialogue contradiction detection task than existing NLI data including those aimed to cover the dialogue domain; (ii) Transformer models that explicitly hinge on utterance structures for dialogue contradiction detection are more robust and generalize well on both analysis and out-of-distribution dialogues than standard (unstructured) Transformers. We also show that our best contradiction detection model correlates well with human judgments and further provide evidence for its usage in both automatically evaluating and improving the consistency of state-of-the-art generative chatbots.", "author": "Yixin Nie; Mary Williamson; Mohit Bansal; Douwe Kiela; Jason Weston", "authorids": "/y/yixin-nie/; /m/mary-williamson/; /m/mohit-bansal/; /d/douwe-kiela/; /j/jason-weston/", "bibtex": "@inproceedings{nie-etal-2021-like,\n title = \"{I} like fish, especially dolphins: Addressing Contradictions in Dialogue Modeling\",\n author = \"Nie, Yixin and\n Williamson, Mary and\n Bansal, Mohit and\n Kiela, Douwe and\n Weston, Jason\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.134/\",\n doi = \"10.18653/v1/2021.acl-long.134\",\n pages = \"1699--1713\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.134.pdf", "site": "https://aclanthology.org/2021.acl-long.134/", "pdf_size": 8695562, "gs_citation": 92, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=11582213088273025337&as_sdt=5,33&sciodt=0,33&hl=en", "gs_version_total": 6, "aff": "UNC Chapel Hill; Facebook AI Research; UNC Chapel Hill; Facebook AI Research; Facebook AI Research", "aff_domain": ";;;;", "email": ";;;;", "github": "", "project": "", "author_num": 5, "aff_unique_index": "0;1;0;1;1", "aff_unique_norm": "University of North Carolina at Chapel Hill;Facebook", "aff_unique_dep": ";Facebook AI Research", "aff_unique_url": "https://www.unc.edu;https://research.facebook.com", "aff_unique_abbr": "UNC;FAIR", "aff_campus_unique_index": "0;0", "aff_campus_unique": "Chapel Hill;", "aff_country_unique_index": "0;0;0;0;0", "aff_country_unique": "United States" }, { "id": "2021.acl-long.313", "title": "ILDC for CJPE: Indian Legal Documents Corpus for Court Judgment Prediction and Explanation", "track": "main", "status": "Long", "award": false, "abstract": "An automated system that could assist a judge in predicting the outcome of a case would help expedite the judicial process. For such a system to be practically useful, predictions by the system should be explainable. To promote research in developing such a system, we introduce ILDC (Indian Legal Documents Corpus). ILDC is a large corpus of 35k Indian Supreme Court cases annotated with original court decisions. A portion of the corpus (a separate test set) is annotated with gold standard explanations by legal experts. Based on ILDC, we propose the task of Court Judgment Prediction and Explanation (CJPE). The task requires an automated system to predict an explainable outcome of a case. We experiment with a battery of baseline models for case predictions and propose a hierarchical occlusion based model for explainability. Our best prediction model has an accuracy of 78% versus 94% for human legal experts, pointing towards the complexity of the prediction task. The analysis of explanations by the proposed algorithm reveals a significant difference in the point of view of the algorithm and legal experts for explaining the judgments, pointing towards scope for future research.", "author": "Vijit Malik; Rishabh Sanjay; Shubham Kumar Nigam; Kripabandhu Ghosh; Shouvik Kumar Guha; Arnab Bhattacharya; Ashutosh Modi", "authorids": "/v/vijit-malik/; /r/rishabh-sanjay/; /s/shubham-kumar-nigam/; /k/kripabandhu-ghosh/; /s/shouvik-kumar-guha/; /a/arnab-bhattacharya/; /a/ashutosh-modi/", "bibtex": "@inproceedings{malik-etal-2021-ildc,\n title = \"{ILDC} for {CJPE}: {I}ndian Legal Documents Corpus for Court Judgment Prediction and Explanation\",\n author = \"Malik, Vijit and\n Sanjay, Rishabh and\n Nigam, Shubham Kumar and\n Ghosh, Kripabandhu and\n Guha, Shouvik Kumar and\n Bhattacharya, Arnab and\n Modi, Ashutosh\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.313/\",\n doi = \"10.18653/v1/2021.acl-long.313\",\n pages = \"4046--4062\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.313.pdf", "site": "https://aclanthology.org/2021.acl-long.313/", "pdf_size": 1207550, "gs_citation": 156, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=10230117336752658673&as_sdt=5,44&sciodt=0,44&hl=en", "gs_version_total": 7, "aff": "Indian Institute of Technology Kanpur (IIT-K); Indian Institute of Technology Kanpur (IIT-K); Indian Institute of Technology Kanpur (IIT-K); Indian Institute of Science Education and Research Kolkata (IISER-K); West Bengal National University of Juridical Sciences (WBNUJS); Indian Institute of Technology Kanpur (IIT-K); Indian Institute of Technology Kanpur (IIT-K)", "aff_domain": "iitk.ac.in;iitk.ac.in;iitk.ac.in;iiserkol.ac.in;nujs.edu;cse.iitk.ac.in;cse.iitk.ac.in", "email": "iitk.ac.in;iitk.ac.in;iitk.ac.in;iiserkol.ac.in;nujs.edu;cse.iitk.ac.in;cse.iitk.ac.in", "github": "", "project": "", "author_num": 7, "aff_unique_index": "0;0;0;1;2;0;0", "aff_unique_norm": "Indian Institute of Technology Kanpur;Indian Institute of Science Education and Research Kolkata;West Bengal National University of Juridical Sciences", "aff_unique_dep": ";;", "aff_unique_url": "https://www.iitk.ac.in;https://www.iiserkol.ac.in;https://www.wbnujs.ac.in", "aff_unique_abbr": "IIT-K;IISER-K;WBNUJS", "aff_campus_unique_index": "0;0;0;1;0;0", "aff_campus_unique": "Kanpur;Kolkata;", "aff_country_unique_index": "0;0;0;0;0;0;0", "aff_country_unique": "India" }, { "id": "2021.acl-long.143", "title": "Implicit Representations of Meaning in Neural Language Models", "track": "main", "status": "Long", "award": false, "abstract": "Does the effectiveness of neural language models derive entirely from accurate modeling of surface word co-occurrence statistics, or do these models represent and reason about the world they describe? In BART and T5 transformer language models, we identify contextual word representations that function as *models of entities and situations* as they evolve throughout a discourse. These neural representations have functional similarities to linguistic models of dynamic semantics: they support a linear readout of each entity\u2019s current properties and relations, and can be manipulated with predictable effects on language generation. Our results indicate that prediction in pretrained neural language models is supported, at least in part, by dynamic representations of meaning and implicit simulation of entity state, and that this behavior can be learned with only text as training data.", "author": "Belinda Z. Li; Maxwell Nye; Jacob Andreas", "authorids": "/b/belinda-z-li/; /m/maxwell-nye/; /j/jacob-andreas/", "bibtex": "@inproceedings{li-etal-2021-implicit,\n title = \"Implicit Representations of Meaning in Neural Language Models\",\n author = \"Li, Belinda Z. and\n Nye, Maxwell and\n Andreas, Jacob\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.143/\",\n doi = \"10.18653/v1/2021.acl-long.143\",\n pages = \"1813--1827\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.143.pdf", "site": "https://aclanthology.org/2021.acl-long.143/", "pdf_size": 1745944, "gs_citation": 189, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=4847123547543019481&as_sdt=5,33&sciodt=0,33&hl=en", "gs_version_total": 5, "aff": "Massachusetts Institute of Technology; Massachusetts Institute of Technology; Massachusetts Institute of Technology", "aff_domain": "mit.edu;mit.edu;mit.edu", "email": "mit.edu;mit.edu;mit.edu", "github": "https://github.com/belindal/state-probes", "project": "", "author_num": 3, "aff_unique_index": "0;0;0", "aff_unique_norm": "Massachusetts Institute of Technology", "aff_unique_dep": "", "aff_unique_url": "https://web.mit.edu", "aff_unique_abbr": "MIT", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0;0;0", "aff_country_unique": "United States" }, { "id": "2021.acl-long.445", "title": "Importance-based Neuron Allocation for Multilingual Neural Machine Translation", "track": "main", "status": "Long", "award": false, "abstract": "Multilingual neural machine translation with a single model has drawn much attention due to its capability to deal with multiple languages. However, the current multilingual translation paradigm often makes the model tend to preserve the general knowledge, but ignore the language-specific knowledge. Some previous works try to solve this problem by adding various kinds of language-specific modules to the model, but they suffer from the parameter explosion problem and require specialized manual design. To solve these problems, we propose to divide the model neurons into general and language-specific parts based on their importance across languages. The general part is responsible for preserving the general knowledge and participating in the translation of all the languages, while the language-specific part is responsible for preserving the language-specific knowledge and participating in the translation of some specific languages. Experimental results on several language pairs, covering IWSLT and Europarl corpus datasets, demonstrate the effectiveness and universality of the proposed method.", "author": "Wanying Xie; Yang Feng; Shuhao Gu; Dong Yu", "authorids": "/w/wanying-xie/; /y/yang-feng/; /s/shuhao-gu/; /d/dong-yu/", "bibtex": "@inproceedings{xie-etal-2021-importance,\n title = \"Importance-based Neuron Allocation for Multilingual Neural Machine Translation\",\n author = \"Xie, Wanying and\n Feng, Yang and\n Gu, Shuhao and\n Yu, Dong\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.445/\",\n doi = \"10.18653/v1/2021.acl-long.445\",\n pages = \"5725--5737\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.445.pdf", "site": "https://aclanthology.org/2021.acl-long.445/", "pdf_size": 2638810, "gs_citation": 24, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=13575472750985043116&as_sdt=5,44&sciodt=0,44&hl=en", "gs_version_total": 4, "aff": "Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences (ICT/CAS) + University of Chinese Academy of Sciences, Beijing, China; Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences (ICT/CAS) + University of Chinese Academy of Sciences, Beijing, China; Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences (ICT/CAS) + University of Chinese Academy of Sciences, Beijing, China; Beijing Language and Culture University, China", "aff_domain": "gmail.com;ict.ac.cn;ict.ac.cn;blcu.edu.cn", "email": "gmail.com;ict.ac.cn;ict.ac.cn;blcu.edu.cn", "github": "https://github.com/ictnlp/NA-MNMT", "project": "", "author_num": 4, "aff_unique_index": "0+1;0+1;0+1;2", "aff_unique_norm": "Chinese Academy of Sciences;University of Chinese Academy of Sciences;Beijing Language and Culture University", "aff_unique_dep": "Institute of Computing Technology;;", "aff_unique_url": "http://www.cas.cn;http://www.ucas.ac.cn;http://www.blcu.edu.cn", "aff_unique_abbr": "CAS;UCAS;BLCU", "aff_campus_unique_index": "1;1;1", "aff_campus_unique": ";Beijing", "aff_country_unique_index": "0+0;0+0;0+0;0", "aff_country_unique": "China" }, { "id": "2021.acl-short.68", "title": "Improving Arabic Diacritization with Regularized Decoding and Adversarial Training", "track": "main", "status": "Short", "award": false, "abstract": "Arabic diacritization is a fundamental task for Arabic language processing. Previous studies have demonstrated that automatically generated knowledge can be helpful to this task. However, these studies regard the auto-generated knowledge instances as gold references, which limits their effectiveness since such knowledge is not always accurate and inferior instances can lead to incorrect predictions. In this paper, we propose to use regularized decoding and adversarial training to appropriately learn from such noisy knowledge for diacritization. Experimental results on two benchmark datasets show that, even with quite flawed auto-generated knowledge, our model can still learn adequate diacritics and outperform all previous studies, on both datasets.", "author": "Han Qin; Guimin Chen; Yuanhe Tian; Yan Song", "authorids": "/h/han-qin/; /g/guimin-chen/; /y/yuanhe-tian/; /y/yan-song/", "bibtex": "@inproceedings{qin-etal-2021-improving,\n title = \"Improving {A}rabic Diacritization with Regularized Decoding and Adversarial Training\",\n author = \"Qin, Han and\n Chen, Guimin and\n Tian, Yuanhe and\n Song, Yan\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-short.68/\",\n doi = \"10.18653/v1/2021.acl-short.68\",\n pages = \"534--542\"\n}", "pdf": "https://aclanthology.org/2021.acl-short.68.pdf", "site": "https://aclanthology.org/2021.acl-short.68/", "pdf_size": 506119, "gs_citation": 18, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=10712403240924228806&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 3, "aff": "The Chinese University of Hong Kong (Shenzhen)+Shenzhen Research Institute of Big Data; QTrade; University of Washington+Shenzhen Research Institute of Big Data; The Chinese University of Hong Kong (Shenzhen)+Shenzhen Research Institute of Big Data", "aff_domain": "link.cuhk.edu.cn;foxmail.com;uw.edu;cuhk.edu.cn", "email": "link.cuhk.edu.cn;foxmail.com;uw.edu;cuhk.edu.cn", "github": "https://github.com/cuhksz-nlp/AD-RDAT", "project": "", "author_num": 4, "aff_unique_index": "0+1;2;3+1;0+1", "aff_unique_norm": "The Chinese University of Hong Kong;Shenzhen Research Institute of Big Data;QTrade;University of Washington", "aff_unique_dep": ";;;", "aff_unique_url": "https://www.cuhk.edu.cn;http://www.sribd.cn;;https://www.washington.edu", "aff_unique_abbr": "CUHK;;;UW", "aff_campus_unique_index": "0;;0", "aff_campus_unique": "Shenzhen;", "aff_country_unique_index": "0+0;2+0;0+0", "aff_country_unique": "China;;United States" }, { "id": "2021.acl-short.81", "title": "Improving Compositional Generalization in Classification Tasks via Structure Annotations", "track": "main", "status": "Short", "award": false, "abstract": "Compositional generalization is the ability to generalize systematically to a new data distribution by combining known components. Although humans seem to have a great ability to generalize compositionally, state-of-the-art neural models struggle to do so. In this work, we study compositional generalization in classification tasks and present two main contributions. First, we study ways to convert a natural language sequence-to-sequence dataset to a classification dataset that also requires compositional generalization. Second, we show that providing structural hints (specifically, providing parse trees and entity links as attention masks for a Transformer model) helps compositional generalization.", "author": "Juyong Kim; Pradeep Ravikumar; Joshua Ainslie; Santiago Ontanon", "authorids": "/j/juyong-kim/; /p/pradeep-ravikumar/; /j/joshua-ainslie/; /s/santiago-ontanon/", "bibtex": "@inproceedings{kim-etal-2021-improving,\n title = \"Improving Compositional Generalization in Classification Tasks via Structure Annotations\",\n author = \"Kim, Juyong and\n Ravikumar, Pradeep and\n Ainslie, Joshua and\n Ontanon, Santiago\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-short.81/\",\n doi = \"10.18653/v1/2021.acl-short.81\",\n pages = \"637--645\"\n}", "pdf": "https://aclanthology.org/2021.acl-short.81.pdf", "site": "https://aclanthology.org/2021.acl-short.81/", "pdf_size": 1615202, "gs_citation": 17, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=8580189120048724786&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 4, "aff": "Carnegie Mellon University; Carnegie Mellon University; Google Research; Google Research", "aff_domain": "cs.cmu.edu;cs.cmu.edu;google.com;google.com", "email": "cs.cmu.edu;cs.cmu.edu;google.com;google.com", "github": "", "project": "http://goo.gle/compositional-classification", "author_num": 4, "aff_unique_index": "0;0;1;1", "aff_unique_norm": "Carnegie Mellon University;Google", "aff_unique_dep": ";Google Research", "aff_unique_url": "https://www.cmu.edu;https://research.google", "aff_unique_abbr": "CMU;Google Research", "aff_campus_unique_index": "1;1", "aff_campus_unique": ";Mountain View", "aff_country_unique_index": "0;0;0;0", "aff_country_unique": "United States" }, { "id": "2021.acl-long.56", "title": "Improving Dialog Systems for Negotiation with Personality Modeling", "track": "main", "status": "Long", "award": false, "abstract": "In this paper, we explore the ability to model and infer personality types of opponents, predict their responses, and use this information to adapt a dialog agent\u2019s high-level strategy in negotiation tasks. Inspired by the idea of incorporating a theory of mind (ToM) into machines, we introduce a probabilistic formulation to encapsulate the opponent\u2019s personality type during both learning and inference. We test our approach on the CraigslistBargain dataset (He et al. 2018) and show that our method using ToM inference achieves a 20% higher dialog agreement rate compared to baselines on a mixed population of opponents. We also demonstrate that our model displays diverse negotiation behavior with different types of opponents.", "author": "Runzhe Yang; Jingxiao Chen; Karthik Narasimhan", "authorids": "/r/runzhe-yang/; /j/jingxiao-chen/; /k/karthik-narasimhan/", "bibtex": "@inproceedings{yang-etal-2021-improving,\n title = \"Improving Dialog Systems for Negotiation with Personality Modeling\",\n author = \"Yang, Runzhe and\n Chen, Jingxiao and\n Narasimhan, Karthik\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.56/\",\n doi = \"10.18653/v1/2021.acl-long.56\",\n pages = \"681--693\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.56.pdf", "site": "https://aclanthology.org/2021.acl-long.56/", "pdf_size": 12217577, "gs_citation": 59, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=37757760217031039&as_sdt=5,33&sciodt=0,33&hl=en", "gs_version_total": 5, "aff": "Princeton University; Shanghai Jiao Tong University; Princeton University", "aff_domain": "princeton.edu;sjtu.edu.cn;princeton.edu", "email": "princeton.edu;sjtu.edu.cn;princeton.edu", "github": "https://github.com/princeton-nlp/NegotiationToM", "project": "", "author_num": 3, "aff_unique_index": "0;1;0", "aff_unique_norm": "Princeton University;Shanghai Jiao Tong University", "aff_unique_dep": ";", "aff_unique_url": "https://www.princeton.edu;https://www.sjtu.edu.cn", "aff_unique_abbr": "Princeton;SJTU", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0;1;0", "aff_country_unique": "United States;China" }, { "id": "2021.acl-long.392", "title": "Improving Document Representations by Generating Pseudo Query Embeddings for Dense Retrieval", "track": "main", "status": "Long", "award": false, "abstract": "Recently, the retrieval models based on dense representations have been gradually applied in the first stage of the document retrieval tasks, showing better performance than traditional sparse vector space models. To obtain high efficiency, the basic structure of these models is Bi-encoder in most cases. However, this simple structure may cause serious information loss during the encoding of documents since the queries are agnostic. To address this problem, we design a method to mimic the queries to each of the documents by an iterative clustering process and represent the documents by multiple pseudo queries (i.e., the cluster centroids). To boost the retrieval process using approximate nearest neighbor search library, we also optimize the matching function with a two-step score calculation procedure. Experimental results on several popular ranking and QA datasets show that our model can achieve state-of-the-art results while still remaining high efficiency.", "author": "Hongyin Tang; Xingwu Sun; Beihong Jin; Jingang Wang; Fuzheng Zhang; Wei Wu", "authorids": "/h/hongyin-tang/; /x/xingwu-sun/; /b/beihong-jin/; /j/jingang-wang/; /f/fuzheng-zhang/; /w/wei-wu/", "bibtex": "@inproceedings{tang-etal-2021-improving-document,\n title = \"Improving Document Representations by Generating Pseudo Query Embeddings for Dense Retrieval\",\n author = \"Tang, Hongyin and\n Sun, Xingwu and\n Jin, Beihong and\n Wang, Jingang and\n Zhang, Fuzheng and\n Wu, Wei\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.392/\",\n doi = \"10.18653/v1/2021.acl-long.392\",\n pages = \"5054--5064\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.392.pdf", "site": "https://aclanthology.org/2021.acl-long.392/", "pdf_size": 860099, "gs_citation": 45, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=9126593507543735512&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 4, "aff": "State Key Laboratory of Computer Science, Institute of Software, Chinese Academy of Sciences+University of Chinese Academy of Sciences, Beijing China; Meituan; State Key Laboratory of Computer Science, Institute of Software, Chinese Academy of Sciences+University of Chinese Academy of Sciences, Beijing China; Meituan; Meituan; Meituan", "aff_domain": "otcaix.iscas.ac.cn;meituan.com;iscas.ac.cn;meituan.com;meituan.com;meituan.com", "email": "otcaix.iscas.ac.cn;meituan.com;iscas.ac.cn;meituan.com;meituan.com;meituan.com", "github": "", "project": "", "author_num": 6, "aff_unique_index": "0+1;2;0+1;2;2;2", "aff_unique_norm": "Chinese Academy of Sciences;University of Chinese Academy of Sciences;Meituan", "aff_unique_dep": "Institute of Software;;", "aff_unique_url": "http://www.ios.ac.cn;http://www.ucas.ac.cn;https://www.meituan.com", "aff_unique_abbr": "CAS;UCAS;Meituan", "aff_campus_unique_index": "1;1", "aff_campus_unique": ";Beijing", "aff_country_unique_index": "0+0;0;0+0;0;0;0", "aff_country_unique": "China" }, { "id": "2021.acl-long.466", "title": "Improving Encoder by Auxiliary Supervision Tasks for Table-to-Text Generation", "track": "main", "status": "Long", "award": false, "abstract": "Table-to-text generation aims at automatically generating natural text to help people conveniently obtain salient information in tables. Although neural models for table-to-text have achieved remarkable progress, some problems are still overlooked. Previous methods cannot deduce the factual results from the entity\u2019s (player or team) performance and the relations between entities. To solve this issue, we first build an entity graph from the input tables and introduce a reasoning module to perform reasoning on the graph. Moreover, there are different relations (e.g., the numeric size relation and the importance relation) between records in different dimensions. And these relations may contribute to the data-to-text generation. However, it is hard for a vanilla encoder to capture these. Consequently, we propose to utilize two auxiliary tasks, Number Ranking (NR) and Importance Ranking (IR), to supervise the encoder to capture the different relations. Experimental results on ROTOWIRE and RW-FG show that our method not only has a good generalization but also outperforms previous methods on several metrics: BLEU, Content Selection, Content Ordering.", "author": "Liang Li; Can Ma; Yinliang Yue; Dayong Hu", "authorids": "/l/liang-li/; /c/can-ma/; /y/yinliang-yue/; /d/dayong-hu/", "bibtex": "@inproceedings{li-etal-2021-improving-encoder,\n title = \"Improving Encoder by Auxiliary Supervision Tasks for Table-to-Text Generation\",\n author = \"Li, Liang and\n Ma, Can and\n Yue, Yinliang and\n Hu, Dayong\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.466/\",\n doi = \"10.18653/v1/2021.acl-long.466\",\n pages = \"5979--5989\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.466.pdf", "site": "https://aclanthology.org/2021.acl-long.466/", "pdf_size": 535567, "gs_citation": 25, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=2708273861610796901&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 2, "aff": "Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China+School of Cyber Security, University of Chinese Academy of Sciences, Beijing, China; Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China; Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China; Heilongjiang Network Space Research Center, Harbin 150010, China", "aff_domain": "iie.ac.cn;iie.ac.cn;iie.ac.cn;163.com", "email": "iie.ac.cn;iie.ac.cn;iie.ac.cn;163.com", "github": "", "project": "", "author_num": 4, "aff_unique_index": "0+1;0;0;2", "aff_unique_norm": "Chinese Academy of Sciences;University of Chinese Academy of Sciences;Heilongjiang Network Space Research Center", "aff_unique_dep": "Institute of Information Engineering;School of Cyber Security;", "aff_unique_url": "http://www.cas.cn;http://www.ucas.ac.cn;", "aff_unique_abbr": "CAS;UCAS;", "aff_campus_unique_index": "0+0;0;0;1", "aff_campus_unique": "Beijing;Harbin", "aff_country_unique_index": "0+0;0;0;0", "aff_country_unique": "China" }, { "id": "2021.acl-long.536", "title": "Improving Factual Consistency of Abstractive Summarization via Question Answering", "track": "main", "status": "Long", "award": false, "abstract": "A commonly observed problem with the state-of-the art abstractive summarization models is that the generated summaries can be factually inconsistent with the input documents. The fact that automatic summarization may produce plausible-sounding yet inaccurate summaries is a major concern that limits its wide application. In this paper we present an approach to address factual consistency in summarization. We first propose an efficient automatic evaluation metric to measure factual consistency; next, we propose a novel learning algorithm that maximizes the proposed metric during model training. Through extensive experiments, we confirm that our method is effective in improving factual consistency and even overall quality of the summaries, as judged by both automatic metrics and human evaluation.", "author": "Feng Nan; Cicero Nogueira dos Santos; Henghui Zhu; Patrick Ng; Kathleen McKeown; Ramesh Nallapati; Dejiao Zhang; Zhiguo Wang; Andrew O. Arnold; Bing Xiang", "authorids": "/f/feng-nan/; /c/cicero-dos-santos/; /h/henghui-zhu/; /p/patrick-ng/; /k/kathleen-mckeown/; /r/ramesh-nallapati/; /d/dejiao-zhang/; /z/zhiguo-wang/; /a/andrew-o-arnold/; /b/bing-xiang/", "bibtex": "@inproceedings{nan-etal-2021-improving,\n title = \"Improving Factual Consistency of Abstractive Summarization via Question Answering\",\n author = \"Nan, Feng and\n Nogueira dos Santos, Cicero and\n Zhu, Henghui and\n Ng, Patrick and\n McKeown, Kathleen and\n Nallapati, Ramesh and\n Zhang, Dejiao and\n Wang, Zhiguo and\n Arnold, Andrew O. and\n Xiang, Bing\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.536/\",\n doi = \"10.18653/v1/2021.acl-long.536\",\n pages = \"6881--6894\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.536.pdf", "site": "https://aclanthology.org/2021.acl-long.536/", "pdf_size": 1246371, "gs_citation": 98, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=11794839836543496465&as_sdt=5,33&sciodt=0,33&hl=en", "gs_version_total": 6, "aff": "Amazon Web Services; Amazon Web Services; Amazon Web Services; Amazon Web Services; Amazon Web Services + Columbia University; Amazon Web Services; Amazon Web Services; Amazon Web Services; Amazon Web Services; Amazon Web Services", "aff_domain": "amazon.com;amazon.com;amazon.com;amazon.com;amazon.com;amazon.com;amazon.com;amazon.com;amazon.com;amazon.com", "email": "amazon.com;amazon.com;amazon.com;amazon.com;amazon.com;amazon.com;amazon.com;amazon.com;amazon.com;amazon.com", "github": "", "project": "", "author_num": 10, "aff_unique_index": "0;0;0;0;0+1;0;0;0;0;0", "aff_unique_norm": "Amazon Web Services;Columbia University", "aff_unique_dep": ";", "aff_unique_url": "https://aws.amazon.com;https://www.columbia.edu", "aff_unique_abbr": "AWS;Columbia", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0;0;0;0;0+0;0;0;0;0;0", "aff_country_unique": "United States" }, { "id": "2021.acl-long.124", "title": "Improving Formality Style Transfer with Context-Aware Rule Injection", "track": "main", "status": "Long", "award": false, "abstract": "Models pre-trained on large-scale regular text corpora often do not work well for user-generated data where the language styles differ significantly from the mainstream text. Here we present Context-Aware Rule Injection (CARI), an innovative method for formality style transfer (FST) by injecting multiple rules into an end-to-end BERT-based encoder and decoder model. CARI is able to learn to select optimal rules based on context. The intrinsic evaluation showed that CARI achieved the new highest performance on the FST benchmark dataset. Our extrinsic evaluation showed that CARI can greatly improve the regular pre-trained models\u2019 performance on several tweet sentiment analysis tasks. Our contributions are as follows: 1.We propose a new method, CARI, to integrate rules for pre-trained language models. CARI is context-aware and can trained end-to-end with the downstream NLP applications. 2.We have achieved new state-of-the-art results for FST on the benchmark GYAFC dataset. 3.We are the first to evaluate FST methods with extrinsic evaluation and specifically on sentiment classification tasks. We show that CARI outperformed existing rule-based FST approaches for sentiment classification.", "author": "Zonghai Yao; Hong Yu", "authorids": "/z/zonghai-yao/; /h/hong-yu/", "bibtex": "@inproceedings{yao-yu-2021-improving,\n title = \"Improving Formality Style Transfer with Context-Aware Rule Injection\",\n author = \"Yao, Zonghai and\n Yu, Hong\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.124/\",\n doi = \"10.18653/v1/2021.acl-long.124\",\n pages = \"1561--1570\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.124.pdf", "site": "https://aclanthology.org/2021.acl-long.124/", "pdf_size": 359385, "gs_citation": 14, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=670685206138357865&as_sdt=5,44&sciodt=0,44&hl=en", "gs_version_total": 4, "aff": "University of Massachusetts Amherst; University of Massachusetts Lowell", "aff_domain": "cs.umass.edu;uml.edu", "email": "cs.umass.edu;uml.edu", "github": "", "project": "", "author_num": 2, "aff_unique_index": "0;1", "aff_unique_norm": "University of Massachusetts Amherst;University of Massachusetts Lowell", "aff_unique_dep": ";", "aff_unique_url": "https://www.umass.edu;https://www.uml.edu", "aff_unique_abbr": "UMass Amherst;UMass Lowell", "aff_campus_unique_index": "0;1", "aff_campus_unique": "Amherst;Lowell", "aff_country_unique_index": "0;0", "aff_country_unique": "United States" }, { "id": "2021.acl-short.94", "title": "Improving Lexically Constrained Neural Machine Translation with Source-Conditioned Masked Span Prediction", "track": "main", "status": "Short", "award": false, "abstract": "Accurate terminology translation is crucial for ensuring the practicality and reliability of neural machine translation (NMT) systems. To address this, lexically constrained NMT explores various methods to ensure pre-specified words and phrases appear in the translation output. However, in many cases, those methods are studied on general domain corpora, where the terms are mostly uni- and bi-grams (>98%). In this paper, we instead tackle a more challenging setup consisting of domain-specific corpora with much longer n-gram and highly specialized terms. Inspired by the recent success of masked span prediction models, we propose a simple and effective training strategy that achieves consistent improvements on both terminology and sentence-level translation for three domain-specific corpora in two language pairs.", "author": "Gyubok Lee; Seongjun Yang; Edward Choi", "authorids": "/g/gyubok-lee/; /s/seongjun-yang/; /e/edward-choi/", "bibtex": "@inproceedings{lee-etal-2021-improving,\n title = \"Improving Lexically Constrained Neural Machine Translation with Source-Conditioned Masked Span Prediction\",\n author = \"Lee, Gyubok and\n Yang, Seongjun and\n Choi, Edward\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-short.94/\",\n doi = \"10.18653/v1/2021.acl-short.94\",\n pages = \"743--753\"\n}", "pdf": "https://aclanthology.org/2021.acl-short.94.pdf", "site": "https://aclanthology.org/2021.acl-short.94/", "pdf_size": 649537, "gs_citation": 7, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=4274890089465750340&as_sdt=40005&sciodt=0,10&hl=en", "gs_version_total": 8, "aff": "Graduate School of AI, KAIST; Graduate School of AI, KAIST; Graduate School of AI, KAIST", "aff_domain": "kaist.ac.kr;kaist.ac.kr;kaist.ac.kr", "email": "kaist.ac.kr;kaist.ac.kr;kaist.ac.kr", "github": "", "project": "", "author_num": 3, "aff_unique_index": "0;0;0", "aff_unique_norm": "KAIST", "aff_unique_dep": "Graduate School of AI", "aff_unique_url": "https://www.kaist.edu", "aff_unique_abbr": "KAIST", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0;0;0", "aff_country_unique": "South Korea" }, { "id": "2021.acl-short.125", "title": "Improving Model Generalization: A Chinese Named Entity Recognition Case Study", "track": "main", "status": "Short", "award": false, "abstract": "Generalization is an important ability that helps to ensure that a machine learning model can perform well on unseen data. In this paper, we study the effect of data bias on model generalization, using Chinese Named Entity Recognition (NER) as a case study. Specifically, we analyzed five benchmarking datasets for Chinese NER, and observed the following two types of data bias that can compromise model generalization ability. Firstly, the test sets of all the five datasets contain a significant proportion of entities that have been seen in the training sets. Such test data would therefore not be able to reflect the true generalization ability of a model. Secondly, all datasets are dominated by a few fat-head entities, i.e., entities appearing with particularly high frequency. As a result, a model might be able to produce high prediction accuracy simply by keyword memorization without leveraging context knowledge. To address these data biases, we first refine each test set by excluding seen entities from it, so as to better evaluate a model\u2019s generalization ability. Then, we propose a simple yet effective entity resampling method to make entities within the same category distributed equally, encouraging a model to leverage both name and context knowledge in the training process. Experimental results demonstrate that the proposed entity resampling method significantly improves a model\u2019s ability in detecting unseen entities, especially for company, organization and position categories.", "author": "Guanqing Liang; Cane Wing-Ki Leung", "authorids": "/g/guanqing-liang/; /c/cane-wing-ki-leung/", "bibtex": "@inproceedings{liang-leung-2021-improving,\n title = \"Improving Model Generalization: A {C}hinese Named Entity Recognition Case Study\",\n author = \"Liang, Guanqing and\n Leung, Cane Wing-Ki\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-short.125/\",\n doi = \"10.18653/v1/2021.acl-short.125\",\n pages = \"992--997\"\n}", "pdf": "https://aclanthology.org/2021.acl-short.125.pdf", "site": "https://aclanthology.org/2021.acl-short.125/", "pdf_size": 260323, "gs_citation": 6, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=14401675012730245355&as_sdt=5,44&sciodt=0,44&hl=en", "gs_version_total": 2, "aff": "Wisers AI Lab, Wisers Information Limited; Wisers AI Lab, Wisers Information Limited", "aff_domain": "wisers.com;wisers.com", "email": "wisers.com;wisers.com", "github": "", "project": "", "author_num": 2, "aff_unique_index": "0;0", "aff_unique_norm": "Wisers Information Limited", "aff_unique_dep": "Wisers AI Lab", "aff_unique_url": "https://www.wisers.net", "aff_unique_abbr": "", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0;0", "aff_country_unique": "China" }, { "id": "2021.acl-long.142", "title": "Improving Named Entity Recognition by External Context Retrieving and Cooperative Learning", "track": "main", "status": "Long", "award": false, "abstract": "Recent advances in Named Entity Recognition (NER) show that document-level contexts can significantly improve model performance. In many application scenarios, however, such contexts are not available. In this paper, we propose to find external contexts of a sentence by retrieving and selecting a set of semantically relevant texts through a search engine, with the original sentence as the query. We find empirically that the contextual representations computed on the retrieval-based input view, constructed through the concatenation of a sentence and its external contexts, can achieve significantly improved performance compared to the original input view based only on the sentence. Furthermore, we can improve the model performance of both input views by Cooperative Learning, a training method that encourages the two input views to produce similar contextual representations or output label distributions. Experiments show that our approach can achieve new state-of-the-art performance on 8 NER data sets across 5 domains.", "author": "Xinyu Wang; Yong Jiang; Nguyen Bach; Tao Wang; Zhongqiang Huang; Fei Huang; Kewei Tu", "authorids": "/x/xinyu-wang/; /y/yong-jiang/; /n/nguyen-bach/; /t/tao-wang/; /z/zhongqiang-huang/; /f/fei-huang/; /k/kewei-tu/", "bibtex": "@inproceedings{wang-etal-2021-improving,\n title = \"Improving Named Entity Recognition by External Context Retrieving and Cooperative Learning\",\n author = \"Wang, Xinyu and\n Jiang, Yong and\n Bach, Nguyen and\n Wang, Tao and\n Huang, Zhongqiang and\n Huang, Fei and\n Tu, Kewei\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.142/\",\n doi = \"10.18653/v1/2021.acl-long.142\",\n pages = \"1800--1812\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.142.pdf", "site": "https://aclanthology.org/2021.acl-long.142/", "pdf_size": 1110244, "gs_citation": 178, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=1565122704217847736&as_sdt=5,44&sciodt=0,44&hl=en", "gs_version_total": 9, "aff": "School of Information Science and Technology, ShanghaiTech University + Shanghai Engineering Research Center of Intelligent Vision and Imaging + Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences + University of Chinese Academy of Sciences; DAMO Academy, Alibaba Group; DAMO Academy, Alibaba Group; DAMO Academy, Alibaba Group; DAMO Academy, Alibaba Group; DAMO Academy, Alibaba Group; School of Information Science and Technology, ShanghaiTech University + Shanghai Engineering Research Center of Intelligent Vision and Imaging + Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences + University of Chinese Academy of Sciences", "aff_domain": "shanghaitech.edu.cn;alibaba-inc.com;alibaba-inc.com;alibaba-inc.com;alibaba-inc.com;alibaba-inc.com;shanghaitech.edu.cn", "email": "shanghaitech.edu.cn;alibaba-inc.com;alibaba-inc.com;alibaba-inc.com;alibaba-inc.com;alibaba-inc.com;shanghaitech.edu.cn", "github": "https://github.com/Alibaba-NLP/CLNER", "project": "", "author_num": 7, "aff_unique_index": "0+1+2+3;4;4;4;4;4;0+1+2+3", "aff_unique_norm": "ShanghaiTech University;Shanghai Engineering Research Center of Intelligent Vision and Imaging;Shanghai Institute of Microsystem and Information Technology;University of Chinese Academy of Sciences;Alibaba Group", "aff_unique_dep": "School of Information Science and Technology;;;;DAMO Academy", "aff_unique_url": "https://www.shanghaitech.edu.cn;;http://www.sim.cas.cn;http://www.ucas.ac.cn;https://www.alibaba-group.com", "aff_unique_abbr": "ShanghaiTech;;SIM;UCAS;Alibaba", "aff_campus_unique_index": "0+0;0+0", "aff_campus_unique": "Shanghai;", "aff_country_unique_index": "0+0+0+0;0;0;0;0;0;0+0+0+0", "aff_country_unique": "China" }, { "id": "2021.acl-long.552", "title": "Improving Paraphrase Detection with the Adversarial Paraphrasing Task", "track": "main", "status": "Long", "award": false, "abstract": "If two sentences have the same meaning, it should follow that they are equivalent in their inferential properties, i.e., each sentence should textually entail the other. However, many paraphrase datasets currently in widespread use rely on a sense of paraphrase based on word overlap and syntax. Can we teach them instead to identify paraphrases in a way that draws on the inferential properties of the sentences, and is not over-reliant on lexical and syntactic similarities of a sentence pair? We apply the adversarial paradigm to this question, and introduce a new adversarial method of dataset creation for paraphrase identification: the Adversarial Paraphrasing Task (APT), which asks participants to generate semantically equivalent (in the sense of mutually implicative) but lexically and syntactically disparate paraphrases. These sentence pairs can then be used both to test paraphrase identification models (which get barely random accuracy) and then improve their performance. To accelerate dataset generation, we explore automation of APT using T5, and show that the resulting dataset also improves accuracy. We discuss implications for paraphrase detection and release our dataset in the hope of making paraphrase detection models better able to detect sentence-level meaning equivalence.", "author": "Animesh Nighojkar; John Licato", "authorids": "/a/animesh-nighojkar/; /j/john-licato/", "bibtex": "@inproceedings{nighojkar-licato-2021-improving,\n title = \"Improving Paraphrase Detection with the Adversarial Paraphrasing Task\",\n author = \"Nighojkar, Animesh and\n Licato, John\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.552/\",\n doi = \"10.18653/v1/2021.acl-long.552\",\n pages = \"7106--7116\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.552.pdf", "site": "https://aclanthology.org/2021.acl-long.552/", "pdf_size": 676328, "gs_citation": 46, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=16300432097239291749&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 7, "aff": "Advancing Machine and Human Reasoning Lab, Department of Computer Science and Engineering, University of South Florida, Tampa, FL, USA; Advancing Machine and Human Reasoning Lab, Department of Computer Science and Engineering, University of South Florida, Tampa, FL, USA", "aff_domain": "usf.edu;usf.edu", "email": "usf.edu;usf.edu", "github": "", "project": "", "author_num": 2, "aff_unique_index": "0;0", "aff_unique_norm": "University of South Florida", "aff_unique_dep": "Department of Computer Science and Engineering", "aff_unique_url": "https://www.usf.edu", "aff_unique_abbr": "USF", "aff_campus_unique_index": "0;0", "aff_campus_unique": "Tampa", "aff_country_unique_index": "0;0", "aff_country_unique": "United States" }, { "id": "2021.acl-long.265", "title": "Improving Pretrained Cross-Lingual Language Models via Self-Labeled Word Alignment", "track": "main", "status": "Long", "award": false, "abstract": "The cross-lingual language models are typically pretrained with masked language modeling on multilingual text or parallel sentences. In this paper, we introduce denoising word alignment as a new cross-lingual pre-training task. Specifically, the model first self-label word alignments for parallel sentences. Then we randomly mask tokens in a bitext pair. Given a masked token, the model uses a pointer network to predict the aligned token in the other language. We alternately perform the above two steps in an expectation-maximization manner. Experimental results show that our method improves cross-lingual transferability on various datasets, especially on the token-level tasks, such as question answering, and structured prediction. Moreover, the model can serve as a pretrained word aligner, which achieves reasonably low error rate on the alignment benchmarks. The code and pretrained parameters are available at github.com/CZWin32768/XLM-Align.", "author": "Zewen Chi; Li Dong; Bo Zheng; Shaohan Huang; Xian-Ling Mao; Heyan Huang; Furu Wei", "authorids": "/z/zewen-chi/; /l/li-dong/; /b/bo-zheng/; /s/shaohan-huang/; /x/xian-ling-mao/; /h/he-yan-huang/; /f/furu-wei/", "bibtex": "@inproceedings{chi-etal-2021-improving,\n title = \"Improving Pretrained Cross-Lingual Language Models via Self-Labeled Word Alignment\",\n author = \"Chi, Zewen and\n Dong, Li and\n Zheng, Bo and\n Huang, Shaohan and\n Mao, Xian-Ling and\n Huang, Heyan and\n Wei, Furu\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.265/\",\n doi = \"10.18653/v1/2021.acl-long.265\",\n pages = \"3418--3430\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.265.pdf", "site": "https://aclanthology.org/2021.acl-long.265/", "pdf_size": 476286, "gs_citation": 72, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=4729212846408638856&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 6, "aff": "Beijing Institute of Technology\u2020; Microsoft Research\u2021; Microsoft Research\u2021; Microsoft Research\u2021; Beijing Institute of Technology\u2020; Beijing Institute of Technology\u2020; Microsoft Research\u2021", "aff_domain": "bit.edu.cn;microsoft.com;microsoft.com;microsoft.com;bit.edu.cn;bit.edu.cn;microsoft.com", "email": "bit.edu.cn;microsoft.com;microsoft.com;microsoft.com;bit.edu.cn;bit.edu.cn;microsoft.com", "github": "github.com/CZWin32768/XLM-Align", "project": "", "author_num": 7, "aff_unique_index": "0;1;1;1;0;0;1", "aff_unique_norm": "Beijing Institute of Technology;Microsoft Research", "aff_unique_dep": ";", "aff_unique_url": "http://www.bit.edu.cn/;https://www.microsoft.com/en-us/research", "aff_unique_abbr": "BIT;MSR", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0;1;1;1;0;0;1", "aff_country_unique": "China;United States" }, { "id": "2021.acl-long.328", "title": "Improving Speech Translation by Understanding and Learning from the Auxiliary Text Translation Task", "track": "main", "status": "Long", "award": false, "abstract": "Pretraining and multitask learning are widely used to improve the speech translation performance. In this study, we are interested in training a speech translation model along with an auxiliary text translation task. We conduct a detailed analysis to understand the impact of the auxiliary task on the primary task within the multitask learning framework. Our analysis confirms that multitask learning tends to generate similar decoder representations from different modalities and preserve more information from the pretrained text translation modules. We observe minimal negative transfer effect between the two tasks and sharing more parameters is helpful to transfer knowledge from the text task to the speech task. The analysis also reveals that the modality representation difference at the top decoder layers is still not negligible, and those layers are critical for the translation quality. Inspired by these findings, we propose three methods to improve translation quality. First, a parameter sharing and initialization strategy is proposed to enhance information sharing between the tasks. Second, a novel attention-based regularization is proposed for the encoders and pulls the representations from different modalities closer. Third, an online knowledge distillation is proposed to enhance the knowledge transfer from the text to the speech task. Our experiments show that the proposed approach improves translation performance by more than 2 BLEU over a strong baseline and achieves state-of-the-art results on the MuST-C English-German, English-French and English-Spanish language pairs.", "author": "Yun Tang; Juan Pino; Xian Li; Changhan Wang; Dmitriy Genzel", "authorids": "/y/yun-tang/; /j/juan-pino/; /x/xian-li/; /c/changhan-wang/; /d/dmitriy-genzel/", "bibtex": "@inproceedings{tang-etal-2021-improving,\n title = \"Improving Speech Translation by Understanding and Learning from the Auxiliary Text Translation Task\",\n author = \"Tang, Yun and\n Pino, Juan and\n Li, Xian and\n Wang, Changhan and\n Genzel, Dmitriy\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.328/\",\n doi = \"10.18653/v1/2021.acl-long.328\",\n pages = \"4252--4261\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.328.pdf", "site": "https://aclanthology.org/2021.acl-long.328/", "pdf_size": 583682, "gs_citation": 79, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=15144463762735036570&as_sdt=5,44&sciodt=0,44&hl=en", "gs_version_total": 6, "aff": "Facebook AI; Facebook AI; Facebook AI; Facebook AI; Facebook AI", "aff_domain": "fb.com;fb.com;fb.com;fb.com;fb.com", "email": "fb.com;fb.com;fb.com;fb.com;fb.com", "github": "", "project": "", "author_num": 5, "aff_unique_index": "0;0;0;0;0", "aff_unique_norm": "Facebook", "aff_unique_dep": "Facebook AI", "aff_unique_url": "https://www.facebook.com", "aff_unique_abbr": "Facebook AI", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0;0;0;0;0", "aff_country_unique": "United States" }, { "id": "2021.acl-long.101", "title": "Improving Zero-Shot Translation by Disentangling Positional Information", "track": "main", "status": "Long", "award": false, "abstract": "Multilingual neural machine translation has shown the capability of directly translating between language pairs unseen in training, i.e. zero-shot translation. Despite being conceptually attractive, it often suffers from low output quality. The difficulty of generalizing to new translation directions suggests the model representations are highly specific to those language pairs seen in training. We demonstrate that a main factor causing the language-specific representations is the positional correspondence to input tokens. We show that this can be easily alleviated by removing residual connections in an encoder layer. With this modification, we gain up to 18.5 BLEU points on zero-shot translation while retaining quality on supervised directions. The improvements are particularly prominent between related languages, where our proposed model outperforms pivot-based translation. Moreover, our approach allows easy integration of new languages, which substantially expands translation coverage. By thorough inspections of the hidden layer outputs, we show that our approach indeed leads to more language-independent representations.", "author": "Danni Liu; Jan Niehues; James Cross; Francisco Guzm\u00e1n; Xian Li", "authorids": "/d/danni-liu/; /j/jan-niehues/; /j/james-cross/; /f/francisco-guzman/; /x/xian-li/", "bibtex": "@inproceedings{liu-etal-2021-improving-zero,\n title = \"Improving Zero-Shot Translation by Disentangling Positional Information\",\n author = \"Liu, Danni and\n Niehues, Jan and\n Cross, James and\n Guzm{\\'a}n, Francisco and\n Li, Xian\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.101/\",\n doi = \"10.18653/v1/2021.acl-long.101\",\n pages = \"1259--1273\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.101.pdf", "site": "https://aclanthology.org/2021.acl-long.101/", "pdf_size": 462218, "gs_citation": 48, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=14239576806195945756&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 5, "aff": "Department of Data Science and Knowledge Engineering, Maastricht University; Department of Data Science and Knowledge Engineering, Maastricht University; Facebook AI; Facebook AI; Facebook AI", "aff_domain": "maastrichtuniversity.nl;maastrichtuniversity.nl;fb.com;fb.com;fb.com", "email": "maastrichtuniversity.nl;maastrichtuniversity.nl;fb.com;fb.com;fb.com", "github": "https://github.com/nlp-dke/NMTGMinor/tree/master/recipes/zero-shot", "project": "", "author_num": 5, "aff_unique_index": "0;0;1;1;1", "aff_unique_norm": "Maastricht University;Facebook", "aff_unique_dep": "Department of Data Science and Knowledge Engineering;Facebook AI", "aff_unique_url": "https://www.maastrichtuniversity.nl;https://www.facebook.com", "aff_unique_abbr": "MU;Facebook AI", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0;0;1;1;1", "aff_country_unique": "Netherlands;United States" }, { "id": "2021.acl-long.40", "title": "Improving the Faithfulness of Attention-based Explanations with Task-specific Information for Text Classification", "track": "main", "status": "Long", "award": false, "abstract": "Neural network architectures in natural language processing often use attention mechanisms to produce probability distributions over input token representations. Attention has empirically been demonstrated to improve performance in various tasks, while its weights have been extensively used as explanations for model predictions. Recent studies (Jain and Wallace, 2019; Serrano and Smith, 2019; Wiegreffe and Pinter, 2019) have showed that it cannot generally be considered as a faithful explanation (Jacovi and Goldberg, 2020) across encoders and tasks. In this paper, we seek to improve the faithfulness of attention-based explanations for text classification. We achieve this by proposing a new family of Task-Scaling (TaSc) mechanisms that learn task-specific non-contextualised information to scale the original attention weights. Evaluation tests for explanation faithfulness, show that the three proposed variants of TaSc improve attention-based explanations across two attention mechanisms, five encoders and five text classification datasets without sacrificing predictive performance. Finally, we demonstrate that TaSc consistently provides more faithful attention-based explanations compared to three widely-used interpretability techniques.", "author": "George Chrysostomou; Nikolaos Aletras", "authorids": "/g/george-chrysostomou/; /n/nikolaos-aletras/", "bibtex": "@inproceedings{chrysostomou-aletras-2021-improving,\n title = \"Improving the Faithfulness of Attention-based Explanations with Task-specific Information for Text Classification\",\n author = \"Chrysostomou, George and\n Aletras, Nikolaos\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.40/\",\n doi = \"10.18653/v1/2021.acl-long.40\",\n pages = \"477--488\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.40.pdf", "site": "https://aclanthology.org/2021.acl-long.40/", "pdf_size": 1745040, "gs_citation": 52, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=12703161132806672502&as_sdt=5,33&sciodt=0,33&hl=en", "gs_version_total": 7, "aff": "Department of Computer Science, University of Sheffield, United Kingdom; Department of Computer Science, University of Sheffield, United Kingdom", "aff_domain": "sheffield.ac.uk;sheffield.ac.uk", "email": "sheffield.ac.uk;sheffield.ac.uk", "github": "https://github.com/GChrysostomou/tasc.git", "project": "", "author_num": 2, "aff_unique_index": "0;0", "aff_unique_norm": "University of Sheffield", "aff_unique_dep": "Department of Computer Science", "aff_unique_url": "https://www.sheffield.ac.uk", "aff_unique_abbr": "Sheffield", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0;0", "aff_country_unique": "United Kingdom" }, { "id": "2021.acl-short.60", "title": "In Factuality: Efficient Integration of Relevant Facts for Visual Question Answering", "track": "main", "status": "Short", "award": false, "abstract": "Visual Question Answering (VQA) methods aim at leveraging visual input to answer questions that may require complex reasoning over entities. Current models are trained on labelled data that may be insufficient to learn complex knowledge representations. In this paper, we propose a new method to enhance the reasoning capabilities of a multi-modal pretrained model (Vision+Language BERT) by integrating facts extracted from an external knowledge base. Evaluation on the KVQA dataset benchmark demonstrates that our method outperforms competitive baselines by 19%, achieving new state-of-the-art results. We also perform an extensive analysis highlighting the limitations of our best performing model through an ablation study.", "author": "Peter Vickers; Nikolaos Aletras; Emilio Monti; Lo\u00efc Barrault", "authorids": "/p/peter-vickers/; /n/nikolaos-aletras/; /e/emilio-monti/; /l/loic-barrault/", "bibtex": "@inproceedings{vickers-etal-2021-factuality,\n title = \"In Factuality: Efficient Integration of Relevant Facts for Visual Question Answering\",\n author = {Vickers, Peter and\n Aletras, Nikolaos and\n Monti, Emilio and\n Barrault, Lo{\\\"i}c},\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-short.60/\",\n doi = \"10.18653/v1/2021.acl-short.60\",\n pages = \"468--475\"\n}", "pdf": "https://aclanthology.org/2021.acl-short.60.pdf", "site": "https://aclanthology.org/2021.acl-short.60/", "pdf_size": 396966, "gs_citation": 12, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=2929332863199894152&as_sdt=80000005&sciodt=0,23&hl=en", "gs_version_total": 3, "aff": "Department of Computer Science, University of Shef\ufb01eld; Department of Computer Science, University of Shef\ufb01eld; Amazon United Kingdom; Department of Computer Science, University of Shef\ufb01eld", "aff_domain": "sheffield.ac.uk;sheffield.ac.uk;amazon.co.uk;sheffield.ac.uk", "email": "sheffield.ac.uk;sheffield.ac.uk;amazon.co.uk;sheffield.ac.uk", "github": "", "project": "https://malllabiisc.github.io/resources/kvqa/", "author_num": 4, "aff_unique_index": "0;0;1;0", "aff_unique_norm": "University of Sheffield;Amazon", "aff_unique_dep": "Department of Computer Science;", "aff_unique_url": "https://www.sheffield.ac.uk;https://www.amazon.co.uk", "aff_unique_abbr": "Sheffield;Amazon UK", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0;0;0;0", "aff_country_unique": "United Kingdom" }, { "id": "2021.acl-long.570", "title": "Including Signed Languages in Natural Language Processing", "track": "main", "status": "Long", "award": true, "abstract": "Signed languages are the primary means of communication for many deaf and hard of hearing individuals. Since signed languages exhibit all the fundamental linguistic properties of natural language, we believe that tools and theories of Natural Language Processing (NLP) are crucial towards its modeling. However, existing research in Sign Language Processing (SLP) seldom attempt to explore and leverage the linguistic organization of signed languages. This position paper calls on the NLP community to include signed languages as a research area with high social and scientific impact. We first discuss the linguistic properties of signed languages to consider during their modeling. Then, we review the limitations of current SLP models and identify the open challenges to extend NLP to signed languages. Finally, we urge (1) the adoption of an efficient tokenization method; (2) the development of linguistically-informed models; (3) the collection of real-world signed language data; (4) the inclusion of local signed language communities as an active and leading voice in the direction of research.", "author": "Kayo Yin; Amit Moryossef; Julie Hochgesang; Yoav Goldberg; Malihe Alikhani", "authorids": "/k/kayo-yin/; /a/amit-moryossef/; /j/julie-hochgesang/; /y/yoav-goldberg/; /m/malihe-alikhani/", "bibtex": "@inproceedings{yin-etal-2021-including,\n title = \"Including Signed Languages in Natural Language Processing\",\n author = \"Yin, Kayo and\n Moryossef, Amit and\n Hochgesang, Julie and\n Goldberg, Yoav and\n Alikhani, Malihe\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.570/\",\n doi = \"10.18653/v1/2021.acl-long.570\",\n pages = \"7347--7360\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.570.pdf", "site": "https://aclanthology.org/2021.acl-long.570/", "pdf_size": 4017742, "gs_citation": 130, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=6376003037168356824&as_sdt=4005&sciodt=0,6&hl=en", "gs_version_total": 9, "aff": "Language Technologies Institute, Carnegie Mellon University; Bar-Ilan University; Department of Linguistics, Gallaudet University; Bar-Ilan University + Allen Institute for AI; School of Computing and Information, University of Pittsburgh", "aff_domain": "cs.cmu.edu;gmail.com;gallaudet.edu;cs.biu.ac.il;pitt.edu", "email": "cs.cmu.edu;gmail.com;gallaudet.edu;cs.biu.ac.il;pitt.edu", "github": "", "project": "", "author_num": 5, "aff_unique_index": "0;1;2;1+3;4", "aff_unique_norm": "Carnegie Mellon University;Bar-Ilan University;Gallaudet University;Allen Institute for AI;University of Pittsburgh", "aff_unique_dep": "Language Technologies Institute;;Department of Linguistics;;School of Computing and Information", "aff_unique_url": "https://www.cmu.edu;https://www.biu.ac.il;https://www.gallaudet.edu;https://allenai.org;https://www.pitt.edu", "aff_unique_abbr": "CMU;BIU;Gallaudet;AI2;Pitt", "aff_campus_unique_index": "0;;0", "aff_campus_unique": "Pittsburgh;", "aff_country_unique_index": "0;1;0;1+0;0", "aff_country_unique": "United States;Israel" }, { "id": "2021.acl-long.58", "title": "Increasing Faithfulness in Knowledge-Grounded Dialogue with Controllable Features", "track": "main", "status": "Long", "award": false, "abstract": "Knowledge-grounded dialogue systems are intended to convey information that is based on evidence provided in a given source text. We discuss the challenges of training a generative neural dialogue model for such systems that is controlled to stay faithful to the evidence. Existing datasets contain a mix of conversational responses that are faithful to selected evidence as well as more subjective or chit-chat style responses. We propose different evaluation measures to disentangle these different styles of responses by quantifying the informativeness and objectivity. At training time, additional inputs based on these evaluation measures are given to the dialogue model. At generation time, these additional inputs act as stylistic controls that encourage the model to generate responses that are faithful to the provided evidence. We also investigate the usage of additional controls at decoding time using resampling techniques. In addition to automatic metrics, we perform a human evaluation study where raters judge the output of these controlled generation models to be generally more objective and faithful to the evidence compared to baseline dialogue systems.", "author": "Hannah Rashkin; David Reitter; Gaurav Singh Tomar; Dipanjan Das", "authorids": "/h/hannah-rashkin/; /d/david-reitter/; /g/gaurav-singh-tomar/; /d/dipanjan-das/", "bibtex": "@inproceedings{rashkin-etal-2021-increasing,\n title = \"Increasing Faithfulness in Knowledge-Grounded Dialogue with Controllable Features\",\n author = \"Rashkin, Hannah and\n Reitter, David and\n Tomar, Gaurav Singh and\n Das, Dipanjan\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.58/\",\n doi = \"10.18653/v1/2021.acl-long.58\",\n pages = \"704--718\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.58.pdf", "site": "https://aclanthology.org/2021.acl-long.58/", "pdf_size": 440666, "gs_citation": 115, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=2003121347362354919&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 5, "aff": "Google Research, New York, NY; Google Research, New York, NY; Google Research, New York, NY; Google Research, New York, NY", "aff_domain": "google.com;google.com;google.com;google.com", "email": "google.com;google.com;google.com;google.com", "github": "", "project": "", "author_num": 4, "aff_unique_index": "0;0;0;0", "aff_unique_norm": "Google", "aff_unique_dep": "Google Research", "aff_unique_url": "https://research.google", "aff_unique_abbr": "Google Research", "aff_campus_unique_index": "0;0;0;0", "aff_campus_unique": "New York", "aff_country_unique_index": "0;0;0;0", "aff_country_unique": "United States" }, { "id": "2021.acl-long.133", "title": "InfoSurgeon: Cross-Media Fine-grained Information Consistency Checking for Fake News Detection", "track": "main", "status": "Long", "award": false, "abstract": "To defend against machine-generated fake news, an effective mechanism is urgently needed. We contribute a novel benchmark for fake news detection at the knowledge element level, as well as a solution for this task which incorporates cross-media consistency checking to detect the fine-grained knowledge elements making news articles misinformative. Due to training data scarcity, we also formulate a novel data synthesis method by manipulating knowledge elements within the knowledge graph to generate noisy training data with specific, hard to detect, known inconsistencies. Our detection approach outperforms the state-of-the-art (up to 16.8% accuracy gain), and more critically, yields fine-grained explanations.", "author": "Yi Fung; Christopher Thomas; Revanth Gangi Reddy; Sandeep Polisetty; Heng Ji; Shih-Fu Chang; Kathleen McKeown; Mohit Bansal; Avi Sil", "authorids": "/y/yi-fung/; /c/christopher-thomas/; /r/revanth-gangi-reddy/; /s/sandeep-polisetty/; /h/heng-ji/; /s/shih-fu-chang/; /k/kathleen-mckeown/; /m/mohit-bansal/; /a/avirup-sil/", "bibtex": "@inproceedings{fung-etal-2021-infosurgeon,\n title = \"{I}nfo{S}urgeon: Cross-Media Fine-grained Information Consistency Checking for Fake News Detection\",\n author = \"Fung, Yi and\n Thomas, Christopher and\n Gangi Reddy, Revanth and\n Polisetty, Sandeep and\n Ji, Heng and\n Chang, Shih-Fu and\n McKeown, Kathleen and\n Bansal, Mohit and\n Sil, Avi\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.133/\",\n doi = \"10.18653/v1/2021.acl-long.133\",\n pages = \"1683--1698\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.133.pdf", "site": "https://aclanthology.org/2021.acl-long.133/", "pdf_size": 7088861, "gs_citation": 83, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=2791053809106925425&as_sdt=5,33&sciodt=0,33&hl=en", "gs_version_total": 5, "aff": "University of Illinois at Urbana-Champaign; Columbia University; University of Illinois at Urbana-Champaign; University of Massachusetts Amherst; University of Illinois at Urbana-Champaign; Columbia University; Columbia University; University of North Carolina at Chapel Hill; IBM", "aff_domain": "illinois.edu;columbia.edu;illinois.edu;umass.edu;illinois.edu;columbia.edu;columbia.edu;cs.unc.edu;us.ibm.com", "email": "illinois.edu;columbia.edu;illinois.edu;umass.edu;illinois.edu;columbia.edu;columbia.edu;cs.unc.edu;us.ibm.com", "github": "https://github.com/yrf1/InfoSurgeon", "project": "", "author_num": 9, "aff_unique_index": "0;1;0;2;0;1;1;3;4", "aff_unique_norm": "University of Illinois at Urbana-Champaign;Columbia University;University of Massachusetts Amherst;University of North Carolina;International Business Machines Corporation", "aff_unique_dep": ";;;;", "aff_unique_url": "https://illinois.edu;https://www.columbia.edu;https://www.umass.edu;https://www.unc.edu;https://www.ibm.com", "aff_unique_abbr": "UIUC;Columbia;UMass Amherst;UNC;IBM", "aff_campus_unique_index": "0;0;2;0;3", "aff_campus_unique": "Urbana-Champaign;;Amherst;Chapel Hill", "aff_country_unique_index": "0;0;0;0;0;0;0;0;0", "aff_country_unique": "United States" }, { "id": "2021.acl-short.97", "title": "Input Representations for Parsing Discourse Representation Structures: Comparing English with Chinese", "track": "main", "status": "Short", "award": false, "abstract": "Neural semantic parsers have obtained acceptable results in the context of parsing DRSs (Discourse Representation Structures). In particular models with character sequences as input showed remarkable performance for English. But how does this approach perform on languages with a different writing system, like Chinese, a language with a large vocabulary of characters? Does rule-based tokenisation of the input help, and which granularity is preferred: characters, or words? The results are promising. Even with DRSs based on English, good results for Chinese are obtained. Tokenisation offers a small advantage for English, but not for Chinese. Overall, characters are preferred as input, both for English and Chinese.", "author": "Chunliu Wang; Rik van Noord; Arianna Bisazza; Johan Bos", "authorids": "/c/chunliu-wang/; /r/rik-van-noord/; /a/arianna-bisazza/; /j/johan-bos/", "bibtex": "@inproceedings{wang-etal-2021-input,\n title = \"Input Representations for Parsing Discourse Representation Structures: Comparing {E}nglish with {C}hinese\",\n author = \"Wang, Chunliu and\n van Noord, Rik and\n Bisazza, Arianna and\n Bos, Johan\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-short.97/\",\n doi = \"10.18653/v1/2021.acl-short.97\",\n pages = \"767--775\"\n}", "pdf": "https://aclanthology.org/2021.acl-short.97.pdf", "site": "https://aclanthology.org/2021.acl-short.97/", "pdf_size": 832528, "gs_citation": 10, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=16408650920798457898&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 4, "aff": "CLCG, Univ. of Groningen; CLCG, Univ. of Groningen; CLCG, Univ. of Groningen; CLCG, Univ. of Groningen", "aff_domain": "rug.nl;rug.nl;rug.nl;rug.nl", "email": "rug.nl;rug.nl;rug.nl;rug.nl", "github": "", "project": "", "author_num": 4, "aff_unique_index": "0;0;0;0", "aff_unique_norm": "University of Groningen", "aff_unique_dep": "CLCG", "aff_unique_url": "https://www.rug.nl", "aff_unique_abbr": "RUG", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0;0;0;0", "aff_country_unique": "Netherlands" }, { "id": "2021.acl-long.462", "title": "Instantaneous Grammatical Error Correction with Shallow Aggressive Decoding", "track": "main", "status": "Long", "award": false, "abstract": "In this paper, we propose Shallow Aggressive Decoding (SAD) to improve the online inference efficiency of the Transformer for instantaneous Grammatical Error Correction (GEC). SAD optimizes the online inference efficiency for GEC by two innovations: 1) it aggressively decodes as many tokens as possible in parallel instead of always decoding only one token in each step to improve computational parallelism; 2) it uses a shallow decoder instead of the conventional Transformer architecture with balanced encoder-decoder depth to reduce the computational cost during inference. Experiments in both English and Chinese GEC benchmarks show that aggressive decoding could yield identical predictions to greedy decoding but with significant speedup for online inference. Its combination with the shallow decoder could offer an even higher online inference speedup over the powerful Transformer baseline without quality loss. Not only does our approach allow a single model to achieve the state-of-the-art results in English GEC benchmarks: 66.4 F0.5 in the CoNLL-14 and 72.9 F0.5 in the BEA-19 test set with an almost 10x online inference speedup over the Transformer-big model, but also it is easily adapted to other languages. Our code is available at https://github.com/AutoTemp/Shallow-Aggressive-Decoding.", "author": "Xin Sun; Tao Ge; Furu Wei; Houfeng Wang", "authorids": "/x/xin-sun/; /t/tao-ge/; /f/furu-wei/; /h/houfeng-wang/", "bibtex": "@inproceedings{sun-etal-2021-instantaneous,\n title = \"Instantaneous Grammatical Error Correction with Shallow Aggressive Decoding\",\n author = \"Sun, Xin and\n Ge, Tao and\n Wei, Furu and\n Wang, Houfeng\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.462/\",\n doi = \"10.18653/v1/2021.acl-long.462\",\n pages = \"5937--5947\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.462.pdf", "site": "https://aclanthology.org/2021.acl-long.462/", "pdf_size": 472806, "gs_citation": 65, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=10814342450921096131&as_sdt=5,33&sciodt=0,33&hl=en", "gs_version_total": 5, "aff": "MOE Key Lab of Computational Linguistics, School of EECS, Peking University; Microsoft Research Asia; Microsoft Research Asia; MOE Key Lab of Computational Linguistics, School of EECS, Peking University", "aff_domain": "pku.edu.cn;microsoft.com;microsoft.com;pku.edu.cn", "email": "pku.edu.cn;microsoft.com;microsoft.com;pku.edu.cn", "github": "https://github.com/AutoTemp/Shallow-Aggressive-Decoding", "project": "", "author_num": 4, "aff_unique_index": "0;1;1;0", "aff_unique_norm": "Peking University;Microsoft Research", "aff_unique_dep": "School of EECS;Research", "aff_unique_url": "http://www.pku.edu.cn;https://www.microsoft.com/en-us/research/group/asia", "aff_unique_abbr": "PKU;MSR Asia", "aff_campus_unique_index": "1;1", "aff_campus_unique": ";Asia", "aff_country_unique_index": "0;0;0;0", "aff_country_unique": "China" }, { "id": "2021.acl-long.71", "title": "Integrated Directional Gradients: Feature Interaction Attribution for Neural NLP Models", "track": "main", "status": "Long", "award": false, "abstract": "In this paper, we introduce Integrated Directional Gradients (IDG), a method for attributing importance scores to groups of features, indicating their relevance to the output of a neural network model for a given input. The success of Deep Neural Networks has been attributed to their ability to capture higher level feature interactions. Hence, in the last few years capturing the importance of these feature interactions has received increased prominence in ML interpretability literature. In this paper, we formally define the feature group attribution problem and outline a set of axioms that any intuitive feature group attribution method should satisfy. Earlier, cooperative game theory inspired axiomatic methods only borrowed axioms from solution concepts (such as Shapley value) for individual feature attributions and introduced their own extensions to model interactions. In contrast, our formulation is inspired by axioms satisfied by characteristic functions as well as solution concepts in cooperative game theory literature. We believe that characteristic functions are much better suited to model importance of groups compared to just solution concepts. We demonstrate that our proposed method, IDG, satisfies all the axioms. Using IDG we analyze two state-of-the-art text classifiers on three benchmark datasets for sentiment analysis. Our experiments show that IDG is able to effectively capture semantic interactions in linguistic models via negations and conjunctions.", "author": "Sandipan Sikdar; Parantapa Bhattacharya; Kieran Heese", "authorids": "/s/sandipan-sikdar/; /p/parantapa-bhattacharya/; /k/kieran-heese/", "bibtex": "@inproceedings{sikdar-etal-2021-integrated,\n title = \"Integrated Directional Gradients: Feature Interaction Attribution for Neural {NLP} Models\",\n author = \"Sikdar, Sandipan and\n Bhattacharya, Parantapa and\n Heese, Kieran\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.71/\",\n doi = \"10.18653/v1/2021.acl-long.71\",\n pages = \"865--878\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.71.pdf", "site": "https://aclanthology.org/2021.acl-long.71/", "pdf_size": 573874, "gs_citation": 62, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=12763947458264002630&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 8, "aff": "RWTH Aachen University; University of Virginia; University of Virginia", "aff_domain": "cssh.rwth-aachen.de;virginia.edu;virginia.edu", "email": "cssh.rwth-aachen.de;virginia.edu;virginia.edu", "github": "", "project": "", "author_num": 3, "aff_unique_index": "0;1;1", "aff_unique_norm": "RWTH Aachen University;University of Virginia", "aff_unique_dep": ";", "aff_unique_url": "https://www.rwth-aachen.de;https://www.virginia.edu", "aff_unique_abbr": "RWTH;UVA", "aff_campus_unique_index": "0", "aff_campus_unique": "Aachen;", "aff_country_unique_index": "0;1;1", "aff_country_unique": "Germany;United States" }, { "id": "2021.acl-long.174", "title": "Integrating Semantics and Neighborhood Information with Graph-Driven Generative Models for Document Retrieval", "track": "main", "status": "Long", "award": false, "abstract": "With the need of fast retrieval speed and small memory footprint, document hashing has been playing a crucial role in large-scale information retrieval. To generate high-quality hashing code, both semantics and neighborhood information are crucial. However, most existing methods leverage only one of them or simply combine them via some intuitive criteria, lacking a theoretical principle to guide the integration process. In this paper, we encode the neighborhood information with a graph-induced Gaussian distribution, and propose to integrate the two types of information with a graph-driven generative model. To deal with the complicated correlations among documents, we further propose a tree-structured approximation method for learning. Under the approximation, we prove that the training objective can be decomposed into terms involving only singleton or pairwise documents, enabling the model to be trained as efficiently as uncorrelated ones. Extensive experimental results on three benchmark datasets show that our method achieves superior performance over state-of-the-art methods, demonstrating the effectiveness of the proposed model for simultaneously preserving semantic and neighborhood information.", "author": "Zijing Ou; Qinliang Su; Jianxing Yu; Bang Liu; Jingwen Wang; Ruihui Zhao; Changyou Chen; Yefeng Zheng", "authorids": "/z/zijing-ou/; /q/qinliang-su/; /j/jianxing-yu/; /b/bang-liu/; /j/jingwen-wang/; /r/ruihui-zhao/; /c/changyou-chen/; /y/yefeng-zheng/", "bibtex": "@inproceedings{ou-etal-2021-integrating,\n title = \"Integrating Semantics and Neighborhood Information with Graph-Driven Generative Models for Document Retrieval\",\n author = \"Ou, Zijing and\n Su, Qinliang and\n Yu, Jianxing and\n Liu, Bang and\n Wang, Jingwen and\n Zhao, Ruihui and\n Chen, Changyou and\n Zheng, Yefeng\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.174/\",\n doi = \"10.18653/v1/2021.acl-long.174\",\n pages = \"2238--2249\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.174.pdf", "site": "https://aclanthology.org/2021.acl-long.174/", "pdf_size": 1158591, "gs_citation": 5, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=17232868116395120297&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 8, "aff": "School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China; School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China; School of Artificial Intelligence, Sun Yat-sen University, Guangdong, China; RALI & Mila, Universit\u00e9 de Montr\u00e9al; Tencent Jarvis Lab; Tencent Jarvis Lab; CSE Department, SUNY at Buffalo; Tencent Jarvis Lab", "aff_domain": "mail2.sysu.edu.cn;mail.sysu.edu.cn;mail.sysu.edu.cn;umontreal.ca;tencent.com;tencent.com;tencent.com;buffalo.edu", "email": "mail2.sysu.edu.cn;mail.sysu.edu.cn;mail.sysu.edu.cn;umontreal.ca;tencent.com;tencent.com;tencent.com;buffalo.edu", "github": "https://github.com/J-zin/SNUH", "project": "", "author_num": 8, "aff_unique_index": "0;0;0;1;2;2;3;2", "aff_unique_norm": "Sun Yat-sen University;Universit\u00e9 de Montr\u00e9al;Tencent;State University of New York at Buffalo", "aff_unique_dep": "School of Computer Science and Engineering;RALI & Mila;Jarvis Lab;Department of Computer Science and Engineering", "aff_unique_url": "http://www.sysu.edu.cn;https://www.umontreal.ca;https://www.tencent.com;https://www.buffalo.edu", "aff_unique_abbr": "SYSU;UdeM;Tencent;SUNY Buffalo", "aff_campus_unique_index": "0;0;1;2;4", "aff_campus_unique": "Guangzhou;Guangdong;Montr\u00e9al;;Buffalo", "aff_country_unique_index": "0;0;0;1;0;0;2;0", "aff_country_unique": "China;Canada;United States" }, { "id": "2021.acl-long.340", "title": "Intent Classification and Slot Filling for Privacy Policies", "track": "main", "status": "Long", "award": false, "abstract": "Understanding privacy policies is crucial for users as it empowers them to learn about the information that matters to them. Sentences written in a privacy policy document explain privacy practices, and the constituent text spans convey further specific information about that practice. We refer to predicting the privacy practice explained in a sentence as intent classification and identifying the text spans sharing specific information as slot filling. In this work, we propose PolicyIE, an English corpus consisting of 5,250 intent and 11,788 slot annotations spanning 31 privacy policies of websites and mobile applications. PolicyIE corpus is a challenging real-world benchmark with limited labeled examples reflecting the cost of collecting large-scale annotations from domain experts. We present two alternative neural approaches as baselines, (1) intent classification and slot filling as a joint sequence tagging and (2) modeling them as a sequence-to-sequence (Seq2Seq) learning task. The experiment results show that both approaches perform comparably in intent classification, while the Seq2Seq method outperforms the sequence tagging approach in slot filling by a large margin. We perform a detailed error analysis to reveal the challenges of the proposed corpus.", "author": "Wasi Ahmad; Jianfeng Chi; Tu Le; Thomas Norton; Yuan Tian; Kai-Wei Chang", "authorids": "/w/wasi-ahmad/; /j/jianfeng-chi/; /t/tu-le/; /t/thomas-norton/; /y/yuan-tian/; /k/kai-wei-chang/", "bibtex": "@inproceedings{ahmad-etal-2021-intent,\n title = \"Intent Classification and Slot Filling for Privacy Policies\",\n author = \"Ahmad, Wasi and\n Chi, Jianfeng and\n Le, Tu and\n Norton, Thomas and\n Tian, Yuan and\n Chang, Kai-Wei\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.340/\",\n doi = \"10.18653/v1/2021.acl-long.340\",\n pages = \"4402--4417\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.340.pdf", "site": "https://aclanthology.org/2021.acl-long.340/", "pdf_size": 438002, "gs_citation": 35, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=14044572577486920363&as_sdt=5,33&sciodt=0,33&hl=en", "gs_version_total": 6, "aff": "University of California, Los Angeles; University of Virginia; University of Virginia; Fordham University; University of Virginia; University of California, Los Angeles", "aff_domain": "cs.ucla.edu;virginia.edu;virginia.edu;law.fordham.edu;virginia.edu;cs.ucla.edu", "email": "cs.ucla.edu;virginia.edu;virginia.edu;law.fordham.edu;virginia.edu;cs.ucla.edu", "github": "", "project": "", "author_num": 6, "aff_unique_index": "0;1;1;2;1;0", "aff_unique_norm": "University of California, Los Angeles;University of Virginia;Fordham University", "aff_unique_dep": ";;", "aff_unique_url": "https://www.ucla.edu;https://www.virginia.edu;https://www.fordham.edu", "aff_unique_abbr": "UCLA;UVA;Fordham", "aff_campus_unique_index": "0;0", "aff_campus_unique": "Los Angeles;", "aff_country_unique_index": "0;0;0;0;0;0", "aff_country_unique": "United States" }, { "id": "2021.acl-long.528", "title": "Inter-GPS: Interpretable Geometry Problem Solving with Formal Language and Symbolic Reasoning", "track": "main", "status": "Long", "award": false, "abstract": "Geometry problem solving has attracted much attention in the NLP community recently. The task is challenging as it requires abstract problem understanding and symbolic reasoning with axiomatic knowledge. However, current datasets are either small in scale or not publicly available. Thus, we construct a new large-scale benchmark, Geometry3K, consisting of 3,002 geometry problems with dense annotation in formal language. We further propose a novel geometry solving approach with formal language and symbolic reasoning, called Interpretable Geometry Problem Solver (Inter-GPS). Inter-GPS first parses the problem text and diagram into formal language automatically via rule-based text parsing and neural object detecting, respectively. Unlike implicit learning in existing methods, Inter-GPS incorporates theorem knowledge as conditional rules and performs symbolic reasoning step by step. Also, a theorem predictor is designed to infer the theorem application sequence fed to the symbolic solver for the more efficient and reasonable searching path. Extensive experiments on the Geometry3K and GEOS datasets demonstrate that Inter-GPS achieves significant improvements over existing methods. The project with code and data is available at https://lupantech.github.io/inter-gps.", "author": "Pan Lu; Ran Gong; Shibiao Jiang; Liang Qiu; Siyuan Huang; Xiaodan Liang; Song-Chun Zhu", "authorids": "/p/pan-lu/; /r/ran-gong/; /s/shibiao-jiang/; /l/liang-qiu/; /s/siyuan-huang/; /x/xiaodan-liang/; /s/song-chun-zhu/", "bibtex": "@inproceedings{lu-etal-2021-inter,\n title = \"{I}nter-{GPS}: Interpretable Geometry Problem Solving with Formal Language and Symbolic Reasoning\",\n author = \"Lu, Pan and\n Gong, Ran and\n Jiang, Shibiao and\n Qiu, Liang and\n Huang, Siyuan and\n Liang, Xiaodan and\n Zhu, Song-Chun\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.528/\",\n doi = \"10.18653/v1/2021.acl-long.528\",\n pages = \"6774--6786\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.528.pdf", "site": "https://aclanthology.org/2021.acl-long.528/", "pdf_size": 1115218, "gs_citation": 235, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=5164947407248829312&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 7, "aff": "Center for Vision, Cognition, Learning and Autonomy, UCLA; Center for Vision, Cognition, Learning and Autonomy, UCLA; College of Computer Science and Technology, Zhejiang University; Center for Vision, Cognition, Learning and Autonomy, UCLA; Center for Vision, Cognition, Learning and Autonomy, UCLA; School of Intelligent Systems Engineering, Sun Yat-sen University; Center for Vision, Cognition, Learning and Autonomy, UCLA+Beijing Institute of General Artificial Intelligence+Peking University+Tsinghua University", "aff_domain": "cs.ucla.edu;ucla.edu;gmail.com;ucla.edu;ucla.edu;gmail.com;stat.ucla.edu", "email": "cs.ucla.edu;ucla.edu;gmail.com;ucla.edu;ucla.edu;gmail.com;stat.ucla.edu", "github": "", "project": "https://lupantech.github.io/inter-gps", "author_num": 7, "aff_unique_index": "0;0;1;0;0;2;0+3+4+5", "aff_unique_norm": "University of California, Los Angeles;Zhejiang University;Sun Yat-sen University;Beijing Institute of General Artificial Intelligence;Peking University;Tsinghua University", "aff_unique_dep": "Center for Vision, Cognition, Learning and Autonomy;College of Computer Science and Technology;School of Intelligent Systems Engineering;;;", "aff_unique_url": "https://www.ucla.edu;http://www.zju.edu.cn;http://www.sysu.edu.cn/;http://www.bigaiai.cn;http://www.pku.edu.cn;https://www.tsinghua.edu.cn", "aff_unique_abbr": "UCLA;ZJU;SYSU;BIGAI;Peking U;THU", "aff_campus_unique_index": "0;0;0;0;0", "aff_campus_unique": "Los Angeles;", "aff_country_unique_index": "0;0;1;0;0;1;0+1+1+1", "aff_country_unique": "United States;China" }, { "id": "2021.acl-long.215", "title": "Interpretable and Low-Resource Entity Matching via Decoupling Feature Learning from Decision Making", "track": "main", "status": "Long", "award": false, "abstract": "Entity Matching (EM) aims at recognizing entity records that denote the same real-world object. Neural EM models learn vector representation of entity descriptions and match entities end-to-end. Though robust, these methods require many annotated resources for training, and lack of interpretability. In this paper, we propose a novel EM framework that consists of Heterogeneous Information Fusion (HIF) and Key Attribute Tree (KAT) Induction to decouple feature representation from matching decision. Using self-supervised learning and mask mechanism in pre-trained language modeling, HIF learns the embeddings of noisy attribute values by inter-attribute attention with unlabeled data. Using a set of comparison features and a limited amount of annotated data, KAT Induction learns an efficient decision tree that can be interpreted by generating entity matching rules whose structure is advocated by domain experts. Experiments on 6 public datasets and 3 industrial datasets show that our method is highly efficient and outperforms SOTA EM models in most cases. We will release the codes upon acceptance.", "author": "Zijun Yao; Chengjiang Li; Tiansi Dong; Xin Lv; Jifan Yu; Lei Hou; Juanzi Li; Yichi Zhang; Zelin Dai", "authorids": "/z/zijun-yao/; /c/chengjiang-li/; /t/tiansi-dong/; /x/xin-lv/; /j/jifan-yu/; /l/lei-hou/; /j/juanzi-li/; /y/yichi-zhang/; /z/zelin-dai/", "bibtex": "@inproceedings{yao-etal-2021-interpretable,\n title = \"Interpretable and Low-Resource Entity Matching via Decoupling Feature Learning from Decision Making\",\n author = \"Yao, Zijun and\n Li, Chengjiang and\n Dong, Tiansi and\n Lv, Xin and\n Yu, Jifan and\n Hou, Lei and\n Li, Juanzi and\n Zhang, Yichi and\n Dai, Zelin\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.215/\",\n doi = \"10.18653/v1/2021.acl-long.215\",\n pages = \"2770--2781\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.215.pdf", "site": "https://aclanthology.org/2021.acl-long.215/", "pdf_size": 803579, "gs_citation": 10, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=7518027458317006917&as_sdt=5,44&sciodt=0,44&hl=en", "gs_version_total": 11, "aff": "Department of Computer Science and Technology, BNRist; Department of Computer Science and Technology, BNRist; B-IT, University of Bonn, Germany; Department of Computer Science and Technology, BNRist; Department of Computer Science and Technology, BNRist; Department of Computer Science and Technology, BNRist; Department of Computer Science and Technology, BNRist; Alibaba Group, Hangzhou, China; Alibaba Group, Hangzhou, China", "aff_domain": "mails.tsinghua.edu.cn;tsinghua.edu.cn;bit.uni-bonn.de; ; ; ; ; ; ", "email": "mails.tsinghua.edu.cn;tsinghua.edu.cn;bit.uni-bonn.de; ; ; ; ; ; ", "github": "https://github.com/THU-KEG/HIF-KAT", "project": "", "author_num": 9, "aff_unique_index": "0;0;1;0;0;0;0;2;2", "aff_unique_norm": "BNRist;University of Bonn;Alibaba Group", "aff_unique_dep": "Department of Computer Science and Technology;B-IT;", "aff_unique_url": ";https://www.uni-bonn.de;https://www.alibaba.com", "aff_unique_abbr": ";Uni Bonn;Alibaba", "aff_campus_unique_index": "1;1", "aff_campus_unique": ";Hangzhou", "aff_country_unique_index": "1;2;2", "aff_country_unique": ";Germany;China" }, { "id": "2021.acl-long.150", "title": "Intrinsic Bias Metrics Do Not Correlate with Application Bias", "track": "main", "status": "Long", "award": false, "abstract": "Natural Language Processing (NLP) systems learn harmful societal biases that cause them to amplify inequality as they are deployed in more and more situations. To guide efforts at debiasing these systems, the NLP community relies on a variety of metrics that quantify bias in models. Some of these metrics are intrinsic, measuring bias in word embedding spaces, and some are extrinsic, measuring bias in downstream tasks that the word embeddings enable. Do these intrinsic and extrinsic metrics correlate with each other? We compare intrinsic and extrinsic metrics across hundreds of trained models covering different tasks and experimental conditions. Our results show no reliable correlation between these metrics that holds in all scenarios across tasks and languages. We urge researchers working on debiasing to focus on extrinsic measures of bias, and to make using these measures more feasible via creation of new challenge sets and annotated test data. To aid this effort, we release code, a new intrinsic metric, and an annotated test set focused on gender bias in hate speech.", "author": "Seraphina Goldfarb-Tarrant; Rebecca Marchant; Ricardo Mu\u00f1oz S\u00e1nchez; Mugdha Pandya; Adam Lopez", "authorids": "/s/seraphina-goldfarb-tarrant/; /r/rebecca-marchant/; /r/ricardo-munoz-sanchez/; /m/mugdha-pandya/; /a/adam-lopez/", "bibtex": "@inproceedings{goldfarb-tarrant-etal-2021-intrinsic,\n title = \"Intrinsic Bias Metrics Do Not Correlate with Application Bias\",\n author = \"Goldfarb-Tarrant, Seraphina and\n Marchant, Rebecca and\n Mu{\\~n}oz S{\\'a}nchez, Ricardo and\n Pandya, Mugdha and\n Lopez, Adam\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.150/\",\n doi = \"10.18653/v1/2021.acl-long.150\",\n pages = \"1926--1940\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.150.pdf", "site": "https://aclanthology.org/2021.acl-long.150/", "pdf_size": 824426, "gs_citation": 188, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=9119754745689154279&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 5, "aff": "University of Edinburgh\u2020; University of Edinburgh\u2020; University of Edinburgh\u2020; University of Edinburgh\u2020; Rasa Technologies GmbH\u2021", "aff_domain": "ed.ac.uk;gmail.com;gmail.com;gmail.com;rasa.com", "email": "ed.ac.uk;gmail.com;gmail.com;gmail.com;rasa.com", "github": "", "project": "https://tinyurl.com/serif-embed", "author_num": 5, "aff_unique_index": "0;0;0;0;1", "aff_unique_norm": "University of Edinburgh;Rasa Technologies", "aff_unique_dep": ";Technologies", "aff_unique_url": "https://www.ed.ac.uk;https://www.rasa.com", "aff_unique_abbr": "Edinburgh;Rasa", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0;0;0;0;1", "aff_country_unique": "United Kingdom;Germany" }, { "id": "2021.acl-long.568", "title": "Intrinsic Dimensionality Explains the Effectiveness of Language Model Fine-Tuning", "track": "main", "status": "Long", "award": true, "abstract": "Although pretrained language models can be fine-tuned to produce state-of-the-art results for a very wide range of language understanding tasks, the dynamics of this process are not well understood, especially in the low data regime. Why can we use relatively vanilla gradient descent algorithms (e.g., without strong regularization) to tune a model with hundreds of millions of parameters on datasets with only hundreds or thousands of labeled examples? In this paper, we argue that analyzing fine-tuning through the lens of intrinsic dimension provides us with empirical and theoretical intuitions to explain this remarkable phenomenon. We empirically show that common pre-trained models have a very low intrinsic dimension; in other words, there exists a low dimension reparameterization that is as effective for fine-tuning as the full parameter space. For example, by optimizing only 200 trainable parameters randomly projected back into the full space, we can tune a RoBERTa model to achieve 90% of the full parameter performance levels on MRPC. Furthermore, we empirically show that pre-training implicitly minimizes intrinsic dimension and, perhaps surprisingly, larger models tend to have lower intrinsic dimension after a fixed number of pre-training updates, at least in part explaining their extreme effectiveness. Lastly, we connect intrinsic dimensionality with low dimensional task representations and compression based generalization bounds to provide intrinsic-dimension-based generalization bounds that are independent of the full parameter count.", "author": "Armen Aghajanyan; Sonal Gupta; Luke Zettlemoyer", "authorids": "/a/armen-aghajanyan/; /s/sonal-gupta/; /l/luke-zettlemoyer/", "bibtex": "@inproceedings{aghajanyan-etal-2021-intrinsic,\n title = \"Intrinsic Dimensionality Explains the Effectiveness of Language Model Fine-Tuning\",\n author = \"Aghajanyan, Armen and\n Gupta, Sonal and\n Zettlemoyer, Luke\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.568/\",\n doi = \"10.18653/v1/2021.acl-long.568\",\n pages = \"7319--7328\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.568.pdf", "site": "https://aclanthology.org/2021.acl-long.568/", "pdf_size": 423651, "gs_citation": 656, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=1687108039804497321&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 5, "aff": "Facebook AI; Facebook; Facebook AI + University of Washington", "aff_domain": "fb.com;fb.com;fb.com", "email": "fb.com;fb.com;fb.com", "github": "", "project": "", "author_num": 3, "aff_unique_index": "0;1;0+2", "aff_unique_norm": "Facebook;Facebook, Inc.;University of Washington", "aff_unique_dep": "Facebook AI;;", "aff_unique_url": "https://www.facebook.com;https://www.facebook.com;https://www.washington.edu", "aff_unique_abbr": "Facebook AI;FB;UW", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0;0;0+0", "aff_country_unique": "United States" }, { "id": "2021.acl-long.36", "title": "Introducing Orthogonal Constraint in Structural Probes", "track": "main", "status": "Long", "award": false, "abstract": "With the recent success of pre-trained models in NLP, a significant focus was put on interpreting their representations. One of the most prominent approaches is structural probing (Hewitt and Manning, 2019), where a linear projection of word embeddings is performed in order to approximate the topology of dependency structures. In this work, we introduce a new type of structural probing, where the linear projection is decomposed into 1. iso-morphic space rotation; 2. linear scaling that identifies and scales the most relevant dimensions. In addition to syntactic dependency, we evaluate our method on two novel tasks (lexical hypernymy and position in a sentence). We jointly train the probes for multiple tasks and experimentally show that lexical and syntactic information is separated in the representations. Moreover, the orthogonal constraint makes the Structural Probes less vulnerable to memorization.", "author": "Tomasz Limisiewicz; David Mare\u010dek", "authorids": "/t/tomasz-limisiewicz/; /d/david-marecek/", "bibtex": "@inproceedings{limisiewicz-marecek-2021-introducing,\n title = \"Introducing Orthogonal Constraint in Structural Probes\",\n author = \"Limisiewicz, Tomasz and\n Mare{\\v{c}}ek, David\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.36/\",\n doi = \"10.18653/v1/2021.acl-long.36\",\n pages = \"428--442\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.36.pdf", "site": "https://aclanthology.org/2021.acl-long.36/", "pdf_size": 602590, "gs_citation": 18, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=695536721055823043&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 4, "aff": "Institute of Formal and Applied Linguistics, Faculty of Mathematics and Physics, Charles University, Prague, Czech Republic; Institute of Formal and Applied Linguistics, Faculty of Mathematics and Physics, Charles University, Prague, Czech Republic", "aff_domain": "ufal.mff.cuni.cz;ufal.mff.cuni.cz", "email": "ufal.mff.cuni.cz;ufal.mff.cuni.cz", "github": "", "project": "", "author_num": 2, "aff_unique_index": "0;0", "aff_unique_norm": "Charles University", "aff_unique_dep": "Institute of Formal and Applied Linguistics, Faculty of Mathematics and Physics", "aff_unique_url": "https://www.cuni.cz", "aff_unique_abbr": "Charles University", "aff_campus_unique_index": "0;0", "aff_campus_unique": "Prague", "aff_country_unique_index": "0;0", "aff_country_unique": "Czech Republic" }, { "id": "2021.acl-long.1", "title": "Investigating label suggestions for opinion mining in German Covid-19 social media", "track": "main", "status": "Long", "award": false, "abstract": "This work investigates the use of interactively updated label suggestions to improve upon the efficiency of gathering annotations on the task of opinion mining in German Covid-19 social media data. We develop guidelines to conduct a controlled annotation study with social science students and find that suggestions from a model trained on a small, expert-annotated dataset already lead to a substantial improvement \u2013 in terms of inter-annotator agreement (+.14 Fleiss\u2019 \u03ba) and annotation quality \u2013 compared to students that do not receive any label suggestions. We further find that label suggestions from interactively trained models do not lead to an improvement over suggestions from a static model. Nonetheless, our analysis of suggestion bias shows that annotators remain capable of reflecting upon the suggested label in general. Finally, we confirm the quality of the annotated data in transfer learning experiments between different annotator groups. To facilitate further research in opinion mining on social media data, we release our collected data consisting of 200 expert and 2,785 student annotations.", "author": "Tilman Beck; Ji-Ung Lee; Christina Viehmann; Marcus Maurer; Oliver Quiring; Iryna Gurevych", "authorids": "/t/tilman-beck/; /j/ji-ung-lee/; /c/christina-viehmann/; /m/marcus-maurer/; /o/oliver-quiring/; /i/iryna-gurevych/", "bibtex": "@inproceedings{beck-etal-2021-investigating,\n title = \"Investigating label suggestions for opinion mining in {G}erman Covid-19 social media\",\n author = \"Beck, Tilman and\n Lee, Ji-Ung and\n Viehmann, Christina and\n Maurer, Marcus and\n Quiring, Oliver and\n Gurevych, Iryna\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.1/\",\n doi = \"10.18653/v1/2021.acl-long.1\",\n pages = \"1--13\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.1.pdf", "site": "https://aclanthology.org/2021.acl-long.1/", "pdf_size": 528252, "gs_citation": 20, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=11153881224259371844&as_sdt=5,44&sciodt=0,44&hl=en", "gs_version_total": 11, "aff": "Ubiquitous Knowledge Processing Lab, Technical University of Darmstadt; Ubiquitous Knowledge Processing Lab, Technical University of Darmstadt; Institut f\u00fcr Publizistik, Johannes Gutenberg-University Mainz; Institut f\u00fcr Publizistik, Johannes Gutenberg-University Mainz; Institut f\u00fcr Publizistik, Johannes Gutenberg-University Mainz; Ubiquitous Knowledge Processing Lab, Technical University of Darmstadt", "aff_domain": ";;;;;", "email": ";;;;;", "github": "https://github.com/UKPLab/acl2021-label-suggestions-german-covid19", "project": "", "author_num": 6, "aff_unique_index": "0;0;1;1;1;0", "aff_unique_norm": "Technical University of Darmstadt;Johannes Gutenberg-University Mainz", "aff_unique_dep": "Ubiquitous Knowledge Processing Lab;Institut f\u00fcr Publizistik", "aff_unique_url": "https://www.tu-darmstadt.de;https://www.uni-mainz.de/", "aff_unique_abbr": "TUD;", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0;0;0;0;0;0", "aff_country_unique": "Germany" }, { "id": "2021.acl-long.167", "title": "IrEne: Interpretable Energy Prediction for Transformers", "track": "main", "status": "Long", "award": false, "abstract": "Existing software-based energy measurements of NLP models are not accurate because they do not consider the complex interactions between energy consumption and model execution. We present IrEne, an interpretable and extensible energy prediction system that accurately predicts the inference energy consumption of a wide range of Transformer-based NLP models. IrEne constructs a model tree graph that breaks down the NLP model into modules that are further broken down into low-level machine learning (ML) primitives. IrEne predicts the inference energy consumption of the ML primitives as a function of generalizable features and fine-grained runtime resource usage. IrEne then aggregates these low-level predictions recursively to predict the energy of each module and finally of the entire model. Experiments across multiple Transformer models show IrEne predicts inference energy consumption of transformer models with an error of under 7% compared to the ground truth. In contrast, existing energy models see an error of over 50%. We also show how IrEne can be used to conduct energy bottleneck analysis and to easily evaluate the energy impact of different architectural choices. We release the code and data at https://github.com/StonyBrookNLP/irene.", "author": "Qingqing Cao; Yash Kumar Lal; Harsh Trivedi; Aruna Balasubramanian; Niranjan Balasubramanian", "authorids": "/q/qingqing-cao/; /y/yash-kumar-lal/; /h/harsh-trivedi/; /a/aruna-balasubramanian/; /n/niranjan-balasubramanian/", "bibtex": "@inproceedings{cao-etal-2021-irene,\n title = \"{I}r{E}ne: Interpretable Energy Prediction for Transformers\",\n author = \"Cao, Qingqing and\n Lal, Yash Kumar and\n Trivedi, Harsh and\n Balasubramanian, Aruna and\n Balasubramanian, Niranjan\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.167/\",\n doi = \"10.18653/v1/2021.acl-long.167\",\n pages = \"2145--2157\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.167.pdf", "site": "https://aclanthology.org/2021.acl-long.167/", "pdf_size": 694153, "gs_citation": 20, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=16859243144292628542&as_sdt=5,44&sciodt=0,44&hl=en", "gs_version_total": 10, "aff": "Department of Computer Science, Stony Brook University; Department of Computer Science, Stony Brook University; Department of Computer Science, Stony Brook University; Department of Computer Science, Stony Brook University; Department of Computer Science, Stony Brook University", "aff_domain": "cs.stonybrook.edu;cs.stonybrook.edu;cs.stonybrook.edu;cs.stonybrook.edu;cs.stonybrook.edu", "email": "cs.stonybrook.edu;cs.stonybrook.edu;cs.stonybrook.edu;cs.stonybrook.edu;cs.stonybrook.edu", "github": "https://github.com/StonyBrookNLP/irene", "project": "", "author_num": 5, "aff_unique_index": "0;0;0;0;0", "aff_unique_norm": "Stony Brook University", "aff_unique_dep": "Department of Computer Science", "aff_unique_url": "https://www.stonybrook.edu", "aff_unique_abbr": "SBU", "aff_campus_unique_index": "0;0;0;0;0", "aff_campus_unique": "Stony Brook", "aff_country_unique_index": "0;0;0;0;0", "aff_country_unique": "United States" }, { "id": "2021.acl-short.17", "title": "Is Sparse Attention more Interpretable?", "track": "main", "status": "Short", "award": false, "abstract": "Sparse attention has been claimed to increase model interpretability under the assumption that it highlights influential inputs. Yet the attention distribution is typically over representations internal to the model rather than the inputs themselves, suggesting this assumption may not have merit. We build on the recent work exploring the interpretability of attention; we design a set of experiments to help us understand how sparsity affects our ability to use attention as an explainability tool. On three text classification tasks, we verify that only a weak relationship between inputs and co-indexed intermediate representations exists\u2014under sparse attention and otherwise. Further, we do not find any plausible mappings from sparse attention distributions to a sparse set of influential inputs through other avenues. Rather, we observe in this setting that inducing sparsity may make it less plausible that attention can be used as a tool for understanding model behavior.", "author": "Clara Meister; Stefan Lazov; Isabelle Augenstein; Ryan Cotterell", "authorids": "/c/clara-meister/; /s/stefan-lazov/; /i/isabelle-augenstein/; /r/ryan-cotterell/", "bibtex": "@inproceedings{meister-etal-2021-sparse,\n title = \"Is Sparse Attention more Interpretable?\",\n author = \"Meister, Clara and\n Lazov, Stefan and\n Augenstein, Isabelle and\n Cotterell, Ryan\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-short.17/\",\n doi = \"10.18653/v1/2021.acl-short.17\",\n pages = \"122--129\"\n}", "pdf": "https://aclanthology.org/2021.acl-short.17.pdf", "site": "https://aclanthology.org/2021.acl-short.17/", "pdf_size": 1087442, "gs_citation": 62, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=9861023286292618949&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 5, "aff": "ETH Z\u00fcrich; University of Cambridge; University of Copenhagen; ETH Z\u00fcrich + University of Cambridge", "aff_domain": "inf.ethz.ch;cantab.net;di.ku.dk;inf.ethz.ch", "email": "inf.ethz.ch;cantab.net;di.ku.dk;inf.ethz.ch", "github": "", "project": "", "author_num": 4, "aff_unique_index": "0;1;2;0+1", "aff_unique_norm": "ETH Z\u00fcrich;University of Cambridge;University of Copenhagen", "aff_unique_dep": ";;", "aff_unique_url": "https://www.ethz.ch;https://www.cam.ac.uk;https://www.ku.dk", "aff_unique_abbr": "ETHZ;Cambridge;UCPH", "aff_campus_unique_index": "1;1", "aff_campus_unique": ";Cambridge", "aff_country_unique_index": "0;1;2;0+1", "aff_country_unique": "Switzerland;United Kingdom;Denmark" }, { "id": "2021.acl-short.99", "title": "Issues with Entailment-based Zero-shot Text Classification", "track": "main", "status": "Short", "award": false, "abstract": "The general format of natural language inference (NLI) makes it tempting to be used for zero-shot text classification by casting any target label into a sentence of hypothesis and verifying whether or not it could be entailed by the input, aiming at generic classification applicable on any specified label space. In this opinion piece, we point out a few overlooked issues that are yet to be discussed in this line of work. We observe huge variance across different classification datasets amongst standard BERT-based NLI models and surprisingly find that pre-trained BERT without any fine-tuning can yield competitive performance against BERT fine-tuned for NLI. With the concern that these models heavily rely on spurious lexical patterns for prediction, we also experiment with preliminary approaches for more robust NLI, but the results are in general negative. Our observations reveal implicit but challenging difficulties in entailment-based zero-shot text classification.", "author": "Tingting Ma; Jin-Ge Yao; Chin-Yew Lin; Tiejun Zhao", "authorids": "/t/tingting-ma/; /j/jin-ge-yao/; /c/chin-yew-lin/; /t/tiejun-zhao/", "bibtex": "@inproceedings{ma-etal-2021-issues,\n title = \"Issues with Entailment-based Zero-shot Text Classification\",\n author = \"Ma, Tingting and\n Yao, Jin-Ge and\n Lin, Chin-Yew and\n Zhao, Tiejun\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-short.99/\",\n doi = \"10.18653/v1/2021.acl-short.99\",\n pages = \"786--796\"\n}", "pdf": "https://aclanthology.org/2021.acl-short.99.pdf", "site": "https://aclanthology.org/2021.acl-short.99/", "pdf_size": 346686, "gs_citation": 45, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=509712889987829975&as_sdt=5,33&sciodt=0,33&hl=en", "gs_version_total": 2, "aff": "Harbin Institute of Technology; Microsoft Research Asia; Microsoft Research Asia; Harbin Institute of Technology", "aff_domain": "gmail.com;microsoft.com;microsoft.com;hit.edu.cn", "email": "gmail.com;microsoft.com;microsoft.com;hit.edu.cn", "github": "", "project": "", "author_num": 4, "aff_unique_index": "0;1;1;0", "aff_unique_norm": "Harbin Institute of Technology;Microsoft Research", "aff_unique_dep": ";Research", "aff_unique_url": "http://www.hit.edu.cn/;https://www.microsoft.com/en-us/research/group/asia", "aff_unique_abbr": "HIT;MSR Asia", "aff_campus_unique_index": "0;1;1;0", "aff_campus_unique": "Harbin;Asia", "aff_country_unique_index": "0;0;0;0", "aff_country_unique": "China" }, { "id": "2021.acl-long.488", "title": "Joint Biomedical Entity and Relation Extraction with Knowledge-Enhanced Collective Inference", "track": "main", "status": "Long", "award": false, "abstract": "Compared to the general news domain, information extraction (IE) from biomedical text requires much broader domain knowledge. However, many previous IE methods do not utilize any external knowledge during inference. Due to the exponential growth of biomedical publications, models that do not go beyond their fixed set of parameters will likely fall behind. Inspired by how humans look up relevant information to comprehend a scientific text, we present a novel framework that utilizes external knowledge for joint entity and relation extraction named KECI (Knowledge-Enhanced Collective Inference). Given an input text, KECI first constructs an initial span graph representing its initial understanding of the text. It then uses an entity linker to form a knowledge graph containing relevant background knowledge for the the entity mentions in the text. To make the final predictions, KECI fuses the initial span graph and the knowledge graph into a more refined graph using an attention mechanism. KECI takes a collective approach to link mention spans to entities by integrating global relational information into local representations using graph convolutional networks. Our experimental results show that the framework is highly effective, achieving new state-of-the-art results in two different benchmark datasets: BioRelEx (binding interaction detection) and ADE (adverse drug event extraction). For example, KECI achieves absolute improvements of 4.59% and 4.91% in F1 scores over the state-of-the-art on the BioRelEx entity and relation extraction tasks", "author": "Tuan Lai; Heng Ji; ChengXiang Zhai; Quan Hung Tran", "authorids": "/t/tuan-lai/; /h/heng-ji/; /c/chengxiang-zhai/; /q/quan-hung-tran/", "bibtex": "@inproceedings{lai-etal-2021-joint,\n title = \"Joint Biomedical Entity and Relation Extraction with Knowledge-Enhanced Collective Inference\",\n author = \"Lai, Tuan and\n Ji, Heng and\n Zhai, ChengXiang and\n Tran, Quan Hung\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.488/\",\n doi = \"10.18653/v1/2021.acl-long.488\",\n pages = \"6248--6260\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.488.pdf", "site": "https://aclanthology.org/2021.acl-long.488/", "pdf_size": 632552, "gs_citation": 83, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=14063568728842893732&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 8, "aff": "University of Illinois at Urbana-Champaign; University of Illinois at Urbana-Champaign; University of Illinois at Urbana-Champaign; Adobe Research", "aff_domain": "illinois.edu;illinois.edu;illinois.edu;adobe.com", "email": "illinois.edu;illinois.edu;illinois.edu;adobe.com", "github": "https://github.com/laituan245/bio_relex", "project": "", "author_num": 4, "aff_unique_index": "0;0;0;1", "aff_unique_norm": "University of Illinois at Urbana-Champaign;Adobe", "aff_unique_dep": ";Adobe Research", "aff_unique_url": "https://illinois.edu;https://research.adobe.com", "aff_unique_abbr": "UIUC;Adobe", "aff_campus_unique_index": "0;0;0", "aff_campus_unique": "Urbana-Champaign;", "aff_country_unique_index": "0;0;0;0", "aff_country_unique": "United States" }, { "id": "2021.acl-long.252", "title": "Joint Models for Answer Verification in Question Answering Systems", "track": "main", "status": "Long", "award": false, "abstract": "This paper studies joint models for selecting correct answer sentences among the top k provided by answer sentence selection (AS2) modules, which are core components of retrieval-based Question Answering (QA) systems. Our work shows that a critical step to effectively exploiting an answer set regards modeling the interrelated information between pair of answers. For this purpose, we build a three-way multi-classifier, which decides if an answer supports, refutes, or is neutral with respect to another one. More specifically, our neural architecture integrates a state-of-the-art AS2 module with the multi-classifier, and a joint layer connecting all components. We tested our models on WikiQA, TREC-QA, and a real-world dataset. The results show that our models obtain the new state of the art in AS2.", "author": "Zeyu Zhang; Thuy Vu; Alessandro Moschitti", "authorids": "/z/zeyu-zhang/; /t/thuy-vu/; /a/alessandro-moschitti/", "bibtex": "@inproceedings{zhang-etal-2021-joint,\n title = \"Joint Models for Answer Verification in Question Answering Systems\",\n author = \"Zhang, Zeyu and\n Vu, Thuy and\n Moschitti, Alessandro\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.252/\",\n doi = \"10.18653/v1/2021.acl-long.252\",\n pages = \"3252--3262\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.252.pdf", "site": "https://aclanthology.org/2021.acl-long.252/", "pdf_size": 601549, "gs_citation": 29, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=11529302928387594908&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 7, "aff": "School of Information, The University of Arizona, Tucson, AZ, USA; Amazon Alexa AI, Manhattan Beach, CA, USA; Amazon Alexa AI, Manhattan Beach, CA, USA", "aff_domain": "email.arizona.edu;amazon.com;amazon.com", "email": "email.arizona.edu;amazon.com;amazon.com", "github": "", "project": "", "author_num": 3, "aff_unique_index": "0;1;1", "aff_unique_norm": "The University of Arizona;Amazon Alexa AI", "aff_unique_dep": "School of Information;AI", "aff_unique_url": "https://www.arizona.edu;https://www.amazon.com/alexa", "aff_unique_abbr": "UArizona;Amazon Alexa AI", "aff_campus_unique_index": "0;1;1", "aff_campus_unique": "Tucson;Manhattan Beach", "aff_country_unique_index": "0;0;0", "aff_country_unique": "United States" }, { "id": "2021.acl-long.529", "title": "Joint Verification and Reranking for Open Fact Checking Over Tables", "track": "main", "status": "Long", "award": false, "abstract": "Structured information is an important knowledge source for automatic verification of factual claims. Nevertheless, the majority of existing research into this task has focused on textual data, and the few recent inquiries into structured data have been for the closed-domain setting where appropriate evidence for each claim is assumed to have already been retrieved. In this paper, we investigate verification over structured data in the open-domain setting, introducing a joint reranking-and-verification model which fuses evidence documents in the verification component. Our open-domain model achieves performance comparable to the closed-domain state-of-the-art on the TabFact dataset, and demonstrates performance gains from the inclusion of multiple tables as well as a significant improvement over a heuristic retrieval baseline.", "author": "Michael Sejr Schlichtkrull; Vladimir Karpukhin; Barlas Oguz; Mike Lewis; Wen-tau Yih; Sebastian Riedel", "authorids": "/m/michael-schlichtkrull/; /v/vladimir-karpukhin/; /b/barlas-oguz/; /m/mike-lewis/; /w/wen-tau-yih/; /s/sebastian-riedel/", "bibtex": "@inproceedings{schlichtkrull-etal-2021-joint,\n title = \"Joint Verification and Reranking for Open Fact Checking Over Tables\",\n author = \"Schlichtkrull, Michael Sejr and\n Karpukhin, Vladimir and\n Oguz, Barlas and\n Lewis, Mike and\n Yih, Wen-tau and\n Riedel, Sebastian\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.529/\",\n doi = \"10.18653/v1/2021.acl-long.529\",\n pages = \"6787--6799\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.529.pdf", "site": "https://aclanthology.org/2021.acl-long.529/", "pdf_size": 476438, "gs_citation": 29, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=12921124975644835397&as_sdt=5,44&sciodt=0,44&hl=en", "gs_version_total": 6, "aff": "University of Cambridge; Facebook AI Research; Facebook AI Research; Facebook AI Research; Facebook AI Research; Facebook AI Research + University College London", "aff_domain": "cst.cam.ac.uk;fb.com;fb.com;fb.com;fb.com;fb.com", "email": "cst.cam.ac.uk;fb.com;fb.com;fb.com;fb.com;fb.com", "github": "", "project": "", "author_num": 6, "aff_unique_index": "0;1;1;1;1;1+2", "aff_unique_norm": "University of Cambridge;Facebook;University College London", "aff_unique_dep": ";Facebook AI Research;", "aff_unique_url": "https://www.cam.ac.uk;https://research.facebook.com;https://www.ucl.ac.uk", "aff_unique_abbr": "Cambridge;FAIR;UCL", "aff_campus_unique_index": "0;", "aff_campus_unique": "Cambridge;", "aff_country_unique_index": "0;1;1;1;1;1+0", "aff_country_unique": "United Kingdom;United States" }, { "id": "2021.acl-long.196", "title": "KACE: Generating Knowledge Aware Contrastive Explanations for Natural Language Inference", "track": "main", "status": "Long", "award": false, "abstract": "In order to better understand the reason behind model behaviors (i.e., making predictions), most recent works have exploited generative models to provide complementary explanations. However, existing approaches in NLP mainly focus on \u201cWHY A\u201d rather than contrastive \u201cWHY A NOT B\u201d, which is shown to be able to better distinguish confusing candidates and improve data efficiency in other research fields. In this paper, we focus on generating contrastive explanations with counterfactual examples in NLI and propose a novel Knowledge-Aware Contrastive Explanation generation framework (KACE).Specifically, we first identify rationales (i.e., key phrases) from input sentences, and use them as key perturbations for generating counterfactual examples. After obtaining qualified counterfactual examples, we take them along with original examples and external knowledge as input, and employ a knowledge-aware generative pre-trained language model to generate contrastive explanations. Experimental results show that contrastive explanations are beneficial to fit the scenarios by clarifying the difference between the predicted answer and other possible wrong ones. Moreover, we train an NLI model enhanced with contrastive explanations and achieves an accuracy of 91.9% on SNLI, gaining improvements of 5.7% against ETPA (\u201cExplain-Then-Predict-Attention\u201d) and 0.6% against NILE (\u201cWHY A\u201d).", "author": "Qianglong Chen; Feng Ji; Xiangji Zeng; Feng-Lin Li; Ji Zhang; Haiqing Chen; Yin Zhang", "authorids": "/q/qianglong-chen/; /f/feng-ji/; /x/xiangji-zeng/; /f/feng-lin-li/; /j/ji-zhang/; /h/haiqing-chen/; /y/yin-zhang/", "bibtex": "@inproceedings{chen-etal-2021-kace,\n title = \"{KACE}: Generating Knowledge Aware Contrastive Explanations for Natural Language Inference\",\n author = \"Chen, Qianglong and\n Ji, Feng and\n Zeng, Xiangji and\n Li, Feng-Lin and\n Zhang, Ji and\n Chen, Haiqing and\n Zhang, Yin\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.196/\",\n doi = \"10.18653/v1/2021.acl-long.196\",\n pages = \"2516--2527\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.196.pdf", "site": "https://aclanthology.org/2021.acl-long.196/", "pdf_size": 591457, "gs_citation": 30, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=8146187154761716853&as_sdt=20000005&sciodt=0,21&hl=en", "gs_version_total": 4, "aff": "College of Computer Science and Technology, Zhejiang University, China + DAMO Academy, Alibaba Group, China; Tencent, China; College of Computer Science and Technology, Zhejiang University, China; DAMO Academy, Alibaba Group, China; DAMO Academy, Alibaba Group, China; DAMO Academy, Alibaba Group, China; College of Computer Science and Technology, Zhejiang University, China", "aff_domain": "zju.edu.cn; ;zju.edu.cn;alibaba-inc.com;alibaba-inc.com;alibaba-inc.com;zju.edu.cn", "email": "zju.edu.cn; ;zju.edu.cn;alibaba-inc.com;alibaba-inc.com;alibaba-inc.com;zju.edu.cn", "github": "", "project": "", "author_num": 7, "aff_unique_index": "0+1;2;0;1;1;1;0", "aff_unique_norm": "Zhejiang University;Alibaba Group;Tencent", "aff_unique_dep": "College of Computer Science and Technology;DAMO Academy;", "aff_unique_url": "http://www.zju.edu.cn;https://www.alibaba.com;https://www.tencent.com", "aff_unique_abbr": "ZJU;Alibaba;Tencent", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0+0;0;0;0;0;0;0", "aff_country_unique": "China" }, { "id": "2021.acl-long.44", "title": "KM-BART: Knowledge Enhanced Multimodal BART for Visual Commonsense Generation", "track": "main", "status": "Long", "award": false, "abstract": "We present Knowledge Enhanced Multimodal BART (KM-BART), which is a Transformer-based sequence-to-sequence model capable of reasoning about commonsense knowledge from multimodal inputs of images and texts. We adapt the generative BART architecture (Lewis et al., 2020) to a multimodal model with visual and textual inputs. We further develop novel pretraining tasks to improve the model performance on the Visual Commonsense Generation (VCG) task. In particular, our pretraining task of Knowledge-based Commonsense Generation (KCG) boosts model performance on the VCG task by leveraging commonsense knowledge from a large language model pretrained on external commonsense knowledge graphs. To the best of our knowledge, we are the first to propose a dedicated task for improving model performance on the VCG task. Experimental results show that our model reaches state-of-the-art performance on the VCG task (Park et al., 2020) by applying these novel pretraining tasks.", "author": "Yiran Xing; Zai Shi; Zhao Meng; Gerhard Lakemeyer; Yunpu Ma; Roger Wattenhofer", "authorids": "/y/yiran-xing/; /z/zai-shi/; /z/zhao-meng/; /g/gerhard-lakemeyer/; /y/yunpu-ma/; /r/roger-wattenhofer/", "bibtex": "@inproceedings{xing-etal-2021-km,\n title = \"{KM}-{BART}: Knowledge Enhanced Multimodal {BART} for Visual Commonsense Generation\",\n author = \"Xing, Yiran and\n Shi, Zai and\n Meng, Zhao and\n Lakemeyer, Gerhard and\n Ma, Yunpu and\n Wattenhofer, Roger\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.44/\",\n doi = \"10.18653/v1/2021.acl-long.44\",\n pages = \"525--535\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.44.pdf", "site": "https://aclanthology.org/2021.acl-long.44/", "pdf_size": 1964697, "gs_citation": 52, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=14230053412011807332&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 12, "aff": "RWTH Aachen, Germany; ETH Zurich, Switzerland; ETH Zurich, Switzerland; RWTH Aachen, Germany; LMU Munich, Germany; ETH Zurich, Switzerland", "aff_domain": "rwth-aachen.de;ethz.ch;ethz.ch;rwth-aachen.de;gmail.com;ethz.ch", "email": "rwth-aachen.de;ethz.ch;ethz.ch;rwth-aachen.de;gmail.com;ethz.ch", "github": "https://github.com/FomalhautB/KM-BART-ACL", "project": "", "author_num": 6, "aff_unique_index": "0;1;1;0;2;1", "aff_unique_norm": "RWTH Aachen University;ETH Zurich;Ludwig Maximilian University of Munich", "aff_unique_dep": ";;", "aff_unique_url": "https://www.rwth-aachen.de;https://www.ethz.ch;https://www.lmu.de", "aff_unique_abbr": "RWTH;ETHZ;LMU", "aff_campus_unique_index": "1", "aff_campus_unique": ";Munich", "aff_country_unique_index": "0;1;1;0;0;1", "aff_country_unique": "Germany;Switzerland" }, { "id": "2021.acl-long.176", "title": "KaggleDBQA: Realistic Evaluation of Text-to-SQL Parsers", "track": "main", "status": "Long", "award": false, "abstract": "The goal of database question answering is to enable natural language querying of real-life relational databases in diverse application domains. Recently, large-scale datasets such as Spider and WikiSQL facilitated novel modeling techniques for text-to-SQL parsing, improving zero-shot generalization to unseen databases. In this work, we examine the challenges that still prevent these techniques from practical deployment. First, we present KaggleDBQA, a new cross-domain evaluation dataset of real Web databases, with domain-specific data types, original formatting, and unrestricted questions. Second, we re-examine the choice of evaluation tasks for text-to-SQL parsers as applied in real-life settings. Finally, we augment our in-domain evaluation task with database documentation, a naturally occurring source of implicit domain knowledge. We show that KaggleDBQA presents a challenge to state-of-the-art zero-shot parsers but a more realistic evaluation setting and creative use of associated database documentation boosts their accuracy by over 13.2%, doubling their performance.", "author": "Chia-Hsuan Lee; Oleksandr Polozov; Matthew Richardson", "authorids": "/c/chia-hsuan-lee/; /o/oleksandr-polozov/; /m/matthew-richardson/", "bibtex": "@inproceedings{lee-etal-2021-kaggledbqa,\n title = \"{K}aggle{DBQA}: Realistic Evaluation of Text-to-{SQL} Parsers\",\n author = \"Lee, Chia-Hsuan and\n Polozov, Oleksandr and\n Richardson, Matthew\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.176/\",\n doi = \"10.18653/v1/2021.acl-long.176\",\n pages = \"2261--2273\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.176.pdf", "site": "https://aclanthology.org/2021.acl-long.176/", "pdf_size": 395849, "gs_citation": 103, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=15568057972205918843&as_sdt=5,33&sciodt=0,33&hl=en", "gs_version_total": 4, "aff": "University of Washington; Microsoft Research, Redmond; Microsoft Research, Redmond", "aff_domain": "uw.edu;microsoft.com;microsoft.com", "email": "uw.edu;microsoft.com;microsoft.com", "github": "", "project": "", "author_num": 3, "aff_unique_index": "0;1;1", "aff_unique_norm": "University of Washington;Microsoft Research", "aff_unique_dep": ";", "aff_unique_url": "https://www.washington.edu;https://www.microsoft.com/en-us/research", "aff_unique_abbr": "UW;MSR", "aff_campus_unique_index": "1;1", "aff_campus_unique": ";Redmond", "aff_country_unique_index": "0;0;0", "aff_country_unique": "United States" }, { "id": "2021.acl-long.498", "title": "Keep It Simple: Unsupervised Simplification of Multi-Paragraph Text", "track": "main", "status": "Long", "award": false, "abstract": "This work presents Keep it Simple (KiS), a new approach to unsupervised text simplification which learns to balance a reward across three properties: fluency, salience and simplicity. We train the model with a novel algorithm to optimize the reward (k-SCST), in which the model proposes several candidate simplifications, computes each candidate\u2019s reward, and encourages candidates that outperform the mean reward. Finally, we propose a realistic text comprehension task as an evaluation method for text simplification. When tested on the English news domain, the KiS model outperforms strong supervised baselines by more than 4 SARI points, and can help people complete a comprehension task an average of 18% faster while retaining accuracy, when compared to the original text.", "author": "Philippe Laban; Tobias Schnabel; Paul Bennett; Marti A. Hearst", "authorids": "/p/philippe-laban/; /t/tobias-schnabel/; /p/paul-bennett/; /m/marti-a-hearst/", "bibtex": "@inproceedings{laban-etal-2021-keep,\n title = \"Keep It Simple: Unsupervised Simplification of Multi-Paragraph Text\",\n author = \"Laban, Philippe and\n Schnabel, Tobias and\n Bennett, Paul and\n Hearst, Marti A.\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.498/\",\n doi = \"10.18653/v1/2021.acl-long.498\",\n pages = \"6365--6378\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.498.pdf", "site": "https://aclanthology.org/2021.acl-long.498/", "pdf_size": 881201, "gs_citation": 70, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=4857541191756909402&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 6, "aff": "UC Berkeley; Microsoft; Microsoft; UC Berkeley", "aff_domain": "berkeley.edu;microsoft.com;microsoft.com;berkeley.edu", "email": "berkeley.edu;microsoft.com;microsoft.com;berkeley.edu", "github": "", "project": "", "author_num": 4, "aff_unique_index": "0;1;1;0", "aff_unique_norm": "University of California, Berkeley;Microsoft Corporation", "aff_unique_dep": ";", "aff_unique_url": "https://www.berkeley.edu;https://www.microsoft.com", "aff_unique_abbr": "UC Berkeley;Microsoft", "aff_campus_unique_index": "0;0", "aff_campus_unique": "Berkeley;", "aff_country_unique_index": "0;0;0;0", "aff_country_unique": "United States" }, { "id": "2021.acl-long.278", "title": "Knowing the No-match: Entity Alignment with Dangling Cases", "track": "main", "status": "Long", "award": false, "abstract": "This paper studies a new problem setting of entity alignment for knowledge graphs (KGs). Since KGs possess different sets of entities, there could be entities that cannot find alignment across them, leading to the problem of dangling entities. As the first attempt to this problem, we construct a new dataset and design a multi-task learning framework for both entity alignment and dangling entity detection. The framework can opt to abstain from predicting alignment for the detected dangling entities. We propose three techniques for dangling entity detection that are based on the distribution of nearest-neighbor distances, i.e., nearest neighbor classification, marginal ranking and background ranking. After detecting and removing dangling entities, an incorporated entity alignment model in our framework can provide more robust alignment for remaining entities. Comprehensive experiments and analyses demonstrate the effectiveness of our framework. We further discover that the dangling entity detection module can, in turn, improve alignment learning and the final performance. The contributed resource is publicly available to foster further research.", "author": "Zequn Sun; Muhao Chen; Wei Hu", "authorids": "/z/zequn-sun/; /m/muhao-chen/; /w/wei-hu/", "bibtex": "@inproceedings{sun-etal-2021-knowing,\n title = \"Knowing the No-match: Entity Alignment with Dangling Cases\",\n author = \"Sun, Zequn and\n Chen, Muhao and\n Hu, Wei\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.278/\",\n doi = \"10.18653/v1/2021.acl-long.278\",\n pages = \"3582--3593\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.278.pdf", "site": "https://aclanthology.org/2021.acl-long.278/", "pdf_size": 336909, "gs_citation": 35, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=15352900141991935461&as_sdt=5,33&sciodt=0,33&hl=en", "gs_version_total": 6, "aff": "State Key Laboratory for Novel Software Technology, Nanjing University, China; Department of Computer Science, University of Southern California, USA+Information Sciences Institute, University of Southern California, USA; State Key Laboratory for Novel Software Technology, Nanjing University, China", "aff_domain": "gmail.com;usc.edu;nju.edu.cn", "email": "gmail.com;usc.edu;nju.edu.cn", "github": "", "project": "", "author_num": 3, "aff_unique_index": "0;1+1;0", "aff_unique_norm": "Nanjing University;University of Southern California", "aff_unique_dep": "State Key Laboratory for Novel Software Technology;Department of Computer Science", "aff_unique_url": "http://www.nju.edu.cn;https://www.usc.edu", "aff_unique_abbr": "Nanjing U;USC", "aff_campus_unique_index": "1+2", "aff_campus_unique": ";Los Angeles;USA", "aff_country_unique_index": "0;1+1;0", "aff_country_unique": "China;United States" }, { "id": "2021.acl-long.376", "title": "Knowledge-Enriched Event Causality Identification via Latent Structure Induction Networks", "track": "main", "status": "Long", "award": false, "abstract": "Identifying causal relations of events is an important task in natural language processing area. However, the task is very challenging, because event causality is usually expressed in diverse forms that often lack explicit causal clues. Existing methods cannot handle well the problem, especially in the condition of lacking training data. Nonetheless, humans can make a correct judgement based on their background knowledge, including descriptive knowledge and relational knowledge. Inspired by it, we propose a novel Latent Structure Induction Network (LSIN) to incorporate the external structural knowledge into this task. Specifically, to make use of the descriptive knowledge, we devise a Descriptive Graph Induction module to obtain and encode the graph-structured descriptive knowledge. To leverage the relational knowledge, we propose a Relational Graph Induction module which is able to automatically learn a reasoning structure for event causality reasoning. Experimental results on two widely used datasets indicate that our approach significantly outperforms previous state-of-the-art methods.", "author": "Pengfei Cao; Xinyu Zuo; Yubo Chen; Kang Liu; Jun Zhao; Yuguang Chen; Weihua Peng", "authorids": "/p/pengfei-cao/; /x/xinyu-zuo/; /y/yubo-chen/; /k/kang-liu/; /j/jun-zhao/; /y/yuguang-chen/; /w/weihua-peng/", "bibtex": "@inproceedings{cao-etal-2021-knowledge,\n title = \"Knowledge-Enriched Event Causality Identification via Latent Structure Induction Networks\",\n author = \"Cao, Pengfei and\n Zuo, Xinyu and\n Chen, Yubo and\n Liu, Kang and\n Zhao, Jun and\n Chen, Yuguang and\n Peng, Weihua\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.376/\",\n doi = \"10.18653/v1/2021.acl-long.376\",\n pages = \"4862--4872\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.376.pdf", "site": "https://aclanthology.org/2021.acl-long.376/", "pdf_size": 444251, "gs_citation": 80, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=6107455200838324043&as_sdt=5,33&sciodt=0,33&hl=en", "gs_version_total": 4, "aff": "National Laboratory of Pattern Recognition, Institute of Automation, CAS, Beijing, China+School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China; National Laboratory of Pattern Recognition, Institute of Automation, CAS, Beijing, China+School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China; National Laboratory of Pattern Recognition, Institute of Automation, CAS, Beijing, China+School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China; National Laboratory of Pattern Recognition, Institute of Automation, CAS, Beijing, China+School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China; National Laboratory of Pattern Recognition, Institute of Automation, CAS, Beijing, China+School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China; Beijing Baidu Netcom Science Technology Co., Ltd; Beijing Baidu Netcom Science Technology Co., Ltd", "aff_domain": "nlpr.ia.ac.cn;nlpr.ia.ac.cn;nlpr.ia.ac.cn;nlpr.ia.ac.cn;nlpr.ia.ac.cn;baidu.com;baidu.com", "email": "nlpr.ia.ac.cn;nlpr.ia.ac.cn;nlpr.ia.ac.cn;nlpr.ia.ac.cn;nlpr.ia.ac.cn;baidu.com;baidu.com", "github": "", "project": "", "author_num": 7, "aff_unique_index": "0+1;0+1;0+1;0+1;0+1;2;2", "aff_unique_norm": "National Laboratory of Pattern Recognition;University of Chinese Academy of Sciences;Baidu", "aff_unique_dep": "Institute of Automation;School of Artificial Intelligence;Baidu Netcom Science Technology", "aff_unique_url": ";http://www.ucas.ac.cn;https://www.baidu.com", "aff_unique_abbr": ";UCAS;Baidu", "aff_campus_unique_index": "0+0;0+0;0+0;0+0;0+0", "aff_campus_unique": "Beijing;", "aff_country_unique_index": "0+0;0+0;0+0;0+0;0+0;0;0", "aff_country_unique": "China" }, { "id": "2021.acl-long.146", "title": "Knowledgeable or Educated Guess? Revisiting Language Models as Knowledge Bases", "track": "main", "status": "Long", "award": false, "abstract": "Previous literatures show that pre-trained masked language models (MLMs) such as BERT can achieve competitive factual knowledge extraction performance on some datasets, indicating that MLMs can potentially be a reliable knowledge source. In this paper, we conduct a rigorous study to explore the underlying predicting mechanisms of MLMs over different extraction paradigms. By investigating the behaviors of MLMs, we find that previous decent performance mainly owes to the biased prompts which overfit dataset artifacts. Furthermore, incorporating illustrative cases and external contexts improve knowledge prediction mainly due to entity type guidance and golden answer leakage. Our findings shed light on the underlying predicting mechanisms of MLMs, and strongly question the previous conclusion that current MLMs can potentially serve as reliable factual knowledge bases.", "author": "Boxi Cao; Hongyu Lin; Xianpei Han; Le Sun; Lingyong Yan; Meng Liao; Tong Xue; Jin Xu", "authorids": "/b/boxi-cao/; /h/hongyu-lin/; /x/xianpei-han/; /l/le-sun/; /l/lingyong-yan/; /m/meng-liao/; /t/tong-xue/; /j/jin-xu/", "bibtex": "@inproceedings{cao-etal-2021-knowledgeable,\n title = \"Knowledgeable or Educated Guess? Revisiting Language Models as Knowledge Bases\",\n author = \"Cao, Boxi and\n Lin, Hongyu and\n Han, Xianpei and\n Sun, Le and\n Yan, Lingyong and\n Liao, Meng and\n Xue, Tong and\n Xu, Jin\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.146/\",\n doi = \"10.18653/v1/2021.acl-long.146\",\n pages = \"1860--1874\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.146.pdf", "site": "https://aclanthology.org/2021.acl-long.146/", "pdf_size": 512560, "gs_citation": 150, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=16154311984752580828&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 9, "aff": "Chinese Information Processing Laboratory+University of Chinese Academy of Sciences; Chinese Information Processing Laboratory+State Key Laboratory of Computer Science; Chinese Information Processing Laboratory+State Key Laboratory of Computer Science; Chinese Information Processing Laboratory+State Key Laboratory of Computer Science; Chinese Information Processing Laboratory+University of Chinese Academy of Sciences; Data Quality Team, WeChat, Tencent Inc.; Data Quality Team, WeChat, Tencent Inc.; Data Quality Team, WeChat, Tencent Inc.", "aff_domain": "iscas.ac.cn;iscas.ac.cn;iscas.ac.cn;iscas.ac.cn;iscas.ac.cn;tencent.com;tencent.com;tencent.com", "email": "iscas.ac.cn;iscas.ac.cn;iscas.ac.cn;iscas.ac.cn;iscas.ac.cn;tencent.com;tencent.com;tencent.com", "github": "https://github.com/c-box/LANKA", "project": "", "author_num": 8, "aff_unique_index": "0+1;0+2;0+2;0+2;0+1;3;3;3", "aff_unique_norm": "Chinese Information Processing Laboratory;University of Chinese Academy of Sciences;State Key Laboratory of Computer Science;Tencent Inc.", "aff_unique_dep": "Information Processing;;;Data Quality Team", "aff_unique_url": ";http://www.ucas.ac.cn;;https://www.tencent.com", "aff_unique_abbr": ";UCAS;;Tencent", "aff_campus_unique_index": ";;;;", "aff_campus_unique": "", "aff_country_unique_index": "0+0;0+0;0+0;0+0;0+0;0;0;0", "aff_country_unique": "China" }, { "id": "2021.acl-long.198", "title": "LGESQL: Line Graph Enhanced Text-to-SQL Model with Mixed Local and Non-Local Relations", "track": "main", "status": "Long", "award": false, "abstract": "This work aims to tackle the challenging heterogeneous graph encoding problem in the text-to-SQL task. Previous methods are typically node-centric and merely utilize different weight matrices to parameterize edge types, which 1) ignore the rich semantics embedded in the topological structure of edges, and 2) fail to distinguish local and non-local relations for each node. To this end, we propose a Line Graph Enhanced Text-to-SQL (LGESQL) model to mine the underlying relational features without constructing meta-paths. By virtue of the line graph, messages propagate more efficiently through not only connections between nodes, but also the topology of directed edges. Furthermore, both local and non-local relations are integrated distinctively during the graph iteration. We also design an auxiliary task called graph pruning to improve the discriminative capability of the encoder. Our framework achieves state-of-the-art results (62.8% with Glove, 72.0% with Electra) on the cross-domain text-to-SQL benchmark Spider at the time of writing.", "author": "Ruisheng Cao; Lu Chen; Zhi Chen; Yanbin Zhao; Su Zhu; Kai Yu", "authorids": "/r/ruisheng-cao/; /l/lu-chen/; /z/zhi-chen/; /y/yanbin-zhao/; /s/su-zhu/; /k/kai-yu/", "bibtex": "@inproceedings{cao-etal-2021-lgesql,\n title = \"{LGESQL}: Line Graph Enhanced Text-to-{SQL} Model with Mixed Local and Non-Local Relations\",\n author = \"Cao, Ruisheng and\n Chen, Lu and\n Chen, Zhi and\n Zhao, Yanbin and\n Zhu, Su and\n Yu, Kai\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.198/\",\n doi = \"10.18653/v1/2021.acl-long.198\",\n pages = \"2541--2555\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.198.pdf", "site": "https://aclanthology.org/2021.acl-long.198/", "pdf_size": 1076941, "gs_citation": 167, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=5632670516042205492&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 6, "aff": "X-LANCE Lab, Department of Computer Science and Engineering, MoE Key Lab of Artificial Intelligence, AI Institute, Shanghai Jiao Tong University, Shanghai, China; X-LANCE Lab, Department of Computer Science and Engineering, MoE Key Lab of Artificial Intelligence, AI Institute, Shanghai Jiao Tong University, Shanghai, China + State Key Lab of Media Convergence Production Technology and Systems, Beijing, China; X-LANCE Lab, Department of Computer Science and Engineering, MoE Key Lab of Artificial Intelligence, AI Institute, Shanghai Jiao Tong University, Shanghai, China; X-LANCE Lab, Department of Computer Science and Engineering, MoE Key Lab of Artificial Intelligence, AI Institute, Shanghai Jiao Tong University, Shanghai, China; AISpeech Co., Ltd., Suzhou, China; X-LANCE Lab, Department of Computer Science and Engineering, MoE Key Lab of Artificial Intelligence, AI Institute, Shanghai Jiao Tong University, Shanghai, China + State Key Lab of Media Convergence Production Technology and Systems, Beijing, China", "aff_domain": "sjtu.edu.cn;sjtu.edu.cn;sjtu.edu.cn; ; ; ", "email": "sjtu.edu.cn;sjtu.edu.cn;sjtu.edu.cn; ; ; ", "github": "", "project": "", "author_num": 6, "aff_unique_index": "0;0+1;0;0;2;0+1", "aff_unique_norm": "Shanghai Jiao Tong University;State Key Lab of Media Convergence Production Technology and Systems;AISpeech Co., Ltd.", "aff_unique_dep": "Department of Computer Science and Engineering;;", "aff_unique_url": "https://www.sjtu.edu.cn;;", "aff_unique_abbr": "SJTU;;", "aff_campus_unique_index": "0;0;0;0;0", "aff_campus_unique": "Shanghai;", "aff_country_unique_index": "0;0+0;0;0;0;0+0", "aff_country_unique": "China" }, { "id": "2021.acl-long.64", "title": "LNN-EL: A Neuro-Symbolic Approach to Short-text Entity Linking", "track": "main", "status": "Long", "award": false, "abstract": "Entity linking (EL) is the task of disambiguating mentions appearing in text by linking them to entities in a knowledge graph, a crucial task for text understanding, question answering or conversational systems. In the special case of short-text EL, which poses additional challenges due to limited context, prior approaches have reached good performance by employing heuristics-based methods or purely neural approaches. Here, we take a different, neuro-symbolic approach that combines the advantages of using interpretable rules based on first-order logic with the performance of neural learning. Even though constrained to use rules, we show that we reach competitive or better performance with SoTA black-box neural approaches. Furthermore, our framework has the benefits of extensibility and transferability. We show that we can easily blend existing rule templates given by a human expert, with multiple types of features (priors, BERT encodings, box embeddings, etc), and even with scores resulting from previous EL methods, thus improving on such methods. As an example of improvement, on the LC-QuAD-1.0 dataset, we show more than 3% increase in F1 score relative to previous SoTA. Finally, we show that the inductive bias offered by using logic results in a set of learned rules that transfers from one dataset to another, sometimes without finetuning, while still having high accuracy.", "author": "Hang Jiang; Sairam Gurajada; Qiuhao Lu; Sumit Neelam; Lucian Popa; Prithviraj Sen; Yunyao Li; Alexander Gray", "authorids": "/h/hang-jiang/; /s/sairam-gurajada/; /q/qiuhao-lu/; /s/sumit-neelam/; /l/lucian-popa/; /p/prithviraj-sen/; /y/yunyao-li/; /a/alexander-gray/", "bibtex": "@inproceedings{jiang-etal-2021-lnn,\n title = \"{LNN}-{EL}: A Neuro-Symbolic Approach to Short-text Entity Linking\",\n author = \"Jiang, Hang and\n Gurajada, Sairam and\n Lu, Qiuhao and\n Neelam, Sumit and\n Popa, Lucian and\n Sen, Prithviraj and\n Li, Yunyao and\n Gray, Alexander\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.64/\",\n doi = \"10.18653/v1/2021.acl-long.64\",\n pages = \"775--787\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.64.pdf", "site": "https://aclanthology.org/2021.acl-long.64/", "pdf_size": 623164, "gs_citation": 36, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=11057174330447568561&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 7, "aff": "MIT+IBM Research; IBM Research; University of Oregon; IBM Research; IBM Research; IBM Research; IBM Research; IBM Research", "aff_domain": "mit.edu;ibm.com;cs.uoregon.edu;in.ibm.com;us.ibm.com;us.ibm.com;us.ibm.com;ibm.com", "email": "mit.edu;ibm.com;cs.uoregon.edu;in.ibm.com;us.ibm.com;us.ibm.com;us.ibm.com;ibm.com", "github": "", "project": "", "author_num": 8, "aff_unique_index": "0+1;1;2;1;1;1;1;1", "aff_unique_norm": "Massachusetts Institute of Technology;IBM;University of Oregon", "aff_unique_dep": ";IBM Research;", "aff_unique_url": "https://web.mit.edu;https://www.ibm.com/research;https://www.uoregon.edu", "aff_unique_abbr": "MIT;IBM;UO", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0+0;0;0;0;0;0;0;0", "aff_country_unique": "United States" }, { "id": "2021.acl-long.298", "title": "Label-Specific Dual Graph Neural Network for Multi-Label Text Classification", "track": "main", "status": "Long", "award": false, "abstract": "Multi-label text classification is one of the fundamental tasks in natural language processing. Previous studies have difficulties to distinguish similar labels well because they learn the same document representations for different labels, that is they do not explicitly extract label-specific semantic components from documents. Moreover, they do not fully explore the high-order interactions among these semantic components, which is very helpful to predict tail labels. In this paper, we propose a novel label-specific dual graph neural network (LDGN), which incorporates category information to learn label-specific components from documents, and employs dual Graph Convolution Network (GCN) to model complete and adaptive interactions among these components based on the statistical label co-occurrence and dynamic reconstruction graph in a joint way. Experimental results on three benchmark datasets demonstrate that LDGN significantly outperforms the state-of-the-art models, and also achieves better performance with respect to tail labels.", "author": "Qianwen Ma; Chunyuan Yuan; Wei Zhou; Songlin Hu", "authorids": "/q/qianwen-ma/; /c/chunyuan-yuan/; /w/wei-zhou/; /s/songlin-hu/", "bibtex": "@inproceedings{ma-etal-2021-label,\n title = \"Label-Specific Dual Graph Neural Network for Multi-Label Text Classification\",\n author = \"Ma, Qianwen and\n Yuan, Chunyuan and\n Zhou, Wei and\n Hu, Songlin\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.298/\",\n doi = \"10.18653/v1/2021.acl-long.298\",\n pages = \"3855--3864\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.298.pdf", "site": "https://aclanthology.org/2021.acl-long.298/", "pdf_size": 697603, "gs_citation": 99, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=5251658046261888917&as_sdt=5,47&sciodt=0,47&hl=en", "gs_version_total": 3, "aff": "Institute of Information Engineering, Chinese Academy of Sciences+School of Cyber Security, University of Chinese Academy of Sciences; Institute of Information Engineering, Chinese Academy of Sciences+School of Cyber Security, University of Chinese Academy of Sciences; Institute of Information Engineering, Chinese Academy of Sciences; Institute of Information Engineering, Chinese Academy of Sciences+School of Cyber Security, University of Chinese Academy of Sciences", "aff_domain": "iie.ac.cn;iie.ac.cn;iie.ac.cn;iie.ac.cn", "email": "iie.ac.cn;iie.ac.cn;iie.ac.cn;iie.ac.cn", "github": "", "project": "", "author_num": 4, "aff_unique_index": "0+1;0+1;0;0+1", "aff_unique_norm": "Chinese Academy of Sciences;University of Chinese Academy of Sciences", "aff_unique_dep": "Institute of Information Engineering;School of Cyber Security", "aff_unique_url": "http://www.cas.cn;http://www.ucas.ac.cn", "aff_unique_abbr": "CAS;UCAS", "aff_campus_unique_index": ";;", "aff_campus_unique": "", "aff_country_unique_index": "0+0;0+0;0;0+0", "aff_country_unique": "China" }, { "id": "2021.acl-long.560", "title": "Language Embeddings for Typology and Cross-lingual Transfer Learning", "track": "main", "status": "Long", "award": false, "abstract": "Cross-lingual language tasks typically require a substantial amount of annotated data or parallel translation data. We explore whether language representations that capture relationships among languages can be learned and subsequently leveraged in cross-lingual tasks without the use of parallel data. We generate dense embeddings for 29 languages using a denoising autoencoder, and evaluate the embeddings using the World Atlas of Language Structures (WALS) and two extrinsic tasks in a zero-shot setting: cross-lingual dependency parsing and cross-lingual natural language inference.", "author": "Dian Yu; Taiqi He; Kenji Sagae", "authorids": "/d/dian-yu/; /t/taiqi-he/; /k/kenji-sagae/", "bibtex": "@inproceedings{yu-etal-2021-language,\n title = \"Language Embeddings for Typology and Cross-lingual Transfer Learning\",\n author = \"Yu, Dian and\n He, Taiqi and\n Sagae, Kenji\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.560/\",\n doi = \"10.18653/v1/2021.acl-long.560\",\n pages = \"7210--7225\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.560.pdf", "site": "https://aclanthology.org/2021.acl-long.560/", "pdf_size": 369841, "gs_citation": 13, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=10688432151469165612&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 5, "aff": "University of California, Davis; University of California, Davis; University of California, Davis", "aff_domain": "ucdavis.edu;ucdavis.edu;ucdavis.edu", "email": "ucdavis.edu;ucdavis.edu;ucdavis.edu", "github": "https://github.com/DianDYu/language_embeddings", "project": "", "author_num": 3, "aff_unique_index": "0;0;0", "aff_unique_norm": "University of California, Davis", "aff_unique_dep": "", "aff_unique_url": "https://www.ucdavis.edu", "aff_unique_abbr": "UC Davis", "aff_campus_unique_index": "0;0;0", "aff_campus_unique": "Davis", "aff_country_unique_index": "0;0;0", "aff_country_unique": "United States" }, { "id": "2021.acl-long.521", "title": "Language Model Augmented Relevance Score", "track": "main", "status": "Long", "award": false, "abstract": "Although automated metrics are commonly used to evaluate NLG systems, they often correlate poorly with human judgements. Newer metrics such as BERTScore have addressed many weaknesses in prior metrics such as BLEU and ROUGE, which rely on n-gram matching. These newer methods, however, are still limited in that they do not consider the generation context, so they cannot properly reward generated text that is correct but deviates from the given reference. In this paper, we propose Language Model Augmented Relevance Score (MARS), a new context-aware metric for NLG evaluation. MARS leverages off-the-shelf language models, guided by reinforcement learning, to create augmented references that consider both the generation context and available human references, which are then used as additional references to score generated text. Compared with seven existing metrics in three common NLG tasks, MARS not only achieves higher correlation with human reference judgements, but also differentiates well-formed candidates from adversarial samples to a larger degree.", "author": "Ruibo Liu; Jason Wei; Soroush Vosoughi", "authorids": "/r/ruibo-liu/; /j/jason-wei/; /s/soroush-vosoughi/", "bibtex": "@inproceedings{liu-etal-2021-language,\n title = \"Language Model Augmented Relevance Score\",\n author = \"Liu, Ruibo and\n Wei, Jason and\n Vosoughi, Soroush\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.521/\",\n doi = \"10.18653/v1/2021.acl-long.521\",\n pages = \"6677--6690\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.521.pdf", "site": "https://aclanthology.org/2021.acl-long.521/", "pdf_size": 479386, "gs_citation": 10, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=17617951070222220820&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 4, "aff": "Dartmouth College; Google AI Language; Dartmouth College", "aff_domain": "dartmouth.edu;google.com;dartmouth.edu", "email": "dartmouth.edu;google.com;dartmouth.edu", "github": "", "project": "", "author_num": 3, "aff_unique_index": "0;1;0", "aff_unique_norm": "Dartmouth College;Google", "aff_unique_dep": ";Google AI Language", "aff_unique_url": "https://www.dartmouth.edu;https://ai.google", "aff_unique_abbr": "Dartmouth;Google AI", "aff_campus_unique_index": "1", "aff_campus_unique": ";Mountain View", "aff_country_unique_index": "0;0;0", "aff_country_unique": "United States" }, { "id": "2021.acl-long.414", "title": "Language Model Evaluation Beyond Perplexity", "track": "main", "status": "Long", "award": false, "abstract": "We propose an alternate approach to quantifying how well language models learn natural language: we ask how well they match the statistical tendencies of natural language. To answer this question, we analyze whether text generated from language models exhibits the statistical tendencies present in the human-generated text on which they were trained. We provide a framework\u2013paired with significance tests\u2013for evaluating the fit of language models to these trends. We find that neural language models appear to learn only a subset of the tendencies considered, but align much more closely with empirical trends than proposed theoretical distributions (when present). Further, the fit to different distributions is highly-dependent on both model architecture and generation strategy. As concrete examples, text generated under the nucleus sampling scheme adheres more closely to the type\u2013token relationship of natural language than text produced using standard ancestral sampling; text from LSTMs reflects the natural language distributions over length, stopwords, and symbols surprisingly well.", "author": "Clara Meister; Ryan Cotterell", "authorids": "/c/clara-meister/; /r/ryan-cotterell/", "bibtex": "@inproceedings{meister-cotterell-2021-language,\n title = \"Language Model Evaluation Beyond Perplexity\",\n author = \"Meister, Clara and\n Cotterell, Ryan\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.414/\",\n doi = \"10.18653/v1/2021.acl-long.414\",\n pages = \"5328--5339\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.414.pdf", "site": "https://aclanthology.org/2021.acl-long.414/", "pdf_size": 3253969, "gs_citation": 87, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=5272673663614846441&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 6, "aff": "ETH Z\u00fcrich; University of Cambridge", "aff_domain": "inf.ethz.ch; ", "email": "inf.ethz.ch; ", "github": "", "project": "", "author_num": 2, "aff_unique_index": "0;1", "aff_unique_norm": "ETH Z\u00fcrich;University of Cambridge", "aff_unique_dep": ";", "aff_unique_url": "https://www.ethz.ch;https://www.cam.ac.uk", "aff_unique_abbr": "ETHZ;Cambridge", "aff_campus_unique_index": "1", "aff_campus_unique": ";Cambridge", "aff_country_unique_index": "0;1", "aff_country_unique": "Switzerland;United Kingdom" }, { "id": "2021.acl-long.117", "title": "Language Model as an Annotator: Exploring DialoGPT for Dialogue Summarization", "track": "main", "status": "Long", "award": false, "abstract": "Current dialogue summarization systems usually encode the text with a number of general semantic features (e.g., keywords and topics) to gain more powerful dialogue modeling capabilities. However, these features are obtained via open-domain toolkits that are dialog-agnostic or heavily relied on human annotations. In this paper, we show how DialoGPT, a pre-trained model for conversational response generation, can be developed as an unsupervised dialogue annotator, which takes advantage of dialogue background knowledge encoded in DialoGPT. We apply DialoGPT to label three types of features on two dialogue summarization datasets, SAMSum and AMI, and employ pre-trained and non pre-trained models as our summarizers. Experimental results show that our proposed method can obtain remarkable improvements on both datasets and achieves new state-of-the-art performance on the SAMSum dataset.", "author": "Xiachong Feng; Xiaocheng Feng; Libo Qin; Bing Qin; Ting Liu", "authorids": "/x/xiachong-feng/; /x/xiaocheng-feng/; /l/libo-qin/; /b/bing-qin/; /t/ting-liu/", "bibtex": "@inproceedings{feng-etal-2021-language,\n title = \"Language Model as an Annotator: Exploring {D}ialo{GPT} for Dialogue Summarization\",\n author = \"Feng, Xiachong and\n Feng, Xiaocheng and\n Qin, Libo and\n Qin, Bing and\n Liu, Ting\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.117/\",\n doi = \"10.18653/v1/2021.acl-long.117\",\n pages = \"1479--1491\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.117.pdf", "site": "https://aclanthology.org/2021.acl-long.117/", "pdf_size": 1607106, "gs_citation": 103, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=1789150837288178507&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 7, "aff": "Harbin Institute of Technology, China + Peng Cheng Laboratory, China; Harbin Institute of Technology, China + Peng Cheng Laboratory, China; Harbin Institute of Technology, China; Harbin Institute of Technology, China + Peng Cheng Laboratory, China; Harbin Institute of Technology, China + Peng Cheng Laboratory, China", "aff_domain": "ir.hit.edu.cn;ir.hit.edu.cn;ir.hit.edu.cn;ir.hit.edu.cn;ir.hit.edu.cn", "email": "ir.hit.edu.cn;ir.hit.edu.cn;ir.hit.edu.cn;ir.hit.edu.cn;ir.hit.edu.cn", "github": "https://github.com/xcfcode/PLM_annotator", "project": "", "author_num": 5, "aff_unique_index": "0+1;0+1;0;0+1;0+1", "aff_unique_norm": "Harbin Institute of Technology;Peng Cheng Laboratory", "aff_unique_dep": ";", "aff_unique_url": "http://www.hit.edu.cn/;", "aff_unique_abbr": "HIT;", "aff_campus_unique_index": ";;;", "aff_campus_unique": "", "aff_country_unique_index": "0+0;0+0;0;0+0;0+0", "aff_country_unique": "China" }, { "id": "2021.acl-long.201", "title": "LayoutLMv2: Multi-modal Pre-training for Visually-rich Document Understanding", "track": "main", "status": "Long", "award": false, "abstract": "Pre-training of text and layout has proved effective in a variety of visually-rich document understanding tasks due to its effective model architecture and the advantage of large-scale unlabeled scanned/digital-born documents. We propose LayoutLMv2 architecture with new pre-training tasks to model the interaction among text, layout, and image in a single multi-modal framework. Specifically, with a two-stream multi-modal Transformer encoder, LayoutLMv2 uses not only the existing masked visual-language modeling task but also the new text-image alignment and text-image matching tasks, which make it better capture the cross-modality interaction in the pre-training stage. Meanwhile, it also integrates a spatial-aware self-attention mechanism into the Transformer architecture so that the model can fully understand the relative positional relationship among different text blocks. Experiment results show that LayoutLMv2 outperforms LayoutLM by a large margin and achieves new state-of-the-art results on a wide variety of downstream visually-rich document understanding tasks, including FUNSD (0.7895 to 0.8420), CORD (0.9493 to 0.9601), SROIE (0.9524 to 0.9781), Kleister-NDA (0.8340 to 0.8520), RVL-CDIP (0.9443 to 0.9564), and DocVQA (0.7295 to 0.8672).", "author": "Yang Xu; Yiheng Xu; Tengchao Lv; Lei Cui; Furu Wei; Guoxin Wang; Yijuan Lu; Dinei Florencio; Cha Zhang; Wanxiang Che; Min Zhang; Lidong Zhou", "authorids": "/y/yang-xu/; /y/yiheng-xu/; /t/tengchao-lv/; /l/lei-cui/; /f/furu-wei/; /g/guoxin-wang/; /y/yijuan-lu/; /d/dinei-florencio/; /c/cha-zhang/; /w/wanxiang-che/; /m/min-zhang/; /l/lidong-zhou/", "bibtex": "@inproceedings{xu-etal-2021-layoutlmv2,\n title = \"{L}ayout{LM}v2: Multi-modal Pre-training for Visually-rich Document Understanding\",\n author = \"Xu, Yang and\n Xu, Yiheng and\n Lv, Tengchao and\n Cui, Lei and\n Wei, Furu and\n Wang, Guoxin and\n Lu, Yijuan and\n Florencio, Dinei and\n Zhang, Cha and\n Che, Wanxiang and\n Zhang, Min and\n Zhou, Lidong\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.201/\",\n doi = \"10.18653/v1/2021.acl-long.201\",\n pages = \"2579--2591\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.201.pdf", "site": "https://aclanthology.org/2021.acl-long.201/", "pdf_size": 995010, "gs_citation": 598, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=7173103050422200794&as_sdt=5,44&sciodt=0,44&hl=en", "gs_version_total": 7, "aff": "Research Center for Social Computing and Information Retrieval, Harbin Institute of Technology; Microsoft Research Asia; Microsoft Azure AI; Soochow University; Microsoft Research Asia; Microsoft Azure AI; Soochow University; Microsoft Azure AI; Microsoft Azure AI; Research Center for Social Computing and Information Retrieval, Harbin Institute of Technology; Soochow University; Microsoft Research Asia", "aff_domain": "ir.hit.edu.cn;ir.hit.edu.cn;microsoft.com;microsoft.com;microsoft.com;microsoft.com;microsoft.com;microsoft.com;microsoft.com;microsoft.com;microsoft.com;suda.edu.cn", "email": "ir.hit.edu.cn;ir.hit.edu.cn;microsoft.com;microsoft.com;microsoft.com;microsoft.com;microsoft.com;microsoft.com;microsoft.com;microsoft.com;microsoft.com;suda.edu.cn", "github": "", "project": "https://aka.ms/layoutlmv2", "author_num": 12, "aff_unique_index": "0;1;2;3;1;2;3;2;2;0;3;1", "aff_unique_norm": "Harbin Institute of Technology;Microsoft Research;Microsoft;Soochow University", "aff_unique_dep": "Research Center for Social Computing and Information Retrieval;Research;Azure AI;", "aff_unique_url": "http://www.hit.edu.cn/;https://www.microsoft.com/en-us/research/group/asia;https://azure.microsoft.com/en-us/ai;https://www.soochow.edu.cn", "aff_unique_abbr": "HIT;MSR Asia;Microsoft Azure AI;Soochow U", "aff_campus_unique_index": "0;1;1;0;1", "aff_campus_unique": "Harbin;Asia;", "aff_country_unique_index": "0;0;1;0;0;1;0;1;1;0;0;0", "aff_country_unique": "China;United States" }, { "id": "2021.acl-long.478", "title": "Learn to Resolve Conversational Dependency: A Consistency Training Framework for Conversational Question Answering", "track": "main", "status": "Long", "award": false, "abstract": "One of the main challenges in conversational question answering (CQA) is to resolve the conversational dependency, such as anaphora and ellipsis. However, existing approaches do not explicitly train QA models on how to resolve the dependency, and thus these models are limited in understanding human dialogues. In this paper, we propose a novel framework, ExCorD (Explicit guidance on how to resolve Conversational Dependency) to enhance the abilities of QA models in comprehending conversational context. ExCorD first generates self-contained questions that can be understood without the conversation history, then trains a QA model with the pairs of original and self-contained questions using a consistency-based regularizer. In our experiments, we demonstrate that ExCorD significantly improves the QA models\u2019 performance by up to 1.2 F1 on QuAC, and 5.2 F1 on CANARD, while addressing the limitations of the existing approaches.", "author": "Gangwoo Kim; Hyunjae Kim; Jungsoo Park; Jaewoo Kang", "authorids": "/g/gangwoo-kim/; /h/hyunjae-kim/; /j/jungsoo-park/; /j/jaewoo-kang/", "bibtex": "@inproceedings{kim-etal-2021-learn,\n title = \"Learn to Resolve Conversational Dependency: A Consistency Training Framework for Conversational Question Answering\",\n author = \"Kim, Gangwoo and\n Kim, Hyunjae and\n Park, Jungsoo and\n Kang, Jaewoo\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.478/\",\n doi = \"10.18653/v1/2021.acl-long.478\",\n pages = \"6130--6141\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.478.pdf", "site": "https://aclanthology.org/2021.acl-long.478/", "pdf_size": 454804, "gs_citation": 44, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=5668766624265416221&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 6, "aff": "Korea University; Korea University; Korea University; Korea University\u2020", "aff_domain": "korea.ac.kr;korea.ac.kr;korea.ac.kr;korea.ac.kr", "email": "korea.ac.kr;korea.ac.kr;korea.ac.kr;korea.ac.kr", "github": "https://github.com/dmis-lab/excord", "project": "", "author_num": 4, "aff_unique_index": "0;0;0;0", "aff_unique_norm": "Korea University", "aff_unique_dep": "", "aff_unique_url": "https://www.korea.ac.kr", "aff_unique_abbr": "KU", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0;0;0;0", "aff_country_unique": "South Korea" }, { "id": "2021.acl-long.276", "title": "LearnDA: Learnable Knowledge-Guided Data Augmentation for Event Causality Identification", "track": "main", "status": "Long", "award": false, "abstract": "Modern models for event causality identification (ECI) are mainly based on supervised learning, which are prone to the data lacking problem. Unfortunately, the existing NLP-related augmentation methods cannot directly produce available data required for this task. To solve the data lacking problem, we introduce a new approach to augment training data for event causality identification, by iteratively generating new examples and classifying event causality in a dual learning framework. On the one hand, our approach is knowledge guided, which can leverage existing knowledge bases to generate well-formed new sentences. On the other hand, our approach employs a dual mechanism, which is a learnable augmentation framework, and can interactively adjust the generation process to generate task-related sentences. Experimental results on two benchmarks EventStoryLine and Causal-TimeBank show that 1) our method can augment suitable task-related training data for ECI; 2) our method outperforms previous methods on EventStoryLine and Causal-TimeBank (+2.5 and +2.1 points on F1 value respectively).", "author": "Xinyu Zuo; Pengfei Cao; Yubo Chen; Kang Liu; Jun Zhao; Weihua Peng; Yuguang Chen", "authorids": "/x/xinyu-zuo/; /p/pengfei-cao/; /y/yubo-chen/; /k/kang-liu/; /j/jun-zhao/; /w/weihua-peng/; /y/yuguang-chen/", "bibtex": "@inproceedings{zuo-etal-2021-learnda,\n title = \"{L}earn{DA}: Learnable Knowledge-Guided Data Augmentation for Event Causality Identification\",\n author = \"Zuo, Xinyu and\n Cao, Pengfei and\n Chen, Yubo and\n Liu, Kang and\n Zhao, Jun and\n Peng, Weihua and\n Chen, Yuguang\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.276/\",\n doi = \"10.18653/v1/2021.acl-long.276\",\n pages = \"3558--3571\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.276.pdf", "site": "https://aclanthology.org/2021.acl-long.276/", "pdf_size": 923127, "gs_citation": 64, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=10138299946696027128&as_sdt=5,44&sciodt=0,44&hl=en", "gs_version_total": 5, "aff": "National Laboratory of Pattern Recognition, Institute of Automation, CAS, Beijing, China+School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China; National Laboratory of Pattern Recognition, Institute of Automation, CAS, Beijing, China+School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China; National Laboratory of Pattern Recognition, Institute of Automation, CAS, Beijing, China+School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China; National Laboratory of Pattern Recognition, Institute of Automation, CAS, Beijing, China+School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China; National Laboratory of Pattern Recognition, Institute of Automation, CAS, Beijing, China+School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China; Beijing Baidu Netcom Science Technology Co., Ltd; Beijing Baidu Netcom Science Technology Co., Ltd", "aff_domain": "nlpr.ia.ac.cn;nlpr.ia.ac.cn;nlpr.ia.ac.cn;nlpr.ia.ac.cn;nlpr.ia.ac.cn;baidu.com;baidu.com", "email": "nlpr.ia.ac.cn;nlpr.ia.ac.cn;nlpr.ia.ac.cn;nlpr.ia.ac.cn;nlpr.ia.ac.cn;baidu.com;baidu.com", "github": "", "project": "", "author_num": 7, "aff_unique_index": "0+1;0+1;0+1;0+1;0+1;2;2", "aff_unique_norm": "National Laboratory of Pattern Recognition;University of Chinese Academy of Sciences;Baidu", "aff_unique_dep": "Institute of Automation;School of Artificial Intelligence;Baidu Netcom Science Technology", "aff_unique_url": ";http://www.ucas.ac.cn;https://www.baidu.com", "aff_unique_abbr": ";UCAS;Baidu", "aff_campus_unique_index": "0+0;0+0;0+0;0+0;0+0", "aff_campus_unique": "Beijing;", "aff_country_unique_index": "0+0;0+0;0+0;0+0;0+0;0;0", "aff_country_unique": "China" }, { "id": "2021.acl-long.518", "title": "Learning Dense Representations of Phrases at Scale", "track": "main", "status": "Long", "award": false, "abstract": "Open-domain question answering can be reformulated as a phrase retrieval problem, without the need for processing documents on-demand during inference (Seo et al., 2019). However, current phrase retrieval models heavily depend on sparse representations and still underperform retriever-reader approaches. In this work, we show for the first time that we can learn dense representations of phrases alone that achieve much stronger performance in open-domain QA. We present an effective method to learn phrase representations from the supervision of reading comprehension tasks, coupled with novel negative sampling methods. We also propose a query-side fine-tuning strategy, which can support transfer learning and reduce the discrepancy between training and inference. On five popular open-domain QA datasets, our model DensePhrases improves over previous phrase retrieval models by 15%-25% absolute accuracy and matches the performance of state-of-the-art retriever-reader models. Our model is easy to parallelize due to pure dense representations and processes more than 10 questions per second on CPUs. Finally, we directly use our pre-indexed dense phrase representations for two slot filling tasks, showing the promise of utilizing DensePhrases as a dense knowledge base for downstream tasks.", "author": "Jinhyuk Lee; Mujeen Sung; Jaewoo Kang; Danqi Chen", "authorids": "/j/jinhyuk-lee/; /m/mujeen-sung/; /j/jaewoo-kang/; /d/danqi-chen/", "bibtex": "@inproceedings{lee-etal-2021-learning-dense,\n title = \"Learning Dense Representations of Phrases at Scale\",\n author = \"Lee, Jinhyuk and\n Sung, Mujeen and\n Kang, Jaewoo and\n Chen, Danqi\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.518/\",\n doi = \"10.18653/v1/2021.acl-long.518\",\n pages = \"6634--6647\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.518.pdf", "site": "https://aclanthology.org/2021.acl-long.518/", "pdf_size": 1717293, "gs_citation": 130, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=5659130969286847495&as_sdt=5,33&sciodt=0,33&hl=en", "gs_version_total": 9, "aff": "Korea University1+Princeton University2; Korea University1; Korea University1; Princeton University2", "aff_domain": "korea.ac.kr;korea.ac.kr;korea.ac.kr;cs.princeton.edu", "email": "korea.ac.kr;korea.ac.kr;korea.ac.kr;cs.princeton.edu", "github": "https://github.com/princeton-nlp/DensePhrases", "project": "", "author_num": 4, "aff_unique_index": "0+1;0;0;1", "aff_unique_norm": "Korea University;Princeton University", "aff_unique_dep": ";", "aff_unique_url": "https://www.korea.ac.kr;https://www.princeton.edu", "aff_unique_abbr": "KU;Princeton", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0+1;0;0;1", "aff_country_unique": "South Korea;United States" }, { "id": "2021.acl-short.72", "title": "Learning Domain-Specialised Representations for Cross-Lingual Biomedical Entity Linking", "track": "main", "status": "Short", "award": false, "abstract": "Injecting external domain-specific knowledge (e.g., UMLS) into pretrained language models (LMs) advances their capability to handle specialised in-domain tasks such as biomedical entity linking (BEL). However, such abundant expert knowledge is available only for a handful of languages (e.g., English). In this work, by proposing a novel cross-lingual biomedical entity linking task (XL-BEL) and establishing a new XL-BEL benchmark spanning 10 typologically diverse languages, we first investigate the ability of standard knowledge-agnostic as well as knowledge-enhanced monolingual and multilingual LMs beyond the standard monolingual English BEL task. The scores indicate large gaps to English performance. We then address the challenge of transferring domain-specific knowledge in resource-rich languages to resource-poor ones. To this end, we propose and evaluate a series of cross-lingual transfer methods for the XL-BEL task, and demonstrate that general-domain bitext helps propagate the available English knowledge to languages with little to no in-domain data. Remarkably, we show that our proposed domain-specific transfer methods yield consistent gains across all target languages, sometimes up to 20 Precision@1 points, without any in-domain knowledge in the target language, and without any in-domain parallel data.", "author": "Fangyu Liu; Ivan Vuli\u0107; Anna Korhonen; Nigel Collier", "authorids": "/f/fangyu-liu/; /i/ivan-vulic/; /a/anna-korhonen/; /n/nigel-collier/", "bibtex": "@inproceedings{liu-etal-2021-learning-domain,\n title = \"Learning Domain-Specialised Representations for Cross-Lingual Biomedical Entity Linking\",\n author = \"Liu, Fangyu and\n Vuli{\\'c}, Ivan and\n Korhonen, Anna and\n Collier, Nigel\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-short.72/\",\n doi = \"10.18653/v1/2021.acl-short.72\",\n pages = \"565--574\"\n}", "pdf": "https://aclanthology.org/2021.acl-short.72.pdf", "site": "https://aclanthology.org/2021.acl-short.72/", "pdf_size": 401036, "gs_citation": 67, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=1947577663781143075&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 7, "aff": "Language Technology Lab, TAL, University of Cambridge; Language Technology Lab, TAL, University of Cambridge; Language Technology Lab, TAL, University of Cambridge; Language Technology Lab, TAL, University of Cambridge", "aff_domain": "cam.ac.uk;cam.ac.uk;cam.ac.uk;cam.ac.uk", "email": "cam.ac.uk;cam.ac.uk;cam.ac.uk;cam.ac.uk", "github": "", "project": "", "author_num": 4, "aff_unique_index": "0;0;0;0", "aff_unique_norm": "University of Cambridge", "aff_unique_dep": "Language Technology Lab, TAL", "aff_unique_url": "https://www.cam.ac.uk", "aff_unique_abbr": "Cambridge", "aff_campus_unique_index": "0;0;0;0", "aff_campus_unique": "Cambridge", "aff_country_unique_index": "0;0;0;0", "aff_country_unique": "United Kingdom" }, { "id": "2021.acl-long.403", "title": "Learning Event Graph Knowledge for Abductive Reasoning", "track": "main", "status": "Long", "award": false, "abstract": "Abductive reasoning aims at inferring the most plausible explanation for observed events, which would play critical roles in various NLP applications, such as reading comprehension and question answering. To facilitate this task, a narrative text based abductive reasoning task \ud835\udefcNLI is proposed, together with explorations about building reasoning framework using pretrained language models. However, abundant event commonsense knowledge is not well exploited for this task. To fill this gap, we propose a variational autoencoder based model ege-RoBERTa, which employs a latent variable to capture the necessary commonsense knowledge from event graph for guiding the abductive reasoning task. Experimental results show that through learning the external event graph knowledge, our approach outperforms the baseline methods on the \ud835\udefcNLI task.", "author": "Li Du; Xiao Ding; Ting Liu; Bing Qin", "authorids": "/l/li-du/; /x/xiao-ding/; /t/ting-liu/; /b/bing-qin/", "bibtex": "@inproceedings{du-etal-2021-learning,\n title = \"Learning Event Graph Knowledge for Abductive Reasoning\",\n author = \"Du, Li and\n Ding, Xiao and\n Liu, Ting and\n Qin, Bing\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.403/\",\n doi = \"10.18653/v1/2021.acl-long.403\",\n pages = \"5181--5190\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.403.pdf", "site": "https://aclanthology.org/2021.acl-long.403/", "pdf_size": 710606, "gs_citation": 29, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=16869240331502328895&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 2, "aff": "Research Center for Social Computing and Information Retrieval, Harbin Institute of Technology, China; Research Center for Social Computing and Information Retrieval, Harbin Institute of Technology, China; Research Center for Social Computing and Information Retrieval, Harbin Institute of Technology, China; Research Center for Social Computing and Information Retrieval, Harbin Institute of Technology, China", "aff_domain": "ir.hit.edu.cn;ir.hit.edu.cn;ir.hit.edu.cn;ir.hit.edu.cn", "email": "ir.hit.edu.cn;ir.hit.edu.cn;ir.hit.edu.cn;ir.hit.edu.cn", "github": "", "project": "", "author_num": 4, "aff_unique_index": "0;0;0;0", "aff_unique_norm": "Harbin Institute of Technology", "aff_unique_dep": "Research Center for Social Computing and Information Retrieval", "aff_unique_url": "http://www.hit.edu.cn/", "aff_unique_abbr": "HIT", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0;0;0;0", "aff_country_unique": "China" }, { "id": "2021.acl-long.69", "title": "Learning Faithful Representations of Causal Graphs", "track": "main", "status": "Long", "award": false, "abstract": "Learning contextual text embeddings that represent causal graphs has been useful in improving the performance of downstream tasks like causal treatment effect estimation. However, existing causal embeddings which are trained to predict direct causal links, fail to capture other indirect causal links of the graph, thus leading to spurious correlations in downstream tasks. In this paper, we define the faithfulness property of contextual embeddings to capture geometric distance-based properties of directed acyclic causal graphs. By incorporating these faithfulness properties, we learn text embeddings that are 31.3% more faithful to human validated causal graphs with about 800K and 200K causal links and achieve 21.1% better Precision-Recall AUC in a link prediction fine-tuning task. Further, in a crowdsourced causal question-answering task on Yahoo! Answers with questions of the form \u201cWhat causes X?\u201d, our faithful embeddings achieved a precision of the first ranked answer (P@1) of 41.07%, outperforming the existing baseline by 10.2%.", "author": "Ananth Balashankar; Lakshminarayanan Subramanian", "authorids": "/a/ananth-balashankar/; /l/lakshminarayanan-subramanian/", "bibtex": "@inproceedings{balashankar-subramanian-2021-learning,\n title = \"Learning Faithful Representations of Causal Graphs\",\n author = \"Balashankar, Ananth and\n Subramanian, Lakshminarayanan\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.69/\",\n doi = \"10.18653/v1/2021.acl-long.69\",\n pages = \"839--850\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.69.pdf", "site": "https://aclanthology.org/2021.acl-long.69/", "pdf_size": 518036, "gs_citation": 16, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=700899425576674642&as_sdt=40005&sciodt=0,10&hl=en", "gs_version_total": 3, "aff": "New York University, New York, NY, USA; New York University, New York, NY, USA", "aff_domain": "nyu.edu;nyu.edu", "email": "nyu.edu;nyu.edu", "github": "", "project": "", "author_num": 2, "aff_unique_index": "0;0", "aff_unique_norm": "New York University", "aff_unique_dep": "", "aff_unique_url": "https://www.nyu.edu", "aff_unique_abbr": "NYU", "aff_campus_unique_index": "0;0", "aff_campus_unique": "New York", "aff_country_unique_index": "0;0", "aff_country_unique": "United States" }, { "id": "2021.acl-long.25", "title": "Learning Language Specific Sub-network for Multilingual Machine Translation", "track": "main", "status": "Long", "award": false, "abstract": "Multilingual neural machine translation aims at learning a single translation model for multiple languages. These jointly trained models often suffer from performance degradationon rich-resource language pairs. We attribute this degeneration to parameter interference. In this paper, we propose LaSS to jointly train a single unified multilingual MT model. LaSS learns Language Specific Sub-network (LaSS) for each language pair to counter parameter interference. Comprehensive experiments on IWSLT and WMT datasets with various Transformer architectures show that LaSS obtains gains on 36 language pairs by up to 1.2 BLEU. Besides, LaSS shows its strong generalization performance at easy adaptation to new language pairs and zero-shot translation. LaSS boosts zero-shot translation with an average of 8.3 BLEU on 30 language pairs. Codes and trained models are available at https://github.com/NLP-Playground/LaSS.", "author": "Zehui Lin; Liwei Wu; Mingxuan Wang; Lei Li", "authorids": "/z/zehui-lin/; /l/liwei-wu/; /m/mingxuan-wang/; /l/lei-li/", "bibtex": "@inproceedings{lin-etal-2021-learning,\n title = \"Learning Language Specific Sub-network for Multilingual Machine Translation\",\n author = \"Lin, Zehui and\n Wu, Liwei and\n Wang, Mingxuan and\n Li, Lei\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.25/\",\n doi = \"10.18653/v1/2021.acl-long.25\",\n pages = \"293--305\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.25.pdf", "site": "https://aclanthology.org/2021.acl-long.25/", "pdf_size": 731647, "gs_citation": 95, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=381076053709264463&as_sdt=5,44&sciodt=0,44&hl=en", "gs_version_total": 9, "aff": "ByteDance AI Lab; ByteDance AI Lab; ByteDance AI Lab; ByteDance AI Lab", "aff_domain": "bytedance.com;bytedance.com;bytedance.com;bytedance.com", "email": "bytedance.com;bytedance.com;bytedance.com;bytedance.com", "github": "https://github.com/NLP-Playground/LaSS", "project": "", "author_num": 4, "aff_unique_index": "0;0;0;0", "aff_unique_norm": "ByteDance", "aff_unique_dep": "AI Lab", "aff_unique_url": "https://www.bytedance.com", "aff_unique_abbr": "ByteDance", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0;0;0;0", "aff_country_unique": "China" }, { "id": "2021.acl-long.322", "title": "Learning Language and Multimodal Privacy-Preserving Markers of Mood from Mobile Data", "track": "main", "status": "Long", "award": false, "abstract": "Mental health conditions remain underdiagnosed even in countries with common access to advanced medical care. The ability to accurately and efficiently predict mood from easily collectible data has several important implications for the early detection, intervention, and treatment of mental health disorders. One promising data source to help monitor human behavior is daily smartphone usage. However, care must be taken to summarize behaviors without identifying the user through personal (e.g., personally identifiable information) or protected (e.g., race, gender) attributes. In this paper, we study behavioral markers of daily mood using a recent dataset of mobile behaviors from adolescent populations at high risk of suicidal behaviors. Using computational models, we find that language and multimodal representations of mobile typed text (spanning typed characters, words, keystroke timings, and app usage) are predictive of daily mood. However, we find that models trained to predict mood often also capture private user identities in their intermediate representations. To tackle this problem, we evaluate approaches that obfuscate user identity while remaining predictive. By combining multimodal representations with privacy-preserving learning, we are able to push forward the performance-privacy frontier.", "author": "Paul Pu Liang; Terrance Liu; Anna Cai; Michal Muszynski; Ryo Ishii; Nick Allen; Randy Auerbach; David Brent; Ruslan Salakhutdinov; Louis-Philippe Morency", "authorids": "/p/paul-pu-liang/; /t/terrance-liu/; /a/anna-cai/; /m/michal-muszynski/; /r/ryo-ishii/; /n/nick-allen/; /r/randy-auerbach/; /d/david-brent/; /r/ruslan-salakhutdinov/; /l/louis-philippe-morency/", "bibtex": "@inproceedings{liang-etal-2021-learning,\n title = \"Learning Language and Multimodal Privacy-Preserving Markers of Mood from Mobile Data\",\n author = \"Liang, Paul Pu and\n Liu, Terrance and\n Cai, Anna and\n Muszynski, Michal and\n Ishii, Ryo and\n Allen, Nick and\n Auerbach, Randy and\n Brent, David and\n Salakhutdinov, Ruslan and\n Morency, Louis-Philippe\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.322/\",\n doi = \"10.18653/v1/2021.acl-long.322\",\n pages = \"4170--4187\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.322.pdf", "site": "https://aclanthology.org/2021.acl-long.322/", "pdf_size": 2927517, "gs_citation": 21, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=17678357804779458487&as_sdt=5,33&sciodt=0,33&hl=en", "gs_version_total": 7, "aff": "Carnegie Mellon University; Carnegie Mellon University; Carnegie Mellon University; Carnegie Mellon University; Carnegie Mellon University; University of Oregon; Columbia University; University of Pittsburgh; Carnegie Mellon University; Carnegie Mellon University", "aff_domain": "cs.cmu.edu;cs.cmu.edu;cs.cmu.edu;cs.cmu.edu;cs.cmu.edu;uoregon.edu;cumc.columbia.edu;upmc.edu;cs.cmu.edu;cs.cmu.edu", "email": "cs.cmu.edu;cs.cmu.edu;cs.cmu.edu;cs.cmu.edu;cs.cmu.edu;uoregon.edu;cumc.columbia.edu;upmc.edu;cs.cmu.edu;cs.cmu.edu", "github": "", "project": "", "author_num": 10, "aff_unique_index": "0;0;0;0;0;1;2;3;0;0", "aff_unique_norm": "Carnegie Mellon University;University of Oregon;Columbia University;University of Pittsburgh", "aff_unique_dep": ";;;", "aff_unique_url": "https://www.cmu.edu;https://www.uoregon.edu;https://www.columbia.edu;https://www.pitt.edu", "aff_unique_abbr": "CMU;UO;Columbia;Pitt", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0;0;0;0;0;0;0;0;0;0", "aff_country_unique": "United States" }, { "id": "2021.acl-long.525", "title": "Learning Latent Structures for Cross Action Phrase Relations in Wet Lab Protocols", "track": "main", "status": "Long", "award": false, "abstract": "Wet laboratory protocols (WLPs) are critical for conveying reproducible procedures in biological research. They are composed of instructions written in natural language describing the step-wise processing of materials by specific actions. This process flow description for reagents and materials synthesis in WLPs can be captured by material state transfer graphs (MSTGs), which encode global temporal and causal relationships between actions. Here, we propose methods to automatically generate a MSTG for a given protocol by extracting all action relationships across multiple sentences. We also note that previous corpora and methods focused primarily on local intra-sentence relationships between actions and entities and did not address two critical issues: (i) resolution of implicit arguments and (ii) establishing long-range dependencies across sentences. We propose a new model that incrementally learns latent structures and is better suited to resolving inter-sentence relations and implicit arguments. This model draws upon a new corpus WLP-MSTG which was created by extending annotations in the WLP corpora for inter-sentence relations and implicit arguments. Our model achieves an F1 score of 54.53% for temporal and causal relations in protocols from our corpus, which is a significant improvement over previous models - DyGIE++:28.17%; spERT:27.81%. We make our annotated WLP-MSTG corpus available to the research community.", "author": "Chaitanya Kulkarni; Jany Chan; Eric Fosler-Lussier; Raghu Machiraju", "authorids": "/c/chaitanya-kulkarni/; /j/jany-chan/; /e/eric-fosler-lussier/; /r/raghu-machiraju/", "bibtex": "@inproceedings{kulkarni-etal-2021-learning,\n title = \"Learning Latent Structures for Cross Action Phrase Relations in Wet Lab Protocols\",\n author = \"Kulkarni, Chaitanya and\n Chan, Jany and\n Fosler-Lussier, Eric and\n Machiraju, Raghu\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.525/\",\n doi = \"10.18653/v1/2021.acl-long.525\",\n pages = \"6737--6750\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.525.pdf", "site": "https://aclanthology.org/2021.acl-long.525/", "pdf_size": 670076, "gs_citation": 3, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=15866283024359696519&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 3, "aff": "Department of Computer Science and Engineering, Ohio State University; Department of Computer Science and Engineering, Ohio State University; Department of Computer Science and Engineering, Ohio State University; Department of Computer Science and Engineering, Ohio State University", "aff_domain": "osu.edu;osu.edu;osu.edu;osu.edu", "email": "osu.edu;osu.edu;osu.edu;osu.edu", "github": "", "project": "", "author_num": 4, "aff_unique_index": "0;0;0;0", "aff_unique_norm": "Ohio State University", "aff_unique_dep": "Department of Computer Science and Engineering", "aff_unique_url": "https://www.osu.edu", "aff_unique_abbr": "OSU", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0;0;0;0", "aff_country_unique": "United States" }, { "id": "2021.acl-long.540", "title": "Learning Prototypical Functions for Physical Artifacts", "track": "main", "status": "Long", "award": false, "abstract": "Humans create things for a reason. Ancient people created spears for hunting, knives for cutting meat, pots for preparing food, etc. The prototypical function of a physical artifact is a kind of commonsense knowledge that we rely on to understand natural language. For example, if someone says \u201cShe borrowed the book\u201d then you would assume that she intends to read the book, or if someone asks \u201cCan I use your knife?\u201d then you would assume that they need to cut something. In this paper, we introduce a new NLP task of learning the prototypical uses for human-made physical objects. We use frames from FrameNet to represent a set of common functions for objects, and describe a manually annotated data set of physical objects labeled with their prototypical function. We also present experimental results for this task, including BERT-based models that use predictions from masked patterns as well as artifact sense definitions from WordNet and frame definitions from FrameNet.", "author": "Tianyu Jiang; Ellen Riloff", "authorids": "/t/tianyu-jiang/; /e/ellen-riloff/", "bibtex": "@inproceedings{jiang-riloff-2021-learning,\n title = \"Learning Prototypical Functions for Physical Artifacts\",\n author = \"Jiang, Tianyu and\n Riloff, Ellen\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.540/\",\n doi = \"10.18653/v1/2021.acl-long.540\",\n pages = \"6941--6951\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.540.pdf", "site": "https://aclanthology.org/2021.acl-long.540/", "pdf_size": 507175, "gs_citation": 3, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=10986987511370878834&as_sdt=5,33&sciodt=0,33&hl=en", "gs_version_total": 6, "aff": "School of Computing, University of Utah; School of Computing, University of Utah", "aff_domain": "cs.utah.edu;cs.utah.edu", "email": "cs.utah.edu;cs.utah.edu", "github": "", "project": "", "author_num": 2, "aff_unique_index": "0;0", "aff_unique_norm": "University of Utah", "aff_unique_dep": "School of Computing", "aff_unique_url": "https://www.utah.edu", "aff_unique_abbr": "U of U", "aff_campus_unique_index": "0;0", "aff_campus_unique": "Utah", "aff_country_unique_index": "0;0", "aff_country_unique": "United States" }, { "id": "2021.acl-long.43", "title": "Learning Relation Alignment for Calibrated Cross-modal Retrieval", "track": "main", "status": "Long", "award": false, "abstract": "Despite the achievements of large-scale multimodal pre-training approaches, cross-modal retrieval, e.g., image-text retrieval, remains a challenging task. To bridge the semantic gap between the two modalities, previous studies mainly focus on word-region alignment at the object level, lacking the matching between the linguistic relation among the words and the visual relation among the regions. The neglect of such relation consistency impairs the contextualized representation of image-text pairs and hinders the model performance and the interpretability. In this paper, we first propose a novel metric, Intra-modal Self-attention Distance (ISD), to quantify the relation consistency by measuring the semantic distance between linguistic and visual relations. In response, we present Inter-modal Alignment on Intra-modal Self-attentions (IAIS), a regularized training method to optimize the ISD and calibrate intra-modal self-attentions from the two modalities mutually via inter-modal alignment. The IAIS regularizer boosts the performance of prevailing models on Flickr30k and MS COCO datasets by a considerable margin, which demonstrates the superiority of our approach.", "author": "Shuhuai Ren; Junyang Lin; Guangxiang Zhao; Rui Men; An Yang; Jingren Zhou; Xu Sun; Hongxia Yang", "authorids": "/s/shuhuai-ren/; /j/junyang-lin/; /g/guangxiang-zhao/; /r/rui-men/; /a/an-yang/; /j/jingren-zhou/; /x/xu-sun/; /h/hongxia-yang/", "bibtex": "@inproceedings{ren-etal-2021-learning,\n title = \"Learning Relation Alignment for Calibrated Cross-modal Retrieval\",\n author = \"Ren, Shuhuai and\n Lin, Junyang and\n Zhao, Guangxiang and\n Men, Rui and\n Yang, An and\n Zhou, Jingren and\n Sun, Xu and\n Yang, Hongxia\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.43/\",\n doi = \"10.18653/v1/2021.acl-long.43\",\n pages = \"514--524\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.43.pdf", "site": "https://aclanthology.org/2021.acl-long.43/", "pdf_size": 1768238, "gs_citation": 37, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=17342232825366470049&as_sdt=40005&sciodt=0,10&hl=en", "gs_version_total": 6, "aff": "MOE Key Lab of Computational Linguistics, School of EECS, Peking University+Center for Data Science, Peking University; Alibaba Group, China; Alibaba Group, China; Alibaba Group, China; Alibaba Group, China; Alibaba Group, China; MOE Key Lab of Computational Linguistics, School of EECS, Peking University+Center for Data Science, Peking University; Alibaba Group, China", "aff_domain": "stu.pku.edu.cn;alibaba-inc.com;pku.edu.cn;alibaba-inc.com;alibaba-inc.com;alibaba-inc.com;pku.edu.cn;alibaba-inc.com", "email": "stu.pku.edu.cn;alibaba-inc.com;pku.edu.cn;alibaba-inc.com;alibaba-inc.com;alibaba-inc.com;pku.edu.cn;alibaba-inc.com", "github": "https://github.com/lancopku/IAIS", "project": "", "author_num": 8, "aff_unique_index": "0+0;1;1;1;1;1;0+0;1", "aff_unique_norm": "Peking University;Alibaba Group", "aff_unique_dep": "School of EECS;", "aff_unique_url": "http://www.pku.edu.cn;https://www.alibaba.com", "aff_unique_abbr": "PKU;Alibaba", "aff_campus_unique_index": "1;1", "aff_campus_unique": ";Beijing", "aff_country_unique_index": "0+0;0;0;0;0;0;0+0;0", "aff_country_unique": "China" }, { "id": "2021.acl-long.367", "title": "Learning Span-Level Interactions for Aspect Sentiment Triplet Extraction", "track": "main", "status": "Long", "award": false, "abstract": "Aspect Sentiment Triplet Extraction (ASTE) is the most recent subtask of ABSA which outputs triplets of an aspect target, its associated sentiment, and the corresponding opinion term. Recent models perform the triplet extraction in an end-to-end manner but heavily rely on the interactions between each target word and opinion word. Thereby, they cannot perform well on targets and opinions which contain multiple words. Our proposed span-level approach explicitly considers the interaction between the whole spans of targets and opinions when predicting their sentiment relation. Thus, it can make predictions with the semantics of whole spans, ensuring better sentiment consistency. To ease the high computational cost caused by span enumeration, we propose a dual-channel span pruning strategy by incorporating supervision from the Aspect Term Extraction (ATE) and Opinion Term Extraction (OTE) tasks. This strategy not only improves computational efficiency but also distinguishes the opinion and target spans more properly. Our framework simultaneously achieves strong performance for the ASTE as well as ATE and OTE tasks. In particular, our analysis shows that our span-level approach achieves more significant improvements over the baselines on triplets with multi-word targets or opinions.", "author": "Lu Xu; Yew Ken Chia; Lidong Bing", "authorids": "/l/lu-xu/; /y/yew-ken-chia/; /l/lidong-bing/", "bibtex": "@inproceedings{xu-etal-2021-learning,\n title = \"Learning Span-Level Interactions for Aspect Sentiment Triplet Extraction\",\n author = \"Xu, Lu and\n Chia, Yew Ken and\n Bing, Lidong\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.367/\",\n doi = \"10.18653/v1/2021.acl-long.367\",\n pages = \"4755--4766\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.367.pdf", "site": "https://aclanthology.org/2021.acl-long.367/", "pdf_size": 1565004, "gs_citation": 227, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=9427273931604571710&as_sdt=5,44&sciodt=0,44&hl=en", "gs_version_total": 5, "aff": "Singapore University of Technology and Design + DAMO Academy, Alibaba Group; Singapore University of Technology and Design + DAMO Academy, Alibaba Group; DAMO Academy, Alibaba Group", "aff_domain": "mymail.sutd.edu.sg;mymail.sutd.edu.sg;alibaba-inc.com", "email": "mymail.sutd.edu.sg;mymail.sutd.edu.sg;alibaba-inc.com", "github": "https://github.com/chiayewken/Span-ASTE", "project": "", "author_num": 3, "aff_unique_index": "0+1;0+1;1", "aff_unique_norm": "Singapore University of Technology and Design;Alibaba Group", "aff_unique_dep": ";DAMO Academy", "aff_unique_url": "https://www.sutd.edu.sg;https://www.alibaba-group.com", "aff_unique_abbr": "SUTD;Alibaba", "aff_campus_unique_index": ";", "aff_campus_unique": "", "aff_country_unique_index": "0+1;0+1;1", "aff_country_unique": "Singapore;China" }, { "id": "2021.acl-long.235", "title": "Learning Syntactic Dense Embedding with Correlation Graph for Automatic Readability Assessment", "track": "main", "status": "Long", "award": false, "abstract": "Deep learning models for automatic readability assessment generally discard linguistic features traditionally used in machine learning models for the task. We propose to incorporate linguistic features into neural network models by learning syntactic dense embeddings based on linguistic features. To cope with the relationships between the features, we form a correlation graph among features and use it to learn their embeddings so that similar features will be represented by similar embeddings. Experiments with six data sets of two proficiency levels demonstrate that our proposed methodology can complement BERT-only model to achieve significantly better performances for automatic readability assessment.", "author": "Xinying Qiu; Yuan Chen; Hanwu Chen; Jian-Yun Nie; Yuming Shen; Dawei Lu", "authorids": "/x/xin-ying-qiu/; /y/yuan-chen/; /h/hanwu-chen/; /j/jian-yun-nie/; /y/yuming-shen/; /d/dawei-lu/", "bibtex": "@inproceedings{qiu-etal-2021-learning,\n title = \"Learning Syntactic Dense Embedding with Correlation Graph for Automatic Readability Assessment\",\n author = \"Qiu, Xinying and\n Chen, Yuan and\n Chen, Hanwu and\n Nie, Jian-Yun and\n Shen, Yuming and\n Lu, Dawei\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.235/\",\n doi = \"10.18653/v1/2021.acl-long.235\",\n pages = \"3013--3025\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.235.pdf", "site": "https://aclanthology.org/2021.acl-long.235/", "pdf_size": 2297224, "gs_citation": 20, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=3081096905767790337&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 5, "aff": "School of Information Science and Technology; School of Information Science and Technology; School of Information Science and Technology; Department of Computer Science and Operations Research, University of Montreal, Canada; School of Information Science and Technology; School of Liberal Arts, Renmin University of China", "aff_domain": "foxmail.com; ; ;iro.umontreal.ca;163.com;163.com", "email": "foxmail.com; ; ;iro.umontreal.ca;163.com;163.com", "github": "", "project": "", "author_num": 6, "aff_unique_index": "0;0;0;1;0;2", "aff_unique_norm": "School of Information Science and Technology;University of Montreal;Renmin University of China", "aff_unique_dep": "Information Science and Technology;Department of Computer Science and Operations Research;School of Liberal Arts", "aff_unique_url": ";https://www.mcgill.ca;http://www.ruc.edu.cn", "aff_unique_abbr": ";McGill;RUC", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "1;2", "aff_country_unique": ";Canada;China" }, { "id": "2021.acl-long.487", "title": "Learning from Miscellaneous Other-Class Words for Few-shot Named Entity Recognition", "track": "main", "status": "Long", "award": false, "abstract": "Few-shot Named Entity Recognition (NER) exploits only a handful of annotations to iden- tify and classify named entity mentions. Pro- totypical network shows superior performance on few-shot NER. However, existing prototyp- ical methods fail to differentiate rich seman- tics in other-class words, which will aggravate overfitting under few shot scenario. To address the issue, we propose a novel model, Mining Undefined Classes from Other-class (MUCO), that can automatically induce different unde- fined classes from the other class to improve few-shot NER. With these extra-labeled unde- fined classes, our method will improve the dis- criminative ability of NER classifier and en- hance the understanding of predefined classes with stand-by semantic knowledge. Experi- mental results demonstrate that our model out- performs five state-of-the-art models in both 1- shot and 5-shots settings on four NER bench- marks. We will release the code upon accep- tance. The source code is released on https: //github.com/shuaiwa16/OtherClassNER.git.", "author": "Meihan Tong; Shuai Wang; Bin Xu; Yixin Cao; Minghui Liu; Lei Hou; Juanzi Li", "authorids": "/m/meihan-tong/; /s/shuai-wang/; /b/bin-xu/; /y/yixin-cao/; /m/minghui-liu/; /l/lei-hou/; /j/juanzi-li/", "bibtex": "@inproceedings{tong-etal-2021-learning,\n title = \"Learning from Miscellaneous Other-Class Words for Few-shot Named Entity Recognition\",\n author = \"Tong, Meihan and\n Wang, Shuai and\n Xu, Bin and\n Cao, Yixin and\n Liu, Minghui and\n Hou, Lei and\n Li, Juanzi\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.487/\",\n doi = \"10.18653/v1/2021.acl-long.487\",\n pages = \"6236--6247\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.487.pdf", "site": "https://aclanthology.org/2021.acl-long.487/", "pdf_size": 729256, "gs_citation": 62, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=16619568431685794173&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 7, "aff": "Knowledge Engineering Laboratory, Tsinghua University, Beijing, China; SLP Group, AI Technology Department, JOYY Inc, China; Knowledge Engineering Laboratory, Tsinghua University, Beijing, China; S-Lab Nanyang Technological University, Singapore; Knowledge Engineering Laboratory, Tsinghua University, Beijing, China; Knowledge Engineering Laboratory, Tsinghua University, Beijing, China; Knowledge Engineering Laboratory, Tsinghua University, Beijing, China", "aff_domain": "gmail.com;yy.com;tsinghua.edu.cn;gmail.com;mails.tsinghua.edu.cn;gmail.com;tsinghua.edu.cn", "email": "gmail.com;yy.com;tsinghua.edu.cn;gmail.com;mails.tsinghua.edu.cn;gmail.com;tsinghua.edu.cn", "github": "https://github.com/shuaiwa16/OtherClassNER.git", "project": "", "author_num": 7, "aff_unique_index": "0;1;0;2;0;0;0", "aff_unique_norm": "Tsinghua University;JOYY Inc;Nanyang Technological University", "aff_unique_dep": "Knowledge Engineering Laboratory;AI Technology Department;S-Lab", "aff_unique_url": "https://www.tsinghua.edu.cn;https://www.joyyinc.com;https://www.ntu.edu.sg", "aff_unique_abbr": "THU;JOYY;NTU", "aff_campus_unique_index": "0;0;0;0;0", "aff_campus_unique": "Beijing;", "aff_country_unique_index": "0;0;0;1;0;0;0", "aff_country_unique": "China;Singapore" }, { "id": "2021.acl-long.57", "title": "Learning from Perturbations: Diverse and Informative Dialogue Generation with Inverse Adversarial Training", "track": "main", "status": "Long", "award": false, "abstract": "In this paper, we propose Inverse Adversarial Training (IAT) algorithm for training neural dialogue systems to avoid generic responses and model dialogue history better. In contrast to standard adversarial training algorithms, IAT encourages the model to be sensitive to the perturbation in the dialogue history and therefore learning from perturbations. By giving higher rewards for responses whose output probability reduces more significantly when dialogue history is perturbed, the model is encouraged to generate more diverse and consistent responses. By penalizing the model when generating the same response given perturbed dialogue history, the model is forced to better capture dialogue history and generate more informative responses. Experimental results on two benchmark datasets show that our approach can better model dialogue history and generate more diverse and consistent responses. In addition, we point out a problem of the widely used maximum mutual information (MMI) based methods for improving the diversity of dialogue response generation models and demonstrate it empirically.", "author": "Wangchunshu Zhou; Qifei Li; Chenle Li", "authorids": "/w/wangchunshu-zhou/; /q/qifei-li/; /c/chenle-li/", "bibtex": "@inproceedings{zhou-etal-2021-learning,\n title = \"Learning from Perturbations: Diverse and Informative Dialogue Generation with Inverse Adversarial Training\",\n author = \"Zhou, Wangchunshu and\n Li, Qifei and\n Li, Chenle\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.57/\",\n doi = \"10.18653/v1/2021.acl-long.57\",\n pages = \"694--703\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.57.pdf", "site": "https://aclanthology.org/2021.acl-long.57/", "pdf_size": 1138111, "gs_citation": 25, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=10736049730692193101&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 4, "aff": "Beihang University, Beijing, China; Beihang University, Beijing, China; Beihang University, Beijing, China", "aff_domain": "buaa.edu.cn; ; ", "email": "buaa.edu.cn; ; ", "github": "", "project": "", "author_num": 3, "aff_unique_index": "0;0;0", "aff_unique_norm": "Beihang University", "aff_unique_dep": "", "aff_unique_url": "http://www.buaa.edu.cn", "aff_unique_abbr": "BUAA", "aff_campus_unique_index": "0;0;0", "aff_campus_unique": "Beijing", "aff_country_unique_index": "0;0;0", "aff_country_unique": "China" }, { "id": "2021.acl-long.132", "title": "Learning from the Worst: Dynamically Generated Datasets to Improve Online Hate Detection", "track": "main", "status": "Long", "award": false, "abstract": "We present a human-and-model-in-the-loop process for dynamically generating datasets and training better performing and more robust hate detection models. We provide a new dataset of 40,000 entries, generated and labelled by trained annotators over four rounds of dynamic data creation. It includes 15,000 challenging perturbations and each hateful entry has fine-grained labels for the type and target of hate. Hateful entries make up 54% of the dataset, which is substantially higher than comparable datasets. We show that model performance is substantially improved using this approach. Models trained on later rounds of data collection perform better on test sets and are harder for annotators to trick. They also have better performance on HateCheck, a suite of functional tests for online hate detection. We provide the code, dataset and annotation guidelines for other researchers to use.", "author": "Bertie Vidgen; Tristan Thrush; Zeerak Waseem; Douwe Kiela", "authorids": "/b/bertie-vidgen/; /t/tristan-thrush/; /z/zeerak-talat/; /d/douwe-kiela/", "bibtex": "@inproceedings{vidgen-etal-2021-learning,\n title = \"Learning from the Worst: Dynamically Generated Datasets to Improve Online Hate Detection\",\n author = \"Vidgen, Bertie and\n Thrush, Tristan and\n Waseem, Zeerak and\n Kiela, Douwe\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.132/\",\n doi = \"10.18653/v1/2021.acl-long.132\",\n pages = \"1667--1682\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.132.pdf", "site": "https://aclanthology.org/2021.acl-long.132/", "pdf_size": 330480, "gs_citation": 277, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=7265559494033067667&as_sdt=5,44&sciodt=0,44&hl=en", "gs_version_total": 4, "aff": "The Alan Turing Institute; Facebook AI Research; University of Sheffield; Facebook AI Research", "aff_domain": "turing.ac.uk; ; ; ", "email": "turing.ac.uk; ; ; ", "github": "", "project": "", "author_num": 4, "aff_unique_index": "0;1;2;1", "aff_unique_norm": "The Alan Turing Institute;Facebook;University of Sheffield", "aff_unique_dep": ";Facebook AI Research;", "aff_unique_url": "https://www.turing.ac.uk;https://research.facebook.com;https://www.sheffield.ac.uk", "aff_unique_abbr": "ATI;FAIR;Sheffield", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0;1;0;1", "aff_country_unique": "United Kingdom;United States" }, { "id": "2021.acl-long.438", "title": "Learning to Ask Conversational Questions by Optimizing Levenshtein Distance", "track": "main", "status": "Long", "award": false, "abstract": "Conversational Question Simplification (CQS) aims to simplify self-contained questions into conversational ones by incorporating some conversational characteristics, e.g., anaphora and ellipsis. Existing maximum likelihood estimation based methods often get trapped in easily learned tokens as all tokens are treated equally during training. In this work, we introduce a Reinforcement Iterative Sequence Editing (RISE) framework that optimizes the minimum Levenshtein distance through explicit editing actions. RISE is able to pay attention to tokens that are related to conversational characteristics. To train RISE, we devise an Iterative Reinforce Training (IRT) algorithm with a Dynamic Programming based Sampling (DPS) process to improve exploration. Experimental results on two benchmark datasets show that RISE significantly outperforms state-of-the-art methods and generalizes well on unseen data.", "author": "Zhongkun Liu; Pengjie Ren; Zhumin Chen; Zhaochun Ren; Maarten de Rijke; Ming Zhou", "authorids": "/z/zhongkun-liu/; /p/pengjie-ren/; /z/zhumin-chen/; /z/zhaochun-ren/; /m/maarten-de-rijke/; /m/ming-zhou/", "bibtex": "@inproceedings{liu-etal-2021-learning-ask,\n title = \"Learning to Ask Conversational Questions by Optimizing {L}evenshtein Distance\",\n author = \"Liu, Zhongkun and\n Ren, Pengjie and\n Chen, Zhumin and\n Ren, Zhaochun and\n de Rijke, Maarten and\n Zhou, Ming\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.438/\",\n doi = \"10.18653/v1/2021.acl-long.438\",\n pages = \"5638--5650\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.438.pdf", "site": "https://aclanthology.org/2021.acl-long.438/", "pdf_size": 661796, "gs_citation": 17, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=2485293079285960521&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 10, "aff": "School of Computer Science and Technology, Shandong University, China; School of Computer Science and Technology, Shandong University, China; School of Computer Science and Technology, Shandong University, China; School of Computer Science and Technology, Shandong University, China; University of Amsterdam & Ahold Delhaize; Sinovation Ventures, China", "aff_domain": "sdu.edu.cn;sdu.edu.cn;sdu.edu.cn;sdu.edu.cn;uva.nl;hotmail.com", "email": "sdu.edu.cn;sdu.edu.cn;sdu.edu.cn;sdu.edu.cn;uva.nl;hotmail.com", "github": "", "project": "", "author_num": 6, "aff_unique_index": "0;0;0;0;1;2", "aff_unique_norm": "Shandong University;University of Amsterdam;Sinovation Ventures", "aff_unique_dep": "School of Computer Science and Technology;;", "aff_unique_url": "http://www.sdu.edu.cn;https://www.uva.nl;https://www.sinovationventures.com", "aff_unique_abbr": "SDU;UvA;", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0;0;0;0;1;0", "aff_country_unique": "China;Netherlands" }, { "id": "2021.acl-long.415", "title": "Learning to Explain: Generating Stable Explanations Fast", "track": "main", "status": "Long", "award": false, "abstract": "The importance of explaining the outcome of a machine learning model, especially a black-box model, is widely acknowledged. Recent approaches explain an outcome by identifying the contributions of input features to this outcome. In environments involving large black-box models or complex inputs, this leads to computationally demanding algorithms. Further, these algorithms often suffer from low stability, with explanations varying significantly across similar examples. In this paper, we propose a Learning to Explain (L2E) approach that learns the behaviour of an underlying explanation algorithm simultaneously from all training examples. Once the explanation algorithm is distilled into an explainer network, it can be used to explain new instances. Our experiments on three classification tasks, which compare our approach to six explanation algorithms, show that L2E is between 5 and 7.5\u00d710\u02c64 times faster than these algorithms, while generating more stable explanations, and having comparable faithfulness to the black-box model.", "author": "Xuelin Situ; Ingrid Zukerman; Cecile Paris; Sameen Maruf; Gholamreza Haffari", "authorids": "/x/xuelin-situ/; /i/ingrid-zukerman/; /c/cecile-paris/; /s/sameen-maruf/; /g/gholamreza-haffari/", "bibtex": "@inproceedings{situ-etal-2021-learning,\n title = \"Learning to Explain: Generating Stable Explanations Fast\",\n author = \"Situ, Xuelin and\n Zukerman, Ingrid and\n Paris, Cecile and\n Maruf, Sameen and\n Haffari, Gholamreza\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.415/\",\n doi = \"10.18653/v1/2021.acl-long.415\",\n pages = \"5340--5355\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.415.pdf", "site": "https://aclanthology.org/2021.acl-long.415/", "pdf_size": 656260, "gs_citation": 39, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=6855714424405614269&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 5, "aff": "Dept of Data Science and AI, Faculty of IT, Monash University, Australia; Dept of Data Science and AI, Faculty of IT, Monash University, Australia; CSIRO Data61, Australia; Dept of Data Science and AI, Faculty of IT, Monash University, Australia; Dept of Data Science and AI, Faculty of IT, Monash University, Australia", "aff_domain": "monash.edu;monash.edu;data61.csiro.au;monash.edu;monash.edu", "email": "monash.edu;monash.edu;data61.csiro.au;monash.edu;monash.edu", "github": "", "project": "", "author_num": 5, "aff_unique_index": "0;0;1;0;0", "aff_unique_norm": "Monash University;CSIRO Data61", "aff_unique_dep": "Dept of Data Science and AI;", "aff_unique_url": "https://www.monash.edu;https://www.csiro.au/en/Research/Data61", "aff_unique_abbr": "Monash;CSIRO Data61", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0;0;0;0;0", "aff_country_unique": "Australia" }, { "id": "2021.acl-short.82", "title": "Learning to Generate Task-Specific Adapters from Task Description", "track": "main", "status": "Short", "award": false, "abstract": "Pre-trained text-to-text transformers such as BART have achieved impressive performance across a range of NLP tasks. Recent study further shows that they can learn to generalize to novel tasks, by including task descriptions as part of the source sequence and training the model with (source, target) examples. At test time, these fine-tuned models can make inferences on new tasks using the new task descriptions as part of the input. However, this approach has potential limitations, as the model learns to solve individual (source, target) examples (i.e., at the instance level), instead of learning to solve tasks by taking all examples within a task as a whole (i.e., at the task level). To this end, we introduce Hypter, a framework that improves text-to-text transformer\u2019s generalization ability to unseen tasks by training a hypernetwork to generate task-specific, light-weight adapters from task descriptions. Experiments on ZEST dataset and a synthetic SQuAD dataset demonstrate that Hypter improves upon fine-tuning baselines. Notably, when using BART-Large as the main network, Hypter brings 11.3% comparative improvement on ZEST dataset.", "author": "Qinyuan Ye; Xiang Ren", "authorids": "/q/qinyuan-ye/; /x/xiang-ren/", "bibtex": "@inproceedings{ye-ren-2021-learning,\n title = \"Learning to Generate Task-Specific Adapters from Task Description\",\n author = \"Ye, Qinyuan and\n Ren, Xiang\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-short.82/\",\n doi = \"10.18653/v1/2021.acl-short.82\",\n pages = \"646--653\"\n}", "pdf": "https://aclanthology.org/2021.acl-short.82.pdf", "site": "https://aclanthology.org/2021.acl-short.82/", "pdf_size": 349468, "gs_citation": 29, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=12790260313238724663&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 5, "aff": "University of Southern California; University of Southern California", "aff_domain": "usc.edu;usc.edu", "email": "usc.edu;usc.edu", "github": "https://github.com/INK-USC/hypter", "project": "", "author_num": 2, "aff_unique_index": "0;0", "aff_unique_norm": "University of Southern California", "aff_unique_dep": "", "aff_unique_url": "https://www.usc.edu", "aff_unique_abbr": "USC", "aff_campus_unique_index": "0;0", "aff_campus_unique": "Los Angeles", "aff_country_unique_index": "0;0", "aff_country_unique": "United States" }, { "id": "2021.acl-long.434", "title": "Learning to Perturb Word Embeddings for Out-of-distribution QA", "track": "main", "status": "Long", "award": false, "abstract": "QA models based on pretrained language models have achieved remarkable performance on various benchmark datasets. However, QA models do not generalize well to unseen data that falls outside the training distribution, due to distributional shifts. Data augmentation (DA) techniques which drop/replace words have shown to be effective in regularizing the model from overfitting to the training data. Yet, they may adversely affect the QA tasks since they incur semantic changes that may lead to wrong answers for the QA task. To tackle this problem, we propose a simple yet effective DA method based on a stochastic noise generator, which learns to perturb the word embedding of the input questions and context without changing their semantics. We validate the performance of the QA models trained with our word embedding perturbation on a single source dataset, on five different target domains. The results show that our method significantly outperforms the baseline DA methods. Notably, the model trained with ours outperforms the model trained with more than 240K artificially generated QA pairs.", "author": "Seanie Lee; Minki Kang; Juho Lee; Sung Ju Hwang", "authorids": "/s/seanie-lee/; /m/minki-kang/; /j/juho-lee/; /s/sung-ju-hwang/", "bibtex": "@inproceedings{lee-etal-2021-learning-perturb,\n title = \"Learning to Perturb Word Embeddings for Out-of-distribution {QA}\",\n author = \"Lee, Seanie and\n Kang, Minki and\n Lee, Juho and\n Hwang, Sung Ju\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.434/\",\n doi = \"10.18653/v1/2021.acl-long.434\",\n pages = \"5583--5595\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.434.pdf", "site": "https://aclanthology.org/2021.acl-long.434/", "pdf_size": 974769, "gs_citation": 22, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=924344032171213587&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 6, "aff": "KAIST1; KAIST1; KAIST1; KAIST1+AITRICS2", "aff_domain": "kaist.ac.kr;kaist.ac.kr;kaist.ac.kr;kaist.ac.kr", "email": "kaist.ac.kr;kaist.ac.kr;kaist.ac.kr;kaist.ac.kr", "github": "", "project": "", "author_num": 4, "aff_unique_index": "0;0;0;0+1", "aff_unique_norm": "Korea Advanced Institute of Science and Technology;AITRICS", "aff_unique_dep": ";", "aff_unique_url": "https://www.kaist.ac.kr;https://www.aitrics.com", "aff_unique_abbr": "KAIST;AITRICS", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0;0;0;0+0", "aff_country_unique": "South Korea" }, { "id": "2021.acl-short.106", "title": "Learning to Solve NLP Tasks in an Incremental Number of Languages", "track": "main", "status": "Short", "award": false, "abstract": "In real scenarios, a multilingual model trained to solve NLP tasks on a set of languages can be required to support new languages over time. Unfortunately, the straightforward retraining on a dataset containing annotated examples for all the languages is both expensive and time-consuming, especially when the number of target languages grows. Moreover, the original annotated material may no longer be available due to storage or business constraints. Re-training only with the new language data will inevitably result in Catastrophic Forgetting of previously acquired knowledge. We propose a Continual Learning strategy that updates a model to support new languages over time, while maintaining consistent results on previously learned languages. We define a Teacher-Student framework where the existing model \u201cteaches\u201d to a student model its knowledge about the languages it supports, while the student is also trained on a new language. We report an experimental evaluation in several tasks including Sentence Classification, Relational Learning and Sequence Labeling.", "author": "Giuseppe Castellucci; Simone Filice; Danilo Croce; Roberto Basili", "authorids": "/g/giuseppe-castellucci/; /s/simone-filice/; /d/danilo-croce/; /r/roberto-basili/", "bibtex": "@inproceedings{castellucci-etal-2021-learning,\n title = \"Learning to Solve {NLP} Tasks in an Incremental Number of Languages\",\n author = \"Castellucci, Giuseppe and\n Filice, Simone and\n Croce, Danilo and\n Basili, Roberto\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-short.106/\",\n doi = \"10.18653/v1/2021.acl-short.106\",\n pages = \"837--847\"\n}", "pdf": "https://aclanthology.org/2021.acl-short.106.pdf", "site": "https://aclanthology.org/2021.acl-short.106/", "pdf_size": 540315, "gs_citation": 21, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=71389756180212871&as_sdt=5,44&sciodt=0,44&hl=en", "gs_version_total": 6, "aff": "Amazon Seattle, USA; Amazon Tel Aviv, Israel; Dept. of Enterprise Engineering University of Rome, Tor Vergata Roma, Italy; Dept. of Enterprise Engineering University of Rome, Tor Vergata Roma, Italy", "aff_domain": "amazon.com;amazon.com;info.uniroma2.it;info.uniroma2.it", "email": "amazon.com;amazon.com;info.uniroma2.it;info.uniroma2.it", "github": "", "project": "", "author_num": 4, "aff_unique_index": "0;0;1;1", "aff_unique_norm": "Amazon;University of Rome Tor Vergata", "aff_unique_dep": ";Dept. of Enterprise Engineering", "aff_unique_url": "https://www.amazon.com;https://www.uniroma2.it", "aff_unique_abbr": "Amazon;UniRoma2", "aff_campus_unique_index": "0;1;2;2", "aff_campus_unique": "Seattle;Tel Aviv;Roma", "aff_country_unique_index": "0;1;2;2", "aff_country_unique": "United States;Israel;Italy" }, { "id": "2021.acl-long.231", "title": "LeeBERT: Learned Early Exit for BERT with cross-level optimization", "track": "main", "status": "Long", "award": false, "abstract": "Pre-trained language models like BERT are performant in a wide range of natural language tasks. However, they are resource exhaustive and computationally expensive for industrial scenarios. Thus, early exits are adopted at each layer of BERT to perform adaptive computation by predicting easier samples with the first few layers to speed up the inference. In this work, to improve efficiency without performance drop, we propose a novel training scheme called Learned Early Exit for BERT (LeeBERT). First, we ask each exit to learn from each other, rather than learning only from the last layer. Second, the weights of different loss terms are learned, thus balancing off different objectives. We formulate the optimization of LeeBERT as a bi-level optimization problem, and we propose a novel cross-level optimization (CLO) algorithm to improve the optimization results. Experiments on the GLUE benchmark show that our proposed methods improve the performance of the state-of-the-art (SOTA) early exit methods for pre-trained models.", "author": "Wei Zhu", "authorids": "/w/wei-zhu/", "bibtex": "@inproceedings{zhu-2021-leebert,\n title = \"{L}ee{BERT}: Learned Early Exit for {BERT} with cross-level optimization\",\n author = \"Zhu, Wei\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.231/\",\n doi = \"10.18653/v1/2021.acl-long.231\",\n pages = \"2968--2980\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.231.pdf", "site": "https://aclanthology.org/2021.acl-long.231/", "pdf_size": 1426969, "gs_citation": 63, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=6046812559383544507&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 4, "aff": "East China Normal University, Shanghai, China+Pingan Health Tech, Shanghai, China", "aff_domain": "stu.ecnu.edu.cn", "email": "stu.ecnu.edu.cn", "github": "", "project": "", "author_num": 1, "aff_unique_index": "0+1", "aff_unique_norm": "East China Normal University;Pingan Health Tech", "aff_unique_dep": ";", "aff_unique_url": "http://www.ecnu.edu.cn;", "aff_unique_abbr": "ECNU;", "aff_campus_unique_index": "0", "aff_campus_unique": "Shanghai;", "aff_country_unique_index": "0+0", "aff_country_unique": "China" }, { "id": "2021.acl-long.508", "title": "Length-Adaptive Transformer: Train Once with Length Drop, Use Anytime with Search", "track": "main", "status": "Long", "award": false, "abstract": "Despite transformers\u2019 impressive accuracy, their computational cost is often prohibitive to use with limited computational resources. Most previous approaches to improve inference efficiency require a separate model for each possible computational budget. In this paper, we extend PoWER-BERT (Goyal et al., 2020) and propose Length-Adaptive Transformer that can be used for various inference scenarios after one-shot training. We train a transformer with LengthDrop, a structural variant of dropout, which stochastically determines a sequence length at each layer. We then conduct a multi-objective evolutionary search to find a length configuration that maximizes the accuracy and minimizes the efficiency metric under any given computational budget. Additionally, we significantly extend the applicability of PoWER-BERT beyond sequence-level classification into token-level classification with Drop-and-Restore process that drops word-vectors temporarily in intermediate layers and restores at the last layer if necessary. We empirically verify the utility of the proposed approach by demonstrating the superior accuracy-efficiency trade-off under various setups, including span-based question answering and text classification. Code is available at https://github.com/clovaai/lengthadaptive-transformer.", "author": "Gyuwan Kim; Kyunghyun Cho", "authorids": "/g/gyuwan-kim/; /k/kyunghyun-cho/", "bibtex": "@inproceedings{kim-cho-2021-length,\n title = \"Length-Adaptive Transformer: Train Once with Length Drop, Use Anytime with Search\",\n author = \"Kim, Gyuwan and\n Cho, Kyunghyun\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.508/\",\n doi = \"10.18653/v1/2021.acl-long.508\",\n pages = \"6501--6511\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.508.pdf", "site": "https://aclanthology.org/2021.acl-long.508/", "pdf_size": 435131, "gs_citation": 112, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=3258624650389778946&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 7, "aff": "Clova AI, NAVER Corp.; New York University", "aff_domain": "navercorp.com;nyu.edu", "email": "navercorp.com;nyu.edu", "github": "https://github.com/clovaai/length-adaptive-transformer", "project": "", "author_num": 2, "aff_unique_index": "0;1", "aff_unique_norm": "NAVER Corp.;New York University", "aff_unique_dep": "Clova AI;", "aff_unique_url": "https://www.naver.com;https://www.nyu.edu", "aff_unique_abbr": "NAVER;NYU", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0;1", "aff_country_unique": "South Korea;United States" }, { "id": "2021.acl-long.120", "title": "Leveraging Type Descriptions for Zero-shot Named Entity Recognition and Classification", "track": "main", "status": "Long", "award": false, "abstract": "A common issue in real-world applications of named entity recognition and classification (NERC) is the absence of annotated data for the target entity classes during training. Zero-shot learning approaches address this issue by learning models from classes with training data that can predict classes without it. This paper presents the first approach for zero-shot NERC, introducing novel architectures that leverage the fact that textual descriptions for many entity classes occur naturally. We address the zero-shot NERC specific challenge that the not-an-entity class is not well defined as different entity classes are considered in training and testing. For evaluation, we adapt two datasets, OntoNotes and MedMentions, emulating the difficulty of real-world zero-shot learning by testing models on the rarest entity classes. Our proposed approach outperforms baselines adapted from machine reading comprehension and zero-shot text classification. Furthermore, we assess the effect of different class descriptions for this task.", "author": "Rami Aly; Andreas Vlachos; Ryan McDonald", "authorids": "/r/rami-aly/; /a/andreas-vlachos/; /r/ryan-mcdonald/", "bibtex": "@inproceedings{aly-etal-2021-leveraging,\n title = \"Leveraging Type Descriptions for Zero-shot Named Entity Recognition and Classification\",\n author = \"Aly, Rami and\n Vlachos, Andreas and\n McDonald, Ryan\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.120/\",\n doi = \"10.18653/v1/2021.acl-long.120\",\n pages = \"1516--1528\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.120.pdf", "site": "https://aclanthology.org/2021.acl-long.120/", "pdf_size": 442194, "gs_citation": 37, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=7757651166174121490&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 3, "aff": "Computer Laboratory, University of Cambridge, U.K.; Computer Laboratory, University of Cambridge, U.K.; ASAPP", "aff_domain": "cl.cam.ac.uk;cl.cam.ac.uk;asapp.com", "email": "cl.cam.ac.uk;cl.cam.ac.uk;asapp.com", "github": "", "project": "", "author_num": 3, "aff_unique_index": "0;0;1", "aff_unique_norm": "University of Cambridge;ASAPP", "aff_unique_dep": "Computer Laboratory;", "aff_unique_url": "https://www.cam.ac.uk;https://www.asapp.com", "aff_unique_abbr": "Cambridge;ASAPP", "aff_campus_unique_index": "0;0", "aff_campus_unique": "Cambridge;", "aff_country_unique_index": "0;0;1", "aff_country_unique": "United Kingdom;United States" }, { "id": "2021.acl-long.410", "title": "LexFit: Lexical Fine-Tuning of Pretrained Language Models", "track": "main", "status": "Long", "award": false, "abstract": "Transformer-based language models (LMs) pretrained on large text collections implicitly store a wealth of lexical semantic knowledge, but it is non-trivial to extract that knowledge effectively from their parameters. Inspired by prior work on semantic specialization of static word embedding (WE) models, we show that it is possible to expose and enrich lexical knowledge from the LMs, that is, to specialize them to serve as effective and universal \u201cdecontextualized\u201d word encoders even when fed input words \u201cin isolation\u201d (i.e., without any context). Their transformation into such word encoders is achieved through a simple and efficient lexical fine-tuning procedure (termed LexFit) based on dual-encoder network structures. Further, we show that LexFit can yield effective word encoders even with limited lexical supervision and, via cross-lingual transfer, in different languages without any readily available external knowledge. Our evaluation over four established, structurally different lexical-level tasks in 8 languages indicates the superiority of LexFit-based WEs over standard static WEs (e.g., fastText) and WEs from vanilla LMs. Other extensive experiments and ablation studies further profile the LexFit framework, and indicate best practices and performance variations across LexFit variants, languages, and lexical tasks, also directly questioning the usefulness of traditional WE models in the era of large neural models.", "author": "Ivan Vuli\u0107; Edoardo Maria Ponti; Anna Korhonen; Goran Glava\u0161", "authorids": "/i/ivan-vulic/; /e/edoardo-maria-ponti/; /a/anna-korhonen/; /g/goran-glavas/", "bibtex": "@inproceedings{vulic-etal-2021-lexfit,\n title = \"{L}ex{F}it: Lexical Fine-Tuning of Pretrained Language Models\",\n author = \"Vuli{\\'c}, Ivan and\n Ponti, Edoardo Maria and\n Korhonen, Anna and\n Glava{\\v{s}}, Goran\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.410/\",\n doi = \"10.18653/v1/2021.acl-long.410\",\n pages = \"5269--5283\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.410.pdf", "site": "https://aclanthology.org/2021.acl-long.410/", "pdf_size": 1096104, "gs_citation": 36, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=7548283592994472698&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 5, "aff": "Language Technology Lab, University of Cambridge, UK+Mila - Quebec AI Institute and McGill University, Canada; Mila - Quebec AI Institute and McGill University, Canada+Language Technology Lab, University of Cambridge, UK; Language Technology Lab, University of Cambridge, UK; Data and Web Science Group, University of Mannheim, Germany", "aff_domain": "cam.ac.uk;mila.quebec;cam.ac.uk;informatik.uni-mannheim.de", "email": "cam.ac.uk;mila.quebec;cam.ac.uk;informatik.uni-mannheim.de", "github": "", "project": "", "author_num": 4, "aff_unique_index": "0+1;1+0;0;2", "aff_unique_norm": "University of Cambridge;Mila - Quebec AI Institute;University of Mannheim", "aff_unique_dep": "Language Technology Lab;Quebec AI Institute;Data and Web Science Group", "aff_unique_url": "https://www.cam.ac.uk;https://mila.quebec;https://www.uni-mannheim.de", "aff_unique_abbr": "Cambridge;Mila;", "aff_campus_unique_index": "0;0;0", "aff_campus_unique": "Cambridge;", "aff_country_unique_index": "0+1;1+0;0;2", "aff_country_unique": "United Kingdom;Canada;Germany" }, { "id": "2021.acl-long.543", "title": "Lexical Semantic Change Discovery", "track": "main", "status": "Long", "award": false, "abstract": "While there is a large amount of research in the field of Lexical Semantic Change Detection, only few approaches go beyond a standard benchmark evaluation of existing models. In this paper, we propose a shift of focus from change detection to change discovery, i.e., discovering novel word senses over time from the full corpus vocabulary. By heavily fine-tuning a type-based and a token-based approach on recently published German data, we demonstrate that both models can successfully be applied to discover new words undergoing meaning change. Furthermore, we provide an almost fully automated framework for both evaluation and discovery.", "author": "Sinan Kurtyigit; Maike Park; Dominik Schlechtweg; Jonas Kuhn; Sabine Schulte im Walde", "authorids": "/s/sinan-kurtyigit/; /m/maike-park/; /d/dominik-schlechtweg/; /j/jonas-kuhn/; /s/sabine-schulte-im-walde/", "bibtex": "@inproceedings{kurtyigit-etal-2021-lexical,\n title = \"Lexical Semantic Change Discovery\",\n author = \"Kurtyigit, Sinan and\n Park, Maike and\n Schlechtweg, Dominik and\n Kuhn, Jonas and\n Schulte im Walde, Sabine\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.543/\",\n doi = \"10.18653/v1/2021.acl-long.543\",\n pages = \"6985--6998\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.543.pdf", "site": "https://aclanthology.org/2021.acl-long.543/", "pdf_size": 6642675, "gs_citation": 34, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=413067918681878345&as_sdt=400005&sciodt=0,14&hl=en", "gs_version_total": 10, "aff": "Institute for Natural Language Processing, University of Stuttgart; Leibniz Institute for the German Language, Mannheim; Institute for Natural Language Processing, University of Stuttgart; Institute for Natural Language Processing, University of Stuttgart; Institute for Natural Language Processing, University of Stuttgart", "aff_domain": "gmail.com;ids-mannheim.de;ims.uni-stuttgart.de;ims.uni-stuttgart.de;ims.uni-stuttgart.de", "email": "gmail.com;ids-mannheim.de;ims.uni-stuttgart.de;ims.uni-stuttgart.de;ims.uni-stuttgart.de", "github": "", "project": "", "author_num": 5, "aff_unique_index": "0;1;0;0;0", "aff_unique_norm": "University of Stuttgart;Leibniz Institute for the German Language", "aff_unique_dep": "Institute for Natural Language Processing;", "aff_unique_url": "https://www.uni-stuttgart.de;https://www.leibniz-institut.de/", "aff_unique_abbr": ";IFS", "aff_campus_unique_index": "1", "aff_campus_unique": ";Mannheim", "aff_country_unique_index": "0;0;0;0;0", "aff_country_unique": "Germany" }, { "id": "2021.acl-long.454", "title": "Lexicon Enhanced Chinese Sequence Labeling Using BERT Adapter", "track": "main", "status": "Long", "award": false, "abstract": "Lexicon information and pre-trained models, such as BERT, have been combined to explore Chinese sequence labeling tasks due to their respective strengths. However, existing methods solely fuse lexicon features via a shallow and random initialized sequence layer and do not integrate them into the bottom layers of BERT. In this paper, we propose Lexicon Enhanced BERT (LEBERT) for Chinese sequence labeling, which integrates external lexicon knowledge into BERT layers directly by a Lexicon Adapter layer. Compared with existing methods, our model facilitates deep lexicon knowledge fusion at the lower layers of BERT. Experiments on ten Chinese datasets of three tasks including Named Entity Recognition, Word Segmentation, and Part-of-Speech Tagging, show that LEBERT achieves state-of-the-art results.", "author": "Wei Liu; Xiyan Fu; Yue Zhang; Wenming Xiao", "authorids": "/w/wei-liu/; /x/xiyan-fu/; /y/yue-zhang/; /w/wenming-xiao/", "bibtex": "@inproceedings{liu-etal-2021-lexicon,\n title = \"Lexicon Enhanced {C}hinese Sequence Labeling Using {BERT} Adapter\",\n author = \"Liu, Wei and\n Fu, Xiyan and\n Zhang, Yue and\n Xiao, Wenming\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.454/\",\n doi = \"10.18653/v1/2021.acl-long.454\",\n pages = \"5847--5858\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.454.pdf", "site": "https://aclanthology.org/2021.acl-long.454/", "pdf_size": 498097, "gs_citation": 207, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=17045360557203718483&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 6, "aff": "DAMO Academy, Alibaba Group; College of Computer Science, Nankai University; School of Engineering, Westlake University+Institute of Advanced Technology, Westlake Institute for Advanced Study; DAMO Academy, Alibaba Group", "aff_domain": "alibaba-inc.com;mail.nankai.edu.cn;wias.org.cn;alibaba-inc.com", "email": "alibaba-inc.com;mail.nankai.edu.cn;wias.org.cn;alibaba-inc.com", "github": "", "project": "", "author_num": 4, "aff_unique_index": "0;1;2+3;0", "aff_unique_norm": "Alibaba Group;Nankai University;Westlake University;Westlake Institute for Advanced Study", "aff_unique_dep": "DAMO Academy;College of Computer Science;School of Engineering;Institute of Advanced Technology", "aff_unique_url": "https://www.alibaba-group.com;http://www.nankai.edu.cn;https://www.westlake.edu.cn;http://www.wias.org.cn/", "aff_unique_abbr": "Alibaba;Nankai;;WIAS", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0;0;0+0;0", "aff_country_unique": "China" }, { "id": "2021.acl-long.382", "title": "Lexicon Learning for Few Shot Sequence Modeling", "track": "main", "status": "Long", "award": false, "abstract": "Sequence-to-sequence transduction is the core problem in language processing applications as diverse as semantic parsing, machine translation, and instruction following. The neural network models that provide the dominant solution to these problems are brittle, especially in low-resource settings: they fail to generalize correctly or systematically from small datasets. Past work has shown that many failures of systematic generalization arise from neural models\u2019 inability to disentangle lexical phenomena from syntactic ones. To address this, we augment neural decoders with a lexical translation mechanism that generalizes existing copy mechanisms to incorporate learned, decontextualized, token-level translation rules. We describe how to initialize this mechanism using a variety of lexicon learning algorithms, and show that it improves systematic generalization on a diverse set of sequence modeling tasks drawn from cognitive science, formal semantics, and machine translation.", "author": "Ekin Akyurek; Jacob Andreas", "authorids": "/e/ekin-akyurek/; /j/jacob-andreas/", "bibtex": "@inproceedings{akyurek-andreas-2021-lexicon,\n title = \"Lexicon Learning for Few Shot Sequence Modeling\",\n author = \"Akyurek, Ekin and\n Andreas, Jacob\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.382/\",\n doi = \"10.18653/v1/2021.acl-long.382\",\n pages = \"4934--4946\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.382.pdf", "site": "https://aclanthology.org/2021.acl-long.382/", "pdf_size": 671180, "gs_citation": 41, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=10199849032411898098&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 2, "aff": "Massachusetts Institute of Technology; Massachusetts Institute of Technology", "aff_domain": "mit.edu;mit.edu", "email": "mit.edu;mit.edu", "github": "https://github.com/ekinakyurek/lexical", "project": "", "author_num": 2, "aff_unique_index": "0;0", "aff_unique_norm": "Massachusetts Institute of Technology", "aff_unique_dep": "", "aff_unique_url": "https://web.mit.edu", "aff_unique_abbr": "MIT", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0;0", "aff_country_unique": "United States" }, { "id": "2021.acl-short.103", "title": "Lightweight Adapter Tuning for Multilingual Speech Translation", "track": "main", "status": "Short", "award": false, "abstract": "Adapter modules were recently introduced as an efficient alternative to fine-tuning in NLP. Adapter tuning consists in freezing pre-trained parameters of a model and injecting lightweight modules between layers, resulting in the addition of only a small number of task-specific trainable parameters. While adapter tuning was investigated for multilingual neural machine translation, this paper proposes a comprehensive analysis of adapters for multilingual speech translation (ST). Starting from different pre-trained models (a multilingual ST trained on parallel data or a multilingual BART (mBART) trained on non parallel multilingual data), we show that adapters can be used to: (a) efficiently specialize ST to specific language pairs with a low extra cost in terms of parameters, and (b) transfer from an automatic speech recognition (ASR) task and an mBART pre-trained model to a multilingual ST task. Experiments show that adapter tuning offer competitive results to full fine-tuning, while being much more parameter-efficient.", "author": "Hang Le; Juan Pino; Changhan Wang; Jiatao Gu; Didier Schwab; Laurent Besacier", "authorids": "/h/hang-le/; /j/juan-pino/; /c/changhan-wang/; /j/jiatao-gu/; /d/didier-schwab/; /l/laurent-besacier/", "bibtex": "@inproceedings{le-etal-2021-lightweight,\n title = \"Lightweight Adapter Tuning for Multilingual Speech Translation\",\n author = \"Le, Hang and\n Pino, Juan and\n Wang, Changhan and\n Gu, Jiatao and\n Schwab, Didier and\n Besacier, Laurent\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-short.103/\",\n doi = \"10.18653/v1/2021.acl-short.103\",\n pages = \"817--824\"\n}", "pdf": "https://aclanthology.org/2021.acl-short.103.pdf", "site": "https://aclanthology.org/2021.acl-short.103/", "pdf_size": 265309, "gs_citation": 106, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=16490328895750628081&as_sdt=80005&sciodt=0,11&hl=en", "gs_version_total": 10, "aff": "Univ. Grenoble Alpes, CNRS, LIG; Facebook AI; Facebook AI; Facebook AI; Univ. Grenoble Alpes, CNRS, LIG; Univ. Grenoble Alpes, CNRS, LIG+Naver Labs Europe", "aff_domain": "univ-grenoble-alpes.fr;fb.com;fb.com;fb.com;univ-grenoble-alpes.fr;univ-grenoble-alpes.fr", "email": "univ-grenoble-alpes.fr;fb.com;fb.com;fb.com;univ-grenoble-alpes.fr;univ-grenoble-alpes.fr", "github": "", "project": "", "author_num": 6, "aff_unique_index": "0;1;1;1;0;0+2", "aff_unique_norm": "Universit\u00e9 Grenoble Alpes;Facebook;Naver Labs", "aff_unique_dep": "Laboratoire d'Informatique de Grenoble (LIG);Facebook AI;Naver Labs Europe", "aff_unique_url": "https://www.univ-grenoble-alpes.fr;https://www.facebook.com;https://labs.naver.com", "aff_unique_abbr": "UGA;Facebook AI;NLE", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0;1;1;1;0;0+2", "aff_country_unique": "France;United States;Europe" }, { "id": "2021.acl-long.226", "title": "Lightweight Cross-Lingual Sentence Representation Learning", "track": "main", "status": "Long", "award": false, "abstract": "Large-scale models for learning fixed-dimensional cross-lingual sentence representations like LASER (Artetxe and Schwenk, 2019b) lead to significant improvement in performance on downstream tasks. However, further increases and modifications based on such large-scale models are usually impractical due to memory limitations. In this work, we introduce a lightweight dual-transformer architecture with just 2 layers for generating memory-efficient cross-lingual sentence representations. We explore different training tasks and observe that current cross-lingual training tasks leave a lot to be desired for this shallow architecture. To ameliorate this, we propose a novel cross-lingual language model, which combines the existing single-word masked language model with the newly proposed cross-lingual token-level reconstruction task. We further augment the training task by the introduction of two computationally-lite sentence-level contrastive learning tasks to enhance the alignment of cross-lingual sentence representation space, which compensates for the learning bottleneck of the lightweight transformer for generative tasks. Our comparisons with competing models on cross-lingual sentence retrieval and multilingual document classification confirm the effectiveness of the newly proposed training tasks for a shallow model.", "author": "Zhuoyuan Mao; Prakhar Gupta; Chenhui Chu; Martin Jaggi; Sadao Kurohashi", "authorids": "/z/zhuoyuan-mao/; /p/prakhar-gupta/; /c/chenhui-chu/; /m/martin-jaggi/; /s/sadao-kurohashi/", "bibtex": "@inproceedings{mao-etal-2021-lightweight,\n title = \"Lightweight Cross-Lingual Sentence Representation Learning\",\n author = \"Mao, Zhuoyuan and\n Gupta, Prakhar and\n Chu, Chenhui and\n Jaggi, Martin and\n Kurohashi, Sadao\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.226/\",\n doi = \"10.18653/v1/2021.acl-long.226\",\n pages = \"2902--2913\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.226.pdf", "site": "https://aclanthology.org/2021.acl-long.226/", "pdf_size": 856403, "gs_citation": 11, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=12284023214635260848&as_sdt=5,33&sciodt=0,33&hl=en", "gs_version_total": 9, "aff": "Kyoto University, Japan; EPFL, Switzerland; Kyoto University, Japan; EPFL, Switzerland; Kyoto University, Japan", "aff_domain": "nlp.ist.i.kyoto-u.ac.jp;epfl.ch;nlp.ist.i.kyoto-u.ac.jp;epfl.ch;nlp.ist.i.kyoto-u.ac.jp", "email": "nlp.ist.i.kyoto-u.ac.jp;epfl.ch;nlp.ist.i.kyoto-u.ac.jp;epfl.ch;nlp.ist.i.kyoto-u.ac.jp", "github": "https://github.com/Mao-KU/lightweight-crosslingual-sent2vec", "project": "", "author_num": 5, "aff_unique_index": "0;1;0;1;0", "aff_unique_norm": "Kyoto University;\u00c9cole Polytechnique F\u00e9d\u00e9rale de Lausanne", "aff_unique_dep": ";", "aff_unique_url": "https://www.kyoto-u.ac.jp;https://www.epfl.ch", "aff_unique_abbr": "Kyoto U;EPFL", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0;1;0;1;0", "aff_country_unique": "Japan;Switzerland" }, { "id": "2021.acl-long.216", "title": "Locate and Label: A Two-stage Identifier for Nested Named Entity Recognition", "track": "main", "status": "Long", "award": false, "abstract": "Named entity recognition (NER) is a well-studied task in natural language processing. Traditional NER research only deals with flat entities and ignores nested entities. The span-based methods treat entity recognition as a span classification task. Although these methods have the innate ability to handle nested NER, they suffer from high computational cost, ignorance of boundary information, under-utilization of the spans that partially match with entities, and difficulties in long entity recognition. To tackle these issues, we propose a two-stage entity identifier. First we generate span proposals by filtering and boundary regression on the seed spans to locate the entities, and then label the boundary-adjusted span proposals with the corresponding categories. Our method effectively utilizes the boundary information of entities and partially matched spans during training. Through boundary regression, entities of any length can be covered theoretically, which improves the ability to recognize long entities. In addition, many low-quality seed spans are filtered out in the first stage, which reduces the time complexity of inference. Experiments on nested NER datasets demonstrate that our proposed method outperforms previous state-of-the-art models.", "author": "Yongliang Shen; Xinyin Ma; Zeqi Tan; Shuai Zhang; Wen Wang; Weiming Lu", "authorids": "/y/yongliang-shen/; /x/xinyin-ma/; /z/zeqi-tan/; /s/shuai-zhang/; /w/wen-wang/; /w/weiming-lu/", "bibtex": "@inproceedings{shen-etal-2021-locate,\n title = \"Locate and Label: A Two-stage Identifier for Nested Named Entity Recognition\",\n author = \"Shen, Yongliang and\n Ma, Xinyin and\n Tan, Zeqi and\n Zhang, Shuai and\n Wang, Wen and\n Lu, Weiming\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.216/\",\n doi = \"10.18653/v1/2021.acl-long.216\",\n pages = \"2782--2794\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.216.pdf", "site": "https://aclanthology.org/2021.acl-long.216/", "pdf_size": 955963, "gs_citation": 207, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=7895445179011795610&as_sdt=8005&sciodt=0,7&hl=en", "gs_version_total": 4, "aff": "College of Computer Science and Technology, Zhejiang University; College of Computer Science and Technology, Zhejiang University; College of Computer Science and Technology, Zhejiang University; College of Computer Science and Technology, Zhejiang University; University of Science and Technology of China; College of Computer Science and Technology, Zhejiang University", "aff_domain": "zju.edu.cn; ; ; ; ;zju.edu.cn", "email": "zju.edu.cn; ; ; ; ;zju.edu.cn", "github": "", "project": "", "author_num": 6, "aff_unique_index": "0;0;0;0;1;0", "aff_unique_norm": "Zhejiang University;University of Science and Technology of China", "aff_unique_dep": "College of Computer Science and Technology;", "aff_unique_url": "http://www.zju.edu.cn;http://www.ustc.edu.cn", "aff_unique_abbr": "ZJU;USTC", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0;0;0;0;0;0", "aff_country_unique": "China" }, { "id": "2021.acl-long.499", "title": "Long Text Generation by Modeling Sentence-Level and Discourse-Level Coherence", "track": "main", "status": "Long", "award": false, "abstract": "Generating long and coherent text is an important but challenging task, particularly for open-ended language generation tasks such as story generation. Despite the success in modeling intra-sentence coherence, existing generation models (e.g., BART) still struggle to maintain a coherent event sequence throughout the generated text. We conjecture that this is because of the difficulty for the decoder to capture the high-level semantics and discourse structures in the context beyond token-level co-occurrence. In this paper, we propose a long text generation model, which can represent the prefix sentences at sentence level and discourse level in the decoding process. To this end, we propose two pretraining objectives to learn the representations by predicting inter-sentence semantic similarity and distinguishing between normal and shuffled sentence orders. Extensive experiments show that our model can generate more coherent texts than state-of-the-art baselines.", "author": "Jian Guan; Xiaoxi Mao; Changjie Fan; Zitao Liu; Wenbiao Ding; Minlie Huang", "authorids": "/j/jian-guan/; /x/xiaoxi-mao/; /c/changjie-fan/; /z/zitao-liu/; /w/wenbiao-ding/; /m/minlie-huang/", "bibtex": "@inproceedings{guan-etal-2021-long,\n title = \"Long Text Generation by Modeling Sentence-Level and Discourse-Level Coherence\",\n author = \"Guan, Jian and\n Mao, Xiaoxi and\n Fan, Changjie and\n Liu, Zitao and\n Ding, Wenbiao and\n Huang, Minlie\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.499/\",\n doi = \"10.18653/v1/2021.acl-long.499\",\n pages = \"6379--6393\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.499.pdf", "site": "https://aclanthology.org/2021.acl-long.499/", "pdf_size": 895485, "gs_citation": 86, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=9231984641844511253&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 7, "aff": "The CoAI group, DCST+Institute for Artificial Intelligence+State Key Lab of Intelligent Technology and Systems+Beijing National Research Center for Information Science and Technology+Tsinghua University, Beijing 100084, China; Netease Fuxi AI Lab; Netease Fuxi AI Lab; TAL Education Group; TAL Education Group; The CoAI group, DCST+Institute for Artificial Intelligence+State Key Lab of Intelligent Technology and Systems+Beijing National Research Center for Information Science and Technology+Tsinghua University, Beijing 100084, China", "aff_domain": "mails.tsinghua.edu.cn;corp.netease.com;corp.netease.com;gmail.com;100tal.com;tsinghua.edu.cn", "email": "mails.tsinghua.edu.cn;corp.netease.com;corp.netease.com;gmail.com;100tal.com;tsinghua.edu.cn", "github": "", "project": "", "author_num": 6, "aff_unique_index": "0+1+2+3+4;5;5;6;6;0+1+2+3+4", "aff_unique_norm": "DCST;Institute for Artificial Intelligence;State Key Lab of Intelligent Technology and Systems;Beijing National Research Center for Information Science and Technology;Tsinghua University;Netease;TAL Education Group", "aff_unique_dep": "The CoAI group;Artificial Intelligence;;;;Fuxi AI Lab;", "aff_unique_url": ";;;;https://www.tsinghua.edu.cn;https://www.netease.com;https://www.tal.com", "aff_unique_abbr": ";;;;THU;Netease;TAL", "aff_campus_unique_index": "1;1", "aff_campus_unique": ";Beijing", "aff_country_unique_index": "1+2+2+2;2;2;2;2;1+2+2+2", "aff_country_unique": ";United States;China" }, { "id": "2021.acl-long.470", "title": "Long-Span Summarization via Local Attention and Content Selection", "track": "main", "status": "Long", "award": false, "abstract": "Transformer-based models have achieved state-of-the-art results in a wide range of natural language processing (NLP) tasks including document summarization. Typically these systems are trained by fine-tuning a large pre-trained model to the target task. One issue with these transformer-based models is that they do not scale well in terms of memory and compute requirements as the input length grows. Thus, for long document summarization, it can be challenging to train or fine-tune these models. In this work, we exploit large pre-trained transformer-based models and address long-span dependencies in abstractive summarization using two methods: local self-attention; and explicit content selection. These approaches are compared on a range of network configurations. Experiments are carried out on standard long-span summarization tasks, including Spotify Podcast, arXiv, and PubMed datasets. We demonstrate that by combining these methods, we can achieve state-of-the-art results on all three tasks in the ROUGE scores. Moreover, without a large-scale GPU card, our approach can achieve comparable or better results than existing approaches.", "author": "Potsawee Manakul; Mark Gales", "authorids": "/p/potsawee-manakul/; /m/mark-gales/", "bibtex": "@inproceedings{manakul-gales-2021-long,\n title = \"Long-Span Summarization via Local Attention and Content Selection\",\n author = \"Manakul, Potsawee and\n Gales, Mark\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.470/\",\n doi = \"10.18653/v1/2021.acl-long.470\",\n pages = \"6026--6041\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.470.pdf", "site": "https://aclanthology.org/2021.acl-long.470/", "pdf_size": 1082806, "gs_citation": 62, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=15314427076433330649&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 6, "aff": "Department of Engineering, University of Cambridge; Department of Engineering, University of Cambridge", "aff_domain": "cam.ac.uk;eng.cam.ac.uk", "email": "cam.ac.uk;eng.cam.ac.uk", "github": "https://github.com/potsawee/longsum0", "project": "", "author_num": 2, "aff_unique_index": "0;0", "aff_unique_norm": "University of Cambridge", "aff_unique_dep": "Department of Engineering", "aff_unique_url": "https://www.cam.ac.uk", "aff_unique_abbr": "Cambridge", "aff_campus_unique_index": "0;0", "aff_campus_unique": "Cambridge", "aff_country_unique_index": "0;0", "aff_country_unique": "United Kingdom" }, { "id": "2021.acl-long.405", "title": "Lower Perplexity is Not Always Human-Like", "track": "main", "status": "Long", "award": false, "abstract": "In computational psycholinguistics, various language models have been evaluated against human reading behavior (e.g., eye movement) to build human-like computational models. However, most previous efforts have focused almost exclusively on English, despite the recent trend towards linguistic universal within the general community. In order to fill the gap, this paper investigates whether the established results in computational psycholinguistics can be generalized across languages. Specifically, we re-examine an established generalization \u2014the lower perplexity a language model has, the more human-like the language model is\u2014 in Japanese with typologically different structures from English. Our experiments demonstrate that this established generalization exhibits a surprising lack of universality; namely, lower perplexity is not always human-like. Moreover, this discrepancy between English and Japanese is further explored from the perspective of (non-)uniform information density. Overall, our results suggest that a cross-lingual evaluation will be necessary to construct human-like computational models.", "author": "Tatsuki Kuribayashi; Yohei Oseki; Takumi Ito; Ryo Yoshida; Masayuki Asahara; Kentaro Inui", "authorids": "/t/tatsuki-kuribayashi/; /y/yohei-oseki/; /t/takumi-ito/; /r/ryo-yoshida/; /m/masayuki-asahara/; /k/kentaro-inui/", "bibtex": "@inproceedings{kuribayashi-etal-2021-lower,\n title = \"Lower Perplexity is Not Always Human-Like\",\n author = \"Kuribayashi, Tatsuki and\n Oseki, Yohei and\n Ito, Takumi and\n Yoshida, Ryo and\n Asahara, Masayuki and\n Inui, Kentaro\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.405/\",\n doi = \"10.18653/v1/2021.acl-long.405\",\n pages = \"5203--5217\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.405.pdf", "site": "https://aclanthology.org/2021.acl-long.405/", "pdf_size": 681970, "gs_citation": 76, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=9681463329270177984&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 10, "aff": "Tohoku University+Langsmith Inc.; University of Tokyo+RIKEN; Tohoku University+Langsmith Inc.; University of Tokyo; NINJAL; Tohoku University+RIKEN", "aff_domain": "tohoku.ac.jp;tohoku.ac.jp;tohoku.ac.jp;g.ecc.u-tokyo.ac.jp;g.ecc.u-tokyo.ac.jp;ninjal.ac.jp", "email": "tohoku.ac.jp;tohoku.ac.jp;tohoku.ac.jp;g.ecc.u-tokyo.ac.jp;g.ecc.u-tokyo.ac.jp;ninjal.ac.jp", "github": "", "project": "", "author_num": 6, "aff_unique_index": "0+1;2+3;0+1;2;4;0+3", "aff_unique_norm": "Tohoku University;Langsmith Inc.;University of Tokyo;RIKEN;National Institute for Japanese Language and Linguistics", "aff_unique_dep": ";;;;", "aff_unique_url": "https://www.tohoku.ac.jp;;https://www.u-tokyo.ac.jp;https://www.riken.jp;https://www.ninjal.ac.jp", "aff_unique_abbr": "Tohoku U;;UTokyo;RIKEN;NINJAL", "aff_campus_unique_index": ";;;", "aff_campus_unique": "", "aff_country_unique_index": "0+1;0+0;0+1;0;0;0+0", "aff_country_unique": "Japan;United States" }, { "id": "2021.acl-long.86", "title": "MATE-KD: Masked Adversarial TExt, a Companion to Knowledge Distillation", "track": "main", "status": "Long", "award": false, "abstract": "The advent of large pre-trained language models has given rise to rapid progress in the field of Natural Language Processing (NLP). While the performance of these models on standard benchmarks has scaled with size, compression techniques such as knowledge distillation have been key in making them practical. We present MATE-KD, a novel text-based adversarial training algorithm which improves the performance of knowledge distillation. MATE-KD first trains a masked language model-based generator to perturb text by maximizing the divergence between teacher and student logits. Then using knowledge distillation a student is trained on both the original and the perturbed training samples. We evaluate our algorithm, using BERT-based models, on the GLUE benchmark and demonstrate that MATE-KD outperforms competitive adversarial learning and data augmentation baselines. On the GLUE test set our 6 layer RoBERTa based model outperforms BERT-large.", "author": "Ahmad Rashid; Vasileios Lioutas; Mehdi Rezagholizadeh", "authorids": "/a/ahmad-rashid/; /v/vasileios-lioutas/; /m/mehdi-rezagholizadeh/", "bibtex": "@inproceedings{rashid-etal-2021-mate,\n title = \"{MATE}-{KD}: Masked Adversarial {TE}xt, a Companion to Knowledge Distillation\",\n author = \"Rashid, Ahmad and\n Lioutas, Vasileios and\n Rezagholizadeh, Mehdi\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.86/\",\n doi = \"10.18653/v1/2021.acl-long.86\",\n pages = \"1062--1071\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.86.pdf", "site": "https://aclanthology.org/2021.acl-long.86/", "pdf_size": 392119, "gs_citation": 36, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=5551478638718798908&as_sdt=10005&sciodt=0,8&hl=en", "gs_version_total": 6, "aff": "Huawei Noah\u2019s Ark Lab; University of British Columbia + Huawei Noah\u2019s Ark Lab; Huawei Noah\u2019s Ark Lab", "aff_domain": "huawei.com;vlioutas.com;huawei.com", "email": "huawei.com;vlioutas.com;huawei.com", "github": "", "project": "", "author_num": 3, "aff_unique_index": "0;1+0;0", "aff_unique_norm": "Huawei;University of British Columbia", "aff_unique_dep": "Noah\u2019s Ark Lab;", "aff_unique_url": "https://www.huawei.com;https://www.ubc.ca", "aff_unique_abbr": "Huawei;UBC", "aff_campus_unique_index": "1", "aff_campus_unique": ";Vancouver", "aff_country_unique_index": "0;1+0;0", "aff_country_unique": "China;Canada" }, { "id": "2021.acl-long.121", "title": "MECT: Multi-Metadata Embedding based Cross-Transformer for Chinese Named Entity Recognition", "track": "main", "status": "Long", "award": false, "abstract": "Recently, word enhancement has become very popular for Chinese Named Entity Recognition (NER), reducing segmentation errors and increasing the semantic and boundary information of Chinese words. However, these methods tend to ignore the information of the Chinese character structure after integrating the lexical information. Chinese characters have evolved from pictographs since ancient times, and their structure often reflects more information about the characters. This paper presents a novel Multi-metadata Embedding based Cross-Transformer (MECT) to improve the performance of Chinese NER by fusing the structural information of Chinese characters. Specifically, we use multi-metadata embedding in a two-stream Transformer to integrate Chinese character features with the radical-level embedding. With the structural characteristics of Chinese characters, MECT can better capture the semantic information of Chinese characters for NER. The experimental results obtained on several well-known benchmarking datasets demonstrate the merits and superiority of the proposed MECT method.", "author": "Shuang Wu; Xiaoning Song; Zhenhua Feng", "authorids": "/s/shuang-wu/; /x/xiaoning-song/; /z/zhenhua-feng/", "bibtex": "@inproceedings{wu-etal-2021-mect,\n title = \"{MECT}: {M}ulti-Metadata Embedding based Cross-Transformer for {C}hinese Named Entity Recognition\",\n author = \"Wu, Shuang and\n Song, Xiaoning and\n Feng, Zhenhua\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.121/\",\n doi = \"10.18653/v1/2021.acl-long.121\",\n pages = \"1529--1539\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.121.pdf", "site": "https://aclanthology.org/2021.acl-long.121/", "pdf_size": 825401, "gs_citation": 175, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=2439597961682412395&as_sdt=5,24&sciodt=0,24&hl=en", "gs_version_total": 8, "aff": "School of Artificial Intelligence and Computer Science, Jiangnan University, China; School of Artificial Intelligence and Computer Science, Jiangnan University, China; Department of Computer Science, University of Surrey, UK+Centre for Vision, Speech and Signal Processing, University of Surrey, UK", "aff_domain": "stu.jiangnan.edu.cn;jiangnan.edu.cn;surrey.ac.uk", "email": "stu.jiangnan.edu.cn;jiangnan.edu.cn;surrey.ac.uk", "github": "https://github.com/CoderMusou/MECT4CNER", "project": "", "author_num": 3, "aff_unique_index": "0;0;1+1", "aff_unique_norm": "Jiangnan University;University of Surrey", "aff_unique_dep": "School of Artificial Intelligence and Computer Science;Department of Computer Science", "aff_unique_url": "https://www.jiangnan.edu.cn/;https://www.surrey.ac.uk", "aff_unique_abbr": "JNU;Surrey", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0;0;1+1", "aff_country_unique": "China;United Kingdom" }, { "id": "2021.acl-long.373", "title": "MLBiNet: A Cross-Sentence Collective Event Detection Network", "track": "main", "status": "Long", "award": false, "abstract": "We consider the problem of collectively detecting multiple events, particularly in cross-sentence settings. The key to dealing with the problem is to encode semantic information and model event inter-dependency at a document-level. In this paper, we reformulate it as a Seq2Seq task and propose a Multi-Layer Bidirectional Network (MLBiNet) to capture the document-level association of events and semantic information simultaneously. Specifically, a bidirectional decoder is firstly devised to model event inter-dependency within a sentence when decoding the event tag vector sequence. Secondly, an information aggregation module is employed to aggregate sentence-level semantic and event tag information. Finally, we stack multiple bidirectional decoders and feed cross-sentence information, forming a multi-layer bidirectional tagging architecture to iteratively propagate information across sentences. We show that our approach provides significant improvement in performance compared to the current state-of-the-art results.", "author": "Dongfang Lou; Zhilin Liao; Shumin Deng; Ningyu Zhang; Huajun Chen", "authorids": "/d/dongfang-lou/; /z/zhilin-liao/; /s/shumin-deng/; /n/ningyu-zhang/; /h/huajun-chen/", "bibtex": "@inproceedings{lou-etal-2021-mlbinet,\n title = \"{MLB}i{N}et: A Cross-Sentence Collective Event Detection Network\",\n author = \"Lou, Dongfang and\n Liao, Zhilin and\n Deng, Shumin and\n Zhang, Ningyu and\n Chen, Huajun\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.373/\",\n doi = \"10.18653/v1/2021.acl-long.373\",\n pages = \"4829--4839\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.373.pdf", "site": "https://aclanthology.org/2021.acl-long.373/", "pdf_size": 409818, "gs_citation": 71, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=1021408025311977094&as_sdt=5,31&sciodt=0,31&hl=en", "gs_version_total": 5, "aff": "Zhejiang University & AZFT Joint Lab for Knowledge Engine+Hangzhou Innovation Center, Zhejiang University; Zhejiang University & AZFT Joint Lab for Knowledge Engine+Hangzhou Innovation Center, Zhejiang University; Zhejiang University & AZFT Joint Lab for Knowledge Engine+Hangzhou Innovation Center, Zhejiang University; Zhejiang University & AZFT Joint Lab for Knowledge Engine+Hangzhou Innovation Center, Zhejiang University; Zhejiang University & AZFT Joint Lab for Knowledge Engine+Hangzhou Innovation Center, Zhejiang University", "aff_domain": "163.com;yeah.net;zju.edu.cn;zju.edu.cn;zju.edu.cn", "email": "163.com;yeah.net;zju.edu.cn;zju.edu.cn;zju.edu.cn", "github": "https://github.com/zjunlp/DocED", "project": "", "author_num": 5, "aff_unique_index": "0+0;0+0;0+0;0+0;0+0", "aff_unique_norm": "Zhejiang University", "aff_unique_dep": "Joint Lab for Knowledge Engine", "aff_unique_url": "http://www.zju.edu.cn", "aff_unique_abbr": "ZJU", "aff_campus_unique_index": "1;1;1;1;1", "aff_campus_unique": ";Hangzhou", "aff_country_unique_index": "0+0;0+0;0+0;0+0;0+0", "aff_country_unique": "China" }, { "id": "2021.acl-long.440", "title": "MMGCN: Multimodal Fusion via Deep Graph Convolution Network for Emotion Recognition in Conversation", "track": "main", "status": "Long", "award": false, "abstract": "Emotion recognition in conversation (ERC) is a crucial component in affective dialogue systems, which helps the system understand users\u2019 emotions and generate empathetic responses. However, most works focus on modeling speaker and contextual information primarily on the textual modality or simply leveraging multimodal information through feature concatenation. In order to explore a more effective way of utilizing both multimodal and long-distance contextual information, we propose a new model based on multimodal fused graph convolutional network, MMGCN, in this work. MMGCN can not only make use of multimodal dependencies effectively, but also leverage speaker information to model inter-speaker and intra-speaker dependency. We evaluate our proposed model on two public benchmark datasets, IEMOCAP and MELD, and the results prove the effectiveness of MMGCN, which outperforms other SOTA methods by a significant margin under the multimodal conversation setting.", "author": "Jingwen Hu; Yuchen Liu; Jinming Zhao; Qin Jin", "authorids": "/j/jingwen-hu/; /y/yuchen-liu/; /j/jinming-zhao/; /q/qin-jin/", "bibtex": "@inproceedings{hu-etal-2021-mmgcn,\n title = \"{MMGCN}: Multimodal Fusion via Deep Graph Convolution Network for Emotion Recognition in Conversation\",\n author = \"Hu, Jingwen and\n Liu, Yuchen and\n Zhao, Jinming and\n Jin, Qin\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.440/\",\n doi = \"10.18653/v1/2021.acl-long.440\",\n pages = \"5666--5675\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.440.pdf", "site": "https://aclanthology.org/2021.acl-long.440/", "pdf_size": 854422, "gs_citation": 258, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=4401583637715431201&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 4, "aff": "School of Information, Renmin University of China; School of Information, Renmin University of China; School of Information, Renmin University of China; School of Information, Renmin University of China", "aff_domain": "ruc.edu.cn;ruc.edu.cn;ruc.edu.cn;ruc.edu.cn", "email": "ruc.edu.cn;ruc.edu.cn;ruc.edu.cn;ruc.edu.cn", "github": "", "project": "", "author_num": 4, "aff_unique_index": "0;0;0;0", "aff_unique_norm": "Renmin University of China", "aff_unique_dep": "School of Information", "aff_unique_url": "http://www.ruc.edu.cn", "aff_unique_abbr": "RUC", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0;0;0;0", "aff_country_unique": "China" }, { "id": "2021.acl-short.37", "title": "MOLEMAN: Mention-Only Linking of Entities with a Mention Annotation Network", "track": "main", "status": "Short", "award": false, "abstract": "We present an instance-based nearest neighbor approach to entity linking. In contrast to most prior entity retrieval systems which represent each entity with a single vector, we build a contextualized mention-encoder that learns to place similar mentions of the same entity closer in vector space than mentions of different entities. This approach allows all mentions of an entity to serve as \u201cclass prototypes\u201d as inference involves retrieving from the full set of labeled entity mentions in the training set and applying the nearest mention neighbor\u2019s entity label. Our model is trained on a large multilingual corpus of mention pairs derived from Wikipedia hyperlinks, and performs nearest neighbor inference on an index of 700 million mentions. It is simpler to train, gives more interpretable predictions, and outperforms all other systems on two multilingual entity linking benchmarks.", "author": "Nicholas FitzGerald; Dan Bikel; Jan Botha; Daniel Gillick; Tom Kwiatkowski; Andrew McCallum", "authorids": "/n/nicholas-fitzgerald/; /d/daniel-m-bikel/; /j/jan-botha/; /d/dan-gillick/; /t/tom-kwiatkowski/; /a/andrew-mccallum/", "bibtex": "@inproceedings{fitzgerald-etal-2021-moleman,\n title = \"{MOLEMAN}: Mention-Only Linking of Entities with a Mention Annotation Network\",\n author = \"FitzGerald, Nicholas and\n Bikel, Dan and\n Botha, Jan and\n Gillick, Daniel and\n Kwiatkowski, Tom and\n McCallum, Andrew\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-short.37/\",\n doi = \"10.18653/v1/2021.acl-short.37\",\n pages = \"278--285\"\n}", "pdf": "https://aclanthology.org/2021.acl-short.37.pdf", "site": "https://aclanthology.org/2021.acl-short.37/", "pdf_size": 619869, "gs_citation": 12, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=2892823995595969329&as_sdt=5,33&sciodt=0,33&hl=en", "gs_version_total": 5, "aff": "Google Research; Google Research; Google Research; Google Research; Google Research; Google Research", "aff_domain": "google.com;google.com;google.com;google.com;google.com;google.com", "email": "google.com;google.com;google.com;google.com;google.com;google.com", "github": "", "project": "", "author_num": 6, "aff_unique_index": "0;0;0;0;0;0", "aff_unique_norm": "Google", "aff_unique_dep": "Google Research", "aff_unique_url": "https://research.google", "aff_unique_abbr": "Google Research", "aff_campus_unique_index": "0;0;0;0;0;0", "aff_campus_unique": "Mountain View", "aff_country_unique_index": "0;0;0;0;0;0", "aff_country_unique": "United States" }, { "id": "2021.acl-long.285", "title": "MPC-BERT: A Pre-Trained Language Model for Multi-Party Conversation Understanding", "track": "main", "status": "Long", "award": false, "abstract": "Recently, various neural models for multi-party conversation (MPC) have achieved impressive improvements on a variety of tasks such as addressee recognition, speaker identification and response prediction. However, these existing methods on MPC usually represent interlocutors and utterances individually and ignore the inherent complicated structure in MPC which may provide crucial interlocutor and utterance semantics and would enhance the conversation understanding process. To this end, we present MPC-BERT, a pre-trained model for MPC understanding that considers learning who says what to whom in a unified model with several elaborated self-supervised tasks. Particularly, these tasks can be generally categorized into (1) interlocutor structure modeling including reply-to utterance recognition, identical speaker searching and pointer consistency distinction, and (2) utterance semantics modeling including masked shared utterance restoration and shared node detection. We evaluate MPC-BERT on three downstream tasks including addressee recognition, speaker identification and response selection. Experimental results show that MPC-BERT outperforms previous methods by large margins and achieves new state-of-the-art performance on all three downstream tasks at two benchmarks.", "author": "Jia-Chen Gu; Chongyang Tao; Zhenhua Ling; Can Xu; Xiubo Geng; Daxin Jiang", "authorids": "/j/jia-chen-gu/; /c/chongyang-tao/; /z/zhenhua-ling/; /c/can-xu/; /x/xiubo-geng/; /d/daxin-jiang/", "bibtex": "@inproceedings{gu-etal-2021-mpc,\n title = \"{MPC}-{BERT}: A Pre-Trained Language Model for Multi-Party Conversation Understanding\",\n author = \"Gu, Jia-Chen and\n Tao, Chongyang and\n Ling, Zhenhua and\n Xu, Can and\n Geng, Xiubo and\n Jiang, Daxin\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.285/\",\n doi = \"10.18653/v1/2021.acl-long.285\",\n pages = \"3682--3692\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.285.pdf", "site": "https://aclanthology.org/2021.acl-long.285/", "pdf_size": 1241015, "gs_citation": 58, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=9751558308697350610&as_sdt=4000005&sciodt=0,18&hl=en", "gs_version_total": 5, "aff": "1National Engineering Laboratory for Speech and Language Information Processing, University of Science and Technology of China, Hefei, China; 2Microsoft, Beijing, China; 1National Engineering Laboratory for Speech and Language Information Processing, University of Science and Technology of China, Hefei, China; 2Microsoft, Beijing, China; 2Microsoft, Beijing, China; 2Microsoft, Beijing, China", "aff_domain": "mail.ustc.edu.cn;microsoft.com;ustc.edu.cn;microsoft.com;microsoft.com;microsoft.com", "email": "mail.ustc.edu.cn;microsoft.com;ustc.edu.cn;microsoft.com;microsoft.com;microsoft.com", "github": "", "project": "", "author_num": 6, "aff_unique_index": "0;1;0;1;1;1", "aff_unique_norm": "University of Science and Technology of China;Microsoft", "aff_unique_dep": "National Engineering Laboratory for Speech and Language Information Processing;", "aff_unique_url": "http://www.ustc.edu.cn;https://www.microsoft.com", "aff_unique_abbr": "USTC;MSFT", "aff_campus_unique_index": "0;1;0;1;1;1", "aff_campus_unique": "Hefei;Beijing", "aff_country_unique_index": "0;0;0;0;0;0", "aff_country_unique": "China" }, { "id": "2021.acl-short.16", "title": "Machine Translation into Low-resource Language Varieties", "track": "main", "status": "Short", "award": false, "abstract": "State-of-the-art machine translation (MT) systems are typically trained to generate \u201cstandard\u201d target language; however, many languages have multiple varieties (regional varieties, dialects, sociolects, non-native varieties) that are different from the standard language. Such varieties are often low-resource, and hence do not benefit from contemporary NLP solutions, MT included. We propose a general framework to rapidly adapt MT systems to generate language varieties that are close to, but different from, the standard target language, using no parallel (source\u2013variety) data. This also includes adaptation of MT systems to low-resource typologically-related target languages. We experiment with adapting an English\u2013Russian MT system to generate Ukrainian and Belarusian, an English\u2013Norwegian Bokm\u00e5l system to generate Nynorsk, and an English\u2013Arabic system to generate four Arabic dialects, obtaining significant improvements over competitive baselines.", "author": "Sachin Kumar; Antonios Anastasopoulos; Shuly Wintner; Yulia Tsvetkov", "authorids": "/s/sachin-kumar/; /a/antonios-anastasopoulos/; /s/shuly-wintner/; /y/yulia-tsvetkov/", "bibtex": "@inproceedings{kumar-etal-2021-machine,\n title = \"Machine Translation into Low-resource Language Varieties\",\n author = \"Kumar, Sachin and\n Anastasopoulos, Antonios and\n Wintner, Shuly and\n Tsvetkov, Yulia\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-short.16/\",\n doi = \"10.18653/v1/2021.acl-short.16\",\n pages = \"110--121\"\n}", "pdf": "https://aclanthology.org/2021.acl-short.16.pdf", "site": "https://aclanthology.org/2021.acl-short.16/", "pdf_size": 478689, "gs_citation": 39, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=313341353283418246&as_sdt=5,31&sciodt=0,31&hl=en", "gs_version_total": 5, "aff": "Language Technologies Institute, Carnegie Mellon University, Pittsburgh, PA, USA; Department of Computer Science, George Mason University, Fairfax, VA, USA; Department of Computer Science, University of Haifa, Haifa, Israel; Paul G. Allen School of Computer Science & Engineering, University of Washington", "aff_domain": "cs.cmu.edu;gmu.edu;cs.haifa.ac.il;cs.washington.edu", "email": "cs.cmu.edu;gmu.edu;cs.haifa.ac.il;cs.washington.edu", "github": "https://github.com/Sachin19/seq2seq-con", "project": "", "author_num": 4, "aff_unique_index": "0;1;2;3", "aff_unique_norm": "Carnegie Mellon University;George Mason University;University of Haifa;University of Washington", "aff_unique_dep": "Language Technologies Institute;Department of Computer Science;Department of Computer Science;Paul G. Allen School of Computer Science & Engineering", "aff_unique_url": "https://www.cmu.edu;https://www.gmu.edu;https://www.haifa.ac.il;https://www.washington.edu", "aff_unique_abbr": "CMU;GMU;UoH;UW", "aff_campus_unique_index": "0;1;2;3", "aff_campus_unique": "Pittsburgh;Fairfax;Haifa;Seattle", "aff_country_unique_index": "0;0;1;0", "aff_country_unique": "United States;Israel" }, { "id": "2021.acl-long.295", "title": "Making Pre-trained Language Models Better Few-shot Learners", "track": "main", "status": "Long", "award": false, "abstract": "The recent GPT-3 model (Brown et al., 2020) achieves remarkable few-shot performance solely by leveraging a natural-language prompt and a few task demonstrations as input context. Inspired by their findings, we study few-shot learning in a more practical scenario, where we use smaller language models for which fine-tuning is computationally efficient. We present LM-BFF\u2014better few-shot fine-tuning of language models\u2014a suite of simple and complementary techniques for fine-tuning language models on a small number of annotated examples. Our approach includes (1) prompt-based fine-tuning together with a novel pipeline for automating prompt generation; and (2) a refined strategy for dynamically and selectively incorporating demonstrations into each context. Finally, we present a systematic evaluation for analyzing few-shot performance on a range of NLP tasks, including classification and regression. Our experiments demonstrate that our methods combine to dramatically outperform standard fine-tuning procedures in this low resource setting, achieving up to 30% absolute improvement, and 11% on average across all tasks. Our approach makes minimal assumptions on task resources and domain expertise, and hence constitutes a strong task-agnostic method for few-shot learning.", "author": "Tianyu Gao; Adam Fisch; Danqi Chen", "authorids": "/t/tianyu-gao/; /a/adam-fisch/; /d/danqi-chen/", "bibtex": "@inproceedings{gao-etal-2021-making,\n title = \"Making Pre-trained Language Models Better Few-shot Learners\",\n author = \"Gao, Tianyu and\n Fisch, Adam and\n Chen, Danqi\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.295/\",\n doi = \"10.18653/v1/2021.acl-long.295\",\n pages = \"3816--3830\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.295.pdf", "site": "https://aclanthology.org/2021.acl-long.295/", "pdf_size": 1333647, "gs_citation": 2071, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=3108520483875751978&as_sdt=5,33&sciodt=0,33&hl=en", "gs_version_total": 9, "aff": "Princeton University; Massachusetts Institute of Technology; Princeton University", "aff_domain": "cs.princeton.edu;csail.mit.edu;cs.princeton.edu", "email": "cs.princeton.edu;csail.mit.edu;cs.princeton.edu", "github": "https://github.com/princeton-nlp/LM-BFF", "project": "", "author_num": 3, "aff_unique_index": "0;1;0", "aff_unique_norm": "Princeton University;Massachusetts Institute of Technology", "aff_unique_dep": ";", "aff_unique_url": "https://www.princeton.edu;https://web.mit.edu", "aff_unique_abbr": "Princeton;MIT", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0;0;0", "aff_country_unique": "United States" }, { "id": "2021.acl-long.228", "title": "Marginal Utility Diminishes: Exploring the Minimum Knowledge for BERT Knowledge Distillation", "track": "main", "status": "Long", "award": false, "abstract": "Recently, knowledge distillation (KD) has shown great success in BERT compression. Instead of only learning from the teacher\u2019s soft label as in conventional KD, researchers find that the rich information contained in the hidden layers of BERT is conducive to the student\u2019s performance. To better exploit the hidden knowledge, a common practice is to force the student to deeply mimic the teacher\u2019s hidden states of all the tokens in a layer-wise manner. In this paper, however, we observe that although distilling the teacher\u2019s hidden state knowledge (HSK) is helpful, the performance gain (marginal utility) diminishes quickly as more HSK is distilled. To understand this effect, we conduct a series of analysis. Specifically, we divide the HSK of BERT into three dimensions, namely depth, length and width. We first investigate a variety of strategies to extract crucial knowledge for each single dimension and then jointly compress the three dimensions. In this way, we show that 1) the student\u2019s performance can be improved by extracting and distilling the crucial HSK, and 2) using a tiny fraction of HSK can achieve the same performance as extensive HSK distillation. Based on the second finding, we further propose an efficient KD paradigm to compress BERT, which does not require loading the teacher during the training of student. For two kinds of student models and computing devices, the proposed KD paradigm gives rise to training speedup of 2.7x 3.4x.", "author": "Yuanxin Liu; Fandong Meng; Zheng Lin; Weiping Wang; Jie Zhou", "authorids": "/y/yuanxin-liu/; /f/fandong-meng/; /z/zheng-lin/; /w/weiping-wang/; /j/jie-zhou/", "bibtex": "@inproceedings{liu-etal-2021-marginal,\n title = \"Marginal Utility Diminishes: Exploring the Minimum Knowledge for {BERT} Knowledge Distillation\",\n author = \"Liu, Yuanxin and\n Meng, Fandong and\n Lin, Zheng and\n Wang, Weiping and\n Zhou, Jie\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.228/\",\n doi = \"10.18653/v1/2021.acl-long.228\",\n pages = \"2928--2941\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.228.pdf", "site": "https://aclanthology.org/2021.acl-long.228/", "pdf_size": 971194, "gs_citation": 5, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=1922667977439296686&as_sdt=400005&sciodt=0,14&hl=en", "gs_version_total": 7, "aff": "Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China+School of Cyber Security, University of Chinese Academy of Sciences, Beijing, China; Pattern Recognition Center, WeChat AI, Tencent Inc, China; Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China; Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China; Pattern Recognition Center, WeChat AI, Tencent Inc, China", "aff_domain": "iie.ac.cn;iie.ac.cn;iie.ac.cn;tencent.com;tencent.com", "email": "iie.ac.cn;iie.ac.cn;iie.ac.cn;tencent.com;tencent.com", "github": "", "project": "", "author_num": 5, "aff_unique_index": "0+1;2;0;0;2", "aff_unique_norm": "Chinese Academy of Sciences;University of Chinese Academy of Sciences;Tencent Inc", "aff_unique_dep": "Institute of Information Engineering;School of Cyber Security;Pattern Recognition Center, WeChat AI", "aff_unique_url": "http://www.cas.cn;http://www.ucas.ac.cn;https://www.tencent.com", "aff_unique_abbr": "CAS;UCAS;Tencent", "aff_campus_unique_index": "0+0;0;0", "aff_campus_unique": "Beijing;", "aff_country_unique_index": "0+0;0;0;0;0", "aff_country_unique": "China" }, { "id": "2021.acl-long.435", "title": "Maria: A Visual Experience Powered Conversational Agent", "track": "main", "status": "Long", "award": false, "abstract": "Arguably, the visual perception of conversational agents to the physical world is a key way for them to exhibit the human-like intelligence. Image-grounded conversation is thus proposed to address this challenge. Existing works focus on exploring the multimodal dialog models that ground the conversation on a given image. In this paper, we take a step further to study image-grounded conversation under a fully open-ended setting where no paired dialog and image are assumed available. Specifically, we present Maria, a neural conversation agent powered by the visual world experiences which are retrieved from a large-scale image index. Maria consists of three flexible components, i.e., text-to-image retriever, visual concept detector and visual-knowledge-grounded response generator. The retriever aims to retrieve a correlated image to the dialog from an image index, while the visual concept detector extracts rich visual knowledge from the image. Then, the response generator is grounded on the extracted visual knowledge and dialog context to generate the target response. Extensive experiments demonstrate Maria outperforms previous state-of-the-art methods on automatic metrics and human evaluation, and can generate informative responses that have some visual commonsense of the physical world.", "author": "Zujie Liang; Huang Hu; Can Xu; Chongyang Tao; Xiubo Geng; Yining Chen; Fan Liang; Daxin Jiang", "authorids": "/z/zujie-liang/; /h/huang-hu/; /c/can-xu/; /c/chongyang-tao/; /x/xiubo-geng/; /y/yining-chen/; /f/fan-liang/; /d/daxin-jiang/", "bibtex": "@inproceedings{liang-etal-2021-maria,\n title = \"Maria: A Visual Experience Powered Conversational Agent\",\n author = \"Liang, Zujie and\n Hu, Huang and\n Xu, Can and\n Tao, Chongyang and\n Geng, Xiubo and\n Chen, Yining and\n Liang, Fan and\n Jiang, Daxin\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.435/\",\n doi = \"10.18653/v1/2021.acl-long.435\",\n pages = \"5596--5611\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.435.pdf", "site": "https://aclanthology.org/2021.acl-long.435/", "pdf_size": 3068128, "gs_citation": 34, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=13362411978520972991&as_sdt=5,33&sciodt=0,33&hl=en", "gs_version_total": 8, "aff": "School of Electronics and Information Technology, Sun Yat-sen University, Guangzhou, China+Microsoft STCA NLP Group, Beijing, China; Microsoft STCA NLP Group, Beijing, China; Microsoft STCA NLP Group, Beijing, China; Microsoft STCA NLP Group, Beijing, China; Microsoft STCA NLP Group, Beijing, China; Microsoft STCA NLP Group, Beijing, China; School of Electronics and Information Technology, Sun Yat-sen University, Guangzhou, China; Microsoft STCA NLP Group, Beijing, China", "aff_domain": "mail2.sysu.edu.cn;microsoft.com;microsoft.com;microsoft.com;microsoft.com;microsoft.com;mail.sysu.edu.cn;microsoft.com", "email": "mail2.sysu.edu.cn;microsoft.com;microsoft.com;microsoft.com;microsoft.com;microsoft.com;mail.sysu.edu.cn;microsoft.com", "github": "", "project": "", "author_num": 8, "aff_unique_index": "0+1;1;1;1;1;1;0;1", "aff_unique_norm": "Sun Yat-sen University;Microsoft", "aff_unique_dep": "School of Electronics and Information Technology;STCA NLP Group", "aff_unique_url": "http://www.sysu.edu.cn;https://www.microsoft.com", "aff_unique_abbr": "SYSU;Microsoft", "aff_campus_unique_index": "0+1;1;1;1;1;1;0;1", "aff_campus_unique": "Guangzhou;Beijing", "aff_country_unique_index": "0+0;0;0;0;0;0;0;0", "aff_country_unique": "China" }, { "id": "2021.acl-long.369", "title": "Mask-Align: Self-Supervised Neural Word Alignment", "track": "main", "status": "Long", "award": false, "abstract": "Word alignment, which aims to align translationally equivalent words between source and target sentences, plays an important role in many natural language processing tasks. Current unsupervised neural alignment methods focus on inducing alignments from neural machine translation models, which does not leverage the full context in the target sequence. In this paper, we propose Mask-Align, a self-supervised word alignment model that takes advantage of the full context on the target side. Our model masks out each target token and predicts it conditioned on both source and the remaining target tokens. This two-step process is based on the assumption that the source token contributing most to recovering the masked target token should be aligned. We also introduce an attention variant called leaky attention, which alleviates the problem of unexpected high cross-attention weights on special tokens such as periods. Experiments on four language pairs show that our model outperforms previous unsupervised neural aligners and obtains new state-of-the-art results.", "author": "Chi Chen; Maosong Sun; Yang Liu", "authorids": "/c/chi-chen/; /m/maosong-sun/; /y/yang-liu-ict/", "bibtex": "@inproceedings{chen-etal-2021-mask,\n title = \"Mask-Align: Self-Supervised Neural Word Alignment\",\n author = \"Chen, Chi and\n Sun, Maosong and\n Liu, Yang\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.369/\",\n doi = \"10.18653/v1/2021.acl-long.369\",\n pages = \"4781--4791\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.369.pdf", "site": "https://aclanthology.org/2021.acl-long.369/", "pdf_size": 648389, "gs_citation": 32, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=6686962059075039798&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 7, "aff": "Department of Computer Science and Technology, Tsinghua University, Beijing, China+Institute for AI Industry Research, Tsinghua University, Beijing, China+Institute for Artificial Intelligence, Tsinghua University, Beijing, China+Beijing National Research Center for Information Science and Technology+Beijing Academy of Artificial Intelligence; Department of Computer Science and Technology, Tsinghua University, Beijing, China+Institute for AI Industry Research, Tsinghua University, Beijing, China+Institute for Artificial Intelligence, Tsinghua University, Beijing, China+Beijing National Research Center for Information Science and Technology+Beijing Academy of Artificial Intelligence; Department of Computer Science and Technology, Tsinghua University, Beijing, China+Institute for AI Industry Research, Tsinghua University, Beijing, China+Institute for Artificial Intelligence, Tsinghua University, Beijing, China+Beijing National Research Center for Information Science and Technology+Beijing Academy of Artificial Intelligence", "aff_domain": ";;", "email": ";;", "github": "https://github.com/THUNLP-MT/Mask-Align", "project": "", "author_num": 3, "aff_unique_index": "0+0+0+1+2;0+0+0+1+2;0+0+0+1+2", "aff_unique_norm": "Tsinghua University;Beijing National Research Center for Information Science and Technology;Beijing Academy of Artificial Intelligence", "aff_unique_dep": "Department of Computer Science and Technology;;", "aff_unique_url": "https://www.tsinghua.edu.cn;;https://www.baaic.cn", "aff_unique_abbr": "THU;;BAAI", "aff_campus_unique_index": "0+0+0;0+0+0;0+0+0", "aff_campus_unique": "Beijing;", "aff_country_unique_index": "0+0+0+0+0;0+0+0+0+0;0+0+0+0+0", "aff_country_unique": "China" }, { "id": "2021.acl-long.421", "title": "Matching Distributions between Model and Data: Cross-domain Knowledge Distillation for Unsupervised Domain Adaptation", "track": "main", "status": "Long", "award": false, "abstract": "Unsupervised Domain Adaptation (UDA) aims to transfer the knowledge of source domain to the unlabeled target domain. Existing methods typically require to learn to adapt the target model by exploiting the source data and sharing the network architecture across domains. However, this pipeline makes the source data risky and is inflexible for deploying the target model. This paper tackles a novel setting where only a trained source model is available and different network architectures can be adapted for target domain in terms of deployment environments. We propose a generic framework named Cross-domain Knowledge Distillation (CdKD) without needing any source data. CdKD matches the joint distributions between a trained source model and a set of target data during distilling the knowledge from the source model to the target domain. As a type of important knowledge in the source domain, for the first time, the gradient information is exploited to boost the transfer performance. Experiments on cross-domain text classification demonstrate that CdKD achieves superior performance, which verifies the effectiveness in this novel setting.", "author": "Bo Zhang; Xiaoming Zhang; Yun Liu; Lei Cheng; Zhoujun Li", "authorids": "/b/bo-zhang/; /x/xiaoming-zhang/; /y/yun-liu/; /l/lei-cheng/; /z/zhoujun-li/", "bibtex": "@inproceedings{zhang-etal-2021-matching,\n title = \"Matching Distributions between Model and Data: Cross-domain Knowledge Distillation for Unsupervised Domain Adaptation\",\n author = \"Zhang, Bo and\n Zhang, Xiaoming and\n Liu, Yun and\n Cheng, Lei and\n Li, Zhoujun\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.421/\",\n doi = \"10.18653/v1/2021.acl-long.421\",\n pages = \"5423--5433\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.421.pdf", "site": "https://aclanthology.org/2021.acl-long.421/", "pdf_size": 669607, "gs_citation": 22, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=16829742576573810731&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 3, "aff": "School of Cyber Science and Technology, Beihang University, China; School of Cyber Science and Technology, Beihang University, China; State Key Laboratory of Software Development Environment, Beihang University, China; Shenzhen Research Institute of Big Data, Shenzhen, China; State Key Laboratory of Software Development Environment, Beihang University, China", "aff_domain": "buaa.edu.cn;buaa.edu.cn;buaa.edu.cn;sribd.cn;buaa.edu.cn", "email": "buaa.edu.cn;buaa.edu.cn;buaa.edu.cn;sribd.cn;buaa.edu.cn", "github": "", "project": "", "author_num": 5, "aff_unique_index": "0;0;0;1;0", "aff_unique_norm": "Beihang University;Shenzhen Research Institute of Big Data", "aff_unique_dep": "School of Cyber Science and Technology;", "aff_unique_url": "http://www.buaa.edu.cn;http://www.sribd.cn", "aff_unique_abbr": "Beihang;", "aff_campus_unique_index": "1", "aff_campus_unique": ";Shenzhen", "aff_country_unique_index": "0;0;0;0;0", "aff_country_unique": "China" }, { "id": "2021.acl-long.455", "title": "Math Word Problem Solving with Explicit Numerical Values", "track": "main", "status": "Long", "award": false, "abstract": "In recent years, math word problem solving has received considerable attention and achieved promising results, but previous methods rarely take numerical values into consideration. Most methods treat the numerical values in the problems as number symbols, and ignore the prominent role of the numerical values in solving the problem. In this paper, we propose a novel approach called NumS2T, which enhances math word problem solving performance by explicitly incorporating numerical values into a sequence-to-tree network. In addition, a numerical properties prediction mechanism is used to capture the category and comparison information of numerals and measure their importance in global expressions. Experimental results on the Math23K and APE datasets demonstrate that our model achieves better performance than existing state-of-the-art models.", "author": "Qinzhuo Wu; Qi Zhang; Zhongyu Wei; Xuanjing Huang", "authorids": "/q/qinzhuo-wu/; /q/qi-zhang/; /z/zhongyu-wei/; /x/xuan-jing-huang/", "bibtex": "@inproceedings{wu-etal-2021-math,\n title = \"Math Word Problem Solving with Explicit Numerical Values\",\n author = \"Wu, Qinzhuo and\n Zhang, Qi and\n Wei, Zhongyu and\n Huang, Xuanjing\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.455/\",\n doi = \"10.18653/v1/2021.acl-long.455\",\n pages = \"5859--5869\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.455.pdf", "site": "https://aclanthology.org/2021.acl-long.455/", "pdf_size": 1278892, "gs_citation": 63, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=11637619670915884703&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 4, "aff": "Shanghai Key Laboratory of Intelligent Information Processing, School of Computer Science, Fudan University, Shanghai, China; Shanghai Key Laboratory of Intelligent Information Processing, School of Computer Science, Fudan University, Shanghai, China; Shanghai Key Laboratory of Intelligent Information Processing, School of Computer Science, Fudan University, Shanghai, China; Shanghai Key Laboratory of Intelligent Information Processing, School of Computer Science, Fudan University, Shanghai, China", "aff_domain": "fudan.edu.cn;fudan.edu.cn;fudan.edu.cn;fudan.edu.cn", "email": "fudan.edu.cn;fudan.edu.cn;fudan.edu.cn;fudan.edu.cn", "github": "https://github.com/qinzhuowu/NumS2T/", "project": "", "author_num": 4, "aff_unique_index": "0;0;0;0", "aff_unique_norm": "Fudan University", "aff_unique_dep": "School of Computer Science", "aff_unique_url": "https://www.fudan.edu.cn", "aff_unique_abbr": "Fudan", "aff_campus_unique_index": "0;0;0;0", "aff_campus_unique": "Shanghai", "aff_country_unique_index": "0;0;0;0", "aff_country_unique": "China" }, { "id": "2021.acl-long.100", "title": "Measure and Evaluation of Semantic Divergence across Two Languages", "track": "main", "status": "Long", "award": false, "abstract": "Languages are dynamic systems: word usage may change over time, reflecting various societal factors. However, all languages do not evolve identically: the impact of an event, the influence of a trend or thinking, can differ between communities. In this paper, we propose to track these divergences by comparing the evolution of a word and its translation across two languages. We investigate several methods of building time-varying and bilingual word embeddings, using contextualised and non-contextualised embeddings. We propose a set of scenarios to characterize semantic divergence across two languages, along with a setup to differentiate them in a bilingual corpus. We evaluate the different methods by generating a corpus of synthetic semantic change across two languages, English and French, before applying them to newspaper corpora to detect bilingual semantic divergence and provide qualitative insight for the task. We conclude that BERT embeddings coupled with a clustering step lead to the best performance on synthetic corpora; however, the performance of CBOW embeddings is very competitive and more adapted to an exploratory analysis on a large corpus.", "author": "Syrielle Montariol; Alexandre Allauzen", "authorids": "/s/syrielle-montariol/; /a/alexandre-allauzen/", "bibtex": "@inproceedings{montariol-allauzen-2021-measure,\n title = \"Measure and Evaluation of Semantic Divergence across Two Languages\",\n author = \"Montariol, Syrielle and\n Allauzen, Alexandre\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.100/\",\n doi = \"10.18653/v1/2021.acl-long.100\",\n pages = \"1247--1258\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.100.pdf", "site": "https://aclanthology.org/2021.acl-long.100/", "pdf_size": 344730, "gs_citation": 11, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=284625980099642883&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 2, "aff": "INRIA Paris + LISN-CNRS; ESPCI Paris + Dauphine University", "aff_domain": "inria.fr;espci.psl.eu", "email": "inria.fr;espci.psl.eu", "github": "", "project": "", "author_num": 2, "aff_unique_index": "0+1;2+3", "aff_unique_norm": "INRIA;Laboratoire d'Informatique pour la Science du Nombre;\u00c9cole Sup\u00e9rieure de Physique et de Chimie Industrielles de Paris;Universit\u00e9 Paris-Dauphine", "aff_unique_dep": ";;;", "aff_unique_url": "https://www.inria.fr;https://www.lisn.ens-cachan.fr/;https://www.espci.fr;https://www.dauphine.psl.eu", "aff_unique_abbr": "INRIA;LISN;ESPCI;Paris-Dauphine", "aff_campus_unique_index": "0;", "aff_campus_unique": "Paris;", "aff_country_unique_index": "0+0;0+0", "aff_country_unique": "France" }, { "id": "2021.acl-long.130", "title": "Measuring Conversational Uptake: A Case Study on Student-Teacher Interactions", "track": "main", "status": "Long", "award": false, "abstract": "In conversation, uptake happens when a speaker builds on the contribution of their interlocutor by, for example, acknowledging, repeating or reformulating what they have said. In education, teachers\u2019 uptake of student contributions has been linked to higher student achievement. Yet measuring and improving teachers\u2019 uptake at scale is challenging, as existing methods require expensive annotation by experts. We propose a framework for computationally measuring uptake, by (1) releasing a dataset of student-teacher exchanges extracted from US math classroom transcripts annotated for uptake by experts; (2) formalizing uptake as pointwise Jensen-Shannon Divergence (pJSD), estimated via next utterance classification; (3) conducting a linguistically-motivated comparison of different unsupervised measures and (4) correlating these measures with educational outcomes. We find that although repetition captures a significant part of uptake, pJSD outperforms repetition-based baselines, as it is capable of identifying a wider range of uptake phenomena like question answering and reformulation. We apply our uptake measure to three different educational datasets with outcome indicators. Unlike baseline measures, pJSD correlates significantly with instruction quality in all three, providing evidence for its generalizability and for its potential to serve as an automated professional development tool for teachers.", "author": "Dorottya Demszky; Jing Liu; Zid Mancenido; Julie Cohen; Heather Hill; Dan Jurafsky; Tatsunori Hashimoto", "authorids": "/d/dorottya-demszky/; /j/jing-liu/; /z/zid-mancenido/; /j/julie-cohen/; /h/heather-hill/; /d/dan-jurafsky/; /t/tatsunori-b-hashimoto/", "bibtex": "@inproceedings{demszky-etal-2021-measuring,\n title = \"Measuring Conversational Uptake: A Case Study on Student-Teacher Interactions\",\n author = \"Demszky, Dorottya and\n Liu, Jing and\n Mancenido, Zid and\n Cohen, Julie and\n Hill, Heather and\n Jurafsky, Dan and\n Hashimoto, Tatsunori\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.130/\",\n doi = \"10.18653/v1/2021.acl-long.130\",\n pages = \"1638--1653\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.130.pdf", "site": "https://aclanthology.org/2021.acl-long.130/", "pdf_size": 545998, "gs_citation": 81, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=12229547349374100309&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 9, "aff": "Stanford University; University of Maryland; Harvard University; University of Virginia; Harvard University; Stanford University; Stanford University", "aff_domain": "stanford.edu; ; ; ; ; ;stanford.edu", "email": "stanford.edu; ; ; ; ; ;stanford.edu", "github": "https://github.com/ddemszky/conversational-uptake", "project": "", "author_num": 7, "aff_unique_index": "0;1;2;3;2;0;0", "aff_unique_norm": "Stanford University;University of Maryland;Harvard University;University of Virginia", "aff_unique_dep": ";;;", "aff_unique_url": "https://www.stanford.edu;https://www/umd.edu;https://www.harvard.edu;https://www.virginia.edu", "aff_unique_abbr": "Stanford;UMD;Harvard;UVA", "aff_campus_unique_index": "0;0;0", "aff_campus_unique": "Stanford;", "aff_country_unique_index": "0;0;0;0;0;0;0", "aff_country_unique": "United States" }, { "id": "2021.acl-long.282", "title": "Measuring Fine-Grained Domain Relevance of Terms: A Hierarchical Core-Fringe Approach", "track": "main", "status": "Long", "award": false, "abstract": "We propose to measure fine-grained domain relevance\u2013 the degree that a term is relevant to a broad (e.g., computer science) or narrow (e.g., deep learning) domain. Such measurement is crucial for many downstream tasks in natural language processing. To handle long-tail terms, we build a core-anchored semantic graph, which uses core terms with rich description information to bridge the vast remaining fringe terms semantically. To support a fine-grained domain without relying on a matching corpus for supervision, we develop hierarchical core-fringe learning, which learns core and fringe terms jointly in a semi-supervised manner contextualized in the hierarchy of the domain. To reduce expensive human efforts, we employ automatic annotation and hierarchical positive-unlabeled learning. Our approach applies to big or small domains, covers head or tail terms, and requires little human effort. Extensive experiments demonstrate that our methods outperform strong baselines and even surpass professional human performance.", "author": "Jie Huang; Kevin Chang; JinJun Xiong; Wen-mei Hwu", "authorids": "/j/jie-huang/; /k/kevin-chang/; /j/jinjun-xiong/; /w/wen-mei-hwu/", "bibtex": "@inproceedings{huang-etal-2021-measuring,\n title = \"Measuring Fine-Grained Domain Relevance of Terms: A Hierarchical Core-Fringe Approach\",\n author = \"Huang, Jie and\n Chang, Kevin and\n Xiong, JinJun and\n Hwu, Wen-mei\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.282/\",\n doi = \"10.18653/v1/2021.acl-long.282\",\n pages = \"3641--3651\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.282.pdf", "site": "https://aclanthology.org/2021.acl-long.282/", "pdf_size": 749047, "gs_citation": 4, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=7057012057298359143&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 7, "aff": "University of Illinois at Urbana-Champaign, USA+IBM-Illinois Center for Cognitive Computing Systems Research (C3SR), USA; University of Illinois at Urbana-Champaign, USA+IBM-Illinois Center for Cognitive Computing Systems Research (C3SR), USA; IBM Thomas J. Watson Research Center, USA+IBM-Illinois Center for Cognitive Computing Systems Research (C3SR), USA; University of Illinois at Urbana-Champaign, USA+IBM-Illinois Center for Cognitive Computing Systems Research (C3SR), USA", "aff_domain": "illinois.edu;illinois.edu;us.ibm.com;illinois.edu", "email": "illinois.edu;illinois.edu;us.ibm.com;illinois.edu", "github": "https://github.com/jeffhj/domain-relevance", "project": "", "author_num": 4, "aff_unique_index": "0+1;0+1;2+1;0+1", "aff_unique_norm": "University of Illinois at Urbana-Champaign;IBM-Illinois Center for Cognitive Computing Systems Research;IBM Thomas J. Watson Research Center", "aff_unique_dep": ";Center for Cognitive Computing Systems Research;", "aff_unique_url": "https://illinois.edu;;https://www.ibm.com/research/watson", "aff_unique_abbr": "UIUC;C3SR;IBM Watson", "aff_campus_unique_index": "0;0;;0", "aff_campus_unique": "Urbana-Champaign;", "aff_country_unique_index": "0+0;0+0;0+0;0+0", "aff_country_unique": "United States" }, { "id": "2021.acl-short.49", "title": "Measuring and Improving BERT\u2019s Mathematical Abilities by Predicting the Order of Reasoning.", "track": "main", "status": "Short", "award": false, "abstract": "Imagine you are in a supermarket. You have two bananas in your basket and want to buy four apples. How many fruits do you have in total? This seemingly straightforward question can be challenging for data-driven language models, even if trained at scale. However, we would expect such generic language models to possess some mathematical abilities in addition to typical linguistic competence. Towards this goal, we investigate if a commonly used language model, BERT, possesses such mathematical abilities and, if so, to what degree. For that, we fine-tune BERT on a popular dataset for word math problems, AQuA-RAT, and conduct several tests to understand learned representations better. Since we teach models trained on natural language to do formal mathematics, we hypothesize that such models would benefit from training on semi-formal steps that explain how math results are derived. To better accommodate such training, we also propose new pretext tasks for learning mathematical rules. We call them (Neighbor) Reasoning Order Prediction (ROP or NROP). With this new model, we achieve significantly better outcomes than data-driven baselines and even on-par with more tailored models.", "author": "Piotr Pi\u0119kos; Mateusz Malinowski; Henryk Michalewski", "authorids": "/p/piotr-piekos/; /m/mateusz-malinowski/; /h/henryk-michalewski/", "bibtex": "@inproceedings{piekos-etal-2021-measuring,\n title = \"Measuring and Improving {BERT}`s Mathematical Abilities by Predicting the Order of Reasoning.\",\n author = \"Pi{\\k{e}}kos, Piotr and\n Malinowski, Mateusz and\n Michalewski, Henryk\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-short.49/\",\n doi = \"10.18653/v1/2021.acl-short.49\",\n pages = \"383--394\"\n}", "pdf": "https://aclanthology.org/2021.acl-short.49.pdf", "site": "https://aclanthology.org/2021.acl-short.49/", "pdf_size": 1663107, "gs_citation": 34, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=9572011291576926596&as_sdt=5,33&sciodt=0,33&hl=en", "gs_version_total": 5, "aff": "University of Warsaw; DeepMind; University of Warsaw + Google", "aff_domain": "gmail.com; ; ", "email": "gmail.com; ; ", "github": "", "project": "", "author_num": 3, "aff_unique_index": "0;1;0+2", "aff_unique_norm": "University of Warsaw;DeepMind;Google", "aff_unique_dep": ";;", "aff_unique_url": "https://www.uw.edu.pl;https://deepmind.com;https://www.google.com", "aff_unique_abbr": "UW;DeepMind;Google", "aff_campus_unique_index": "1", "aff_campus_unique": ";Mountain View", "aff_country_unique_index": "0;1;0+2", "aff_country_unique": "Poland;United Kingdom;United States" }, { "id": "2021.acl-long.505", "title": "Measuring and Increasing Context Usage in Context-Aware Machine Translation", "track": "main", "status": "Long", "award": false, "abstract": "Recent work in neural machine translation has demonstrated both the necessity and feasibility of using inter-sentential context, context from sentences other than those currently being translated. However, while many current methods present model architectures that theoretically can use this extra context, it is often not clear how much they do actually utilize it at translation time. In this paper, we introduce a new metric, conditional cross-mutual information, to quantify usage of context by these models. Using this metric, we measure how much document-level machine translation systems use particular varieties of context. We find that target context is referenced more than source context, and that including more context has a diminishing affect on results. We then introduce a new, simple training method, context-aware word dropout, to increase the usage of context by context-aware models. Experiments show that our method not only increases context usage, but also improves the translation quality according to metrics such as BLEU and COMET, as well as performance on anaphoric pronoun resolution and lexical cohesion contrastive datasets.", "author": "Patrick Fernandes; Kayo Yin; Graham Neubig; Andr\u00e9 F. T. Martins", "authorids": "/p/patrick-fernandes/; /k/kayo-yin/; /g/graham-neubig/; /a/andre-f-t-martins/", "bibtex": "@inproceedings{fernandes-etal-2021-measuring,\n title = \"Measuring and Increasing Context Usage in Context-Aware Machine Translation\",\n author = \"Fernandes, Patrick and\n Yin, Kayo and\n Neubig, Graham and\n Martins, Andr{\\'e} F. T.\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.505/\",\n doi = \"10.18653/v1/2021.acl-long.505\",\n pages = \"6467--6478\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.505.pdf", "site": "https://aclanthology.org/2021.acl-long.505/", "pdf_size": 381171, "gs_citation": 61, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=10129866795545274041&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 4, "aff": "Language Technologies Institute, Carnegie Mellon University, Pittsburgh, PA + Instituto Superior T\u00e9cnico & LUMLIS (Lisbon ELLIS Unit), Lisbon, Portugal + Instituto de Telecomunicac\u00f5es, Lisbon, Portugal; Language Technologies Institute, Carnegie Mellon University, Pittsburgh, PA; Language Technologies Institute, Carnegie Mellon University, Pittsburgh, PA; Instituto Superior T\u00e9cnico & LUMLIS (Lisbon ELLIS Unit), Lisbon, Portugal + Instituto de Telecomunicac\u00f5es, Lisbon, Portugal + Unbabel, Lisbon, Portugal", "aff_domain": "cs.cmu.edu;cs.cmu.edu;cs.cmu.edu;tecnico.ulisboa.pt", "email": "cs.cmu.edu;cs.cmu.edu;cs.cmu.edu;tecnico.ulisboa.pt", "github": "https://github.com/neulab/contextual-mt", "project": "", "author_num": 4, "aff_unique_index": "0+1+2;0;0;1+2+3", "aff_unique_norm": "Carnegie Mellon University;Instituto Superior T\u00e9cnico;Instituto de Telecomunicac\u00f5es;Unbabel", "aff_unique_dep": "Language Technologies Institute;;;", "aff_unique_url": "https://www.cmu.edu;https://www.ist.utl.pt;https://www.it.pt;https://www.unbabel.com", "aff_unique_abbr": "CMU;IST;;", "aff_campus_unique_index": "0+1+1;0;0;1+1", "aff_campus_unique": "Pittsburgh;Lisbon;", "aff_country_unique_index": "0+1+1;0;0;1+1+1", "aff_country_unique": "United States;Portugal" }, { "id": "2021.acl-short.129", "title": "MedNLI Is Not Immune: Natural Language Inference Artifacts in the Clinical Domain", "track": "main", "status": "Short", "award": false, "abstract": "Crowdworker-constructed natural language inference (NLI) datasets have been found to contain statistical artifacts associated with the annotation process that allow hypothesis-only classifiers to achieve better-than-random performance (CITATION). We investigate whether MedNLI, a physician-annotated dataset with premises extracted from clinical notes, contains such artifacts (CITATION). We find that entailed hypotheses contain generic versions of specific concepts in the premise, as well as modifiers related to responsiveness, duration, and probability. Neutral hypotheses feature conditions and behaviors that co-occur with, or cause, the condition(s) in the premise. Contradiction hypotheses feature explicit negation of the premise and implicit negation via assertion of good health. Adversarial filtering demonstrates that performance degrades when evaluated on the difficult subset. We provide partition information and recommendations for alternative dataset construction strategies for knowledge-intensive domains.", "author": "Christine Herlihy; Rachel Rudinger", "authorids": "/c/christine-herlihy/; /r/rachel-rudinger/", "bibtex": "@inproceedings{herlihy-rudinger-2021-mednli,\n title = \"{M}ed{NLI} Is Not Immune: {N}atural Language Inference Artifacts in the Clinical Domain\",\n author = \"Herlihy, Christine and\n Rudinger, Rachel\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-short.129/\",\n doi = \"10.18653/v1/2021.acl-short.129\",\n pages = \"1020--1027\"\n}", "pdf": "https://aclanthology.org/2021.acl-short.129.pdf", "site": "https://aclanthology.org/2021.acl-short.129/", "pdf_size": 268224, "gs_citation": 27, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=578918540540223408&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 8, "aff": "Department of Computer Science, University of Maryland; Department of Computer Science, University of Maryland", "aff_domain": "cs.umd.edu;umd.edu", "email": "cs.umd.edu;umd.edu", "github": "https://github.com/crherlihy/clinical_nli_artifacts", "project": "", "author_num": 2, "aff_unique_index": "0;0", "aff_unique_norm": "University of Maryland", "aff_unique_dep": "Department of Computer Science", "aff_unique_url": "https://www/umd.edu", "aff_unique_abbr": "UMD", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0;0", "aff_country_unique": "United States" }, { "id": "2021.acl-short.45", "title": "Men Are Elected, Women Are Married: Events Gender Bias on Wikipedia", "track": "main", "status": "Short", "award": false, "abstract": "Human activities can be seen as sequences of events, which are crucial to understanding societies. Disproportional event distribution for different demographic groups can manifest and amplify social stereotypes, and potentially jeopardize the ability of members in some groups to pursue certain goals. In this paper, we present the first event-centric study of gender biases in a Wikipedia corpus. To facilitate the study, we curate a corpus of career and personal life descriptions with demographic information consisting of 7,854 fragments from 10,412 celebrities. Then we detect events with a state-of-the-art event detection model, calibrate the results using strategically generated templates, and extract events that have asymmetric associations with genders. Our study discovers that the Wikipedia pages tend to intermingle personal life events with professional events for females but not for males, which calls for the awareness of the Wikipedia community to formalize guidelines and train the editors to mind the implicit biases that contributors carry. Our work also lays the foundation for future works on quantifying and discovering event biases at the corpus level.", "author": "Jiao Sun; Nanyun Peng", "authorids": "/j/jiao-sun/; /n/nanyun-peng/", "bibtex": "@inproceedings{sun-peng-2021-men,\n title = \"Men Are Elected, Women Are Married: Events Gender Bias on {W}ikipedia\",\n author = \"Sun, Jiao and\n Peng, Nanyun\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-short.45/\",\n doi = \"10.18653/v1/2021.acl-short.45\",\n pages = \"350--360\"\n}", "pdf": "https://aclanthology.org/2021.acl-short.45.pdf", "site": "https://aclanthology.org/2021.acl-short.45/", "pdf_size": 796036, "gs_citation": 51, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=974401863480464592&as_sdt=5,44&sciodt=0,44&hl=en", "gs_version_total": 3, "aff": "Computer Science Department, University of Southern California + Information Sciences Institute, University of Southern California; Computer Science Department, University of Southern California + Information Sciences Institute, University of Southern California + Computer Science Department, University of California, Los Angeles", "aff_domain": "usc.edu;ucla.cs.edu", "email": "usc.edu;ucla.cs.edu", "github": "", "project": "", "author_num": 2, "aff_unique_index": "0+0;0+0+1", "aff_unique_norm": "University of Southern California;University of California, Los Angeles", "aff_unique_dep": "Computer Science Department;Computer Science Department", "aff_unique_url": "https://www.usc.edu;https://www.ucla.edu", "aff_unique_abbr": "USC;UCLA", "aff_campus_unique_index": "0+0;0+0+0", "aff_campus_unique": "Los Angeles", "aff_country_unique_index": "0+0;0+0+0", "aff_country_unique": "United States" }, { "id": "2021.acl-long.9", "title": "Mention Flags (MF): Constraining Transformer-based Text Generators", "track": "main", "status": "Long", "award": false, "abstract": "This paper focuses on Seq2Seq (S2S) constrained text generation where the text generator is constrained to mention specific words which are inputs to the encoder in the generated outputs. Pre-trained S2S models or a Copy Mechanism are trained to copy the surface tokens from encoders to decoders, but they cannot guarantee constraint satisfaction. Constrained decoding algorithms always produce hypotheses satisfying all constraints. However, they are computationally expensive and can lower the generated text quality. In this paper, we propose Mention Flags (MF), which traces whether lexical constraints are satisfied in the generated outputs in an S2S decoder. The MF models can be trained to generate tokens in a hypothesis until all constraints are satisfied, guaranteeing high constraint satisfaction. Our experiments on the Common Sense Generation task (CommonGen) (Lin et al., 2020), End2end Restaurant Dialog task (E2ENLG) (Du\u02c7sek et al., 2020) and Novel Object Captioning task (nocaps) (Agrawal et al., 2019) show that the MF models maintain higher constraint satisfaction and text quality than the baseline models and other constrained decoding algorithms, achieving state-of-the-art performance on all three tasks. These results are achieved with a much lower run-time than constrained decoding algorithms. We also show that the MF models work well in the low-resource setting.", "author": "Yufei Wang; Ian Wood; Stephen Wan; Mark Dras; Mark Johnson", "authorids": "/y/yufei-wang/; /i/ian-wood/; /s/stephen-wan/; /m/mark-dras/; /m/mark-johnson/", "bibtex": "@inproceedings{wang-etal-2021-mention,\n title = \"Mention Flags ({MF}): Constraining Transformer-based Text Generators\",\n author = \"Wang, Yufei and\n Wood, Ian and\n Wan, Stephen and\n Dras, Mark and\n Johnson, Mark\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.9/\",\n doi = \"10.18653/v1/2021.acl-long.9\",\n pages = \"103--113\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.9.pdf", "site": "https://aclanthology.org/2021.acl-long.9/", "pdf_size": 660621, "gs_citation": 40, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=16084095940549553210&as_sdt=400005&sciodt=0,14&hl=en", "gs_version_total": 4, "aff": "Macquarie University, Sydney, Australia; Macquarie University, Sydney, Australia+CSIRO Data61, Sydney, Australia; CSIRO Data61, Sydney, Australia; Macquarie University, Sydney, Australia; Oracle Digital Assistant, Oracle Corporation", "aff_domain": "students.mq.edu.au;mq.edu.au;data61.csiro.au;mq.edu.au;oracle.com", "email": "students.mq.edu.au;mq.edu.au;data61.csiro.au;mq.edu.au;oracle.com", "github": "https://github.com/GaryYufei/ACL2021MF", "project": "", "author_num": 5, "aff_unique_index": "0;0+1;1;0;2", "aff_unique_norm": "Macquarie University;CSIRO Data61;Oracle Corporation", "aff_unique_dep": ";;Oracle Digital Assistant", "aff_unique_url": "https://www.mq.edu.au;https://www.csiro.au/en/Research/Data61;https://www.oracle.com", "aff_unique_abbr": "MQ;CSIRO Data61;Oracle", "aff_campus_unique_index": "0;0+0;0;0", "aff_campus_unique": "Sydney;", "aff_country_unique_index": "0;0+0;0;0;1", "aff_country_unique": "Australia;United States" }, { "id": "2021.acl-long.236", "title": "Meta-KD: A Meta Knowledge Distillation Framework for Language Model Compression across Domains", "track": "main", "status": "Long", "award": false, "abstract": "Pre-trained language models have been applied to various NLP tasks with considerable performance gains. However, the large model sizes, together with the long inference time, limit the deployment of such models in real-time applications. One line of model compression approaches considers knowledge distillation to distill large teacher models into small student models. Most of these studies focus on single-domain only, which ignores the transferable knowledge from other domains. We notice that training a teacher with transferable knowledge digested across domains can achieve better generalization capability to help knowledge distillation. Hence we propose a Meta-Knowledge Distillation (Meta-KD) framework to build a meta-teacher model that captures transferable knowledge across domains and passes such knowledge to students. Specifically, we explicitly force the meta-teacher to capture transferable knowledge at both instance-level and feature-level from multiple domains, and then propose a meta-distillation algorithm to learn single-domain student models with guidance from the meta-teacher. Experiments on public multi-domain NLP tasks show the effectiveness and superiority of the proposed Meta-KD framework. Further, we also demonstrate the capability of Meta-KD in the settings where the training data is scarce.", "author": "Haojie Pan; Chengyu Wang; Minghui Qiu; Yichang Zhang; Yaliang Li; Jun Huang", "authorids": "/h/haojie-pan/; /c/chengyu-wang/; /m/minghui-qiu/; /y/yichang-zhang/; /y/yaliang-li/; /j/jun-huang/", "bibtex": "@inproceedings{pan-etal-2021-meta,\n title = \"Meta-{KD}: A Meta Knowledge Distillation Framework for Language Model Compression across Domains\",\n author = \"Pan, Haojie and\n Wang, Chengyu and\n Qiu, Minghui and\n Zhang, Yichang and\n Li, Yaliang and\n Huang, Jun\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.236/\",\n doi = \"10.18653/v1/2021.acl-long.236\",\n pages = \"3026--3036\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.236.pdf", "site": "https://aclanthology.org/2021.acl-long.236/", "pdf_size": 527695, "gs_citation": 52, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=10343168138935123907&as_sdt=5,44&sciodt=0,44&hl=en", "gs_version_total": 9, "aff": "Zhejiang Lab + Alibaba Group; Alibaba Group; Alibaba Group; Alibaba Group; Alibaba Group; Alibaba Group", "aff_domain": "alibaba-inc.com;alibaba-inc.com;alibaba-inc.com;alibaba-inc.com;alibaba-inc.com;alibaba-inc.com", "email": "alibaba-inc.com;alibaba-inc.com;alibaba-inc.com;alibaba-inc.com;alibaba-inc.com;alibaba-inc.com", "github": "", "project": "", "author_num": 6, "aff_unique_index": "0+1;1;1;1;1;1", "aff_unique_norm": "Zhejiang Lab;Alibaba Group", "aff_unique_dep": ";", "aff_unique_url": "http://www.zhejianglab.com;https://www.alibaba.com", "aff_unique_abbr": ";Alibaba", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0+0;0;0;0;0;0", "aff_country_unique": "China" }, { "id": "2021.acl-long.258", "title": "Meta-Learning to Compositionally Generalize", "track": "main", "status": "Long", "award": false, "abstract": "Natural language is compositional; the meaning of a sentence is a function of the meaning of its parts. This property allows humans to create and interpret novel sentences, generalizing robustly outside their prior experience. Neural networks have been shown to struggle with this kind of generalization, in particular performing poorly on tasks designed to assess compositional generalization (i.e. where training and testing distributions differ in ways that would be trivial for a compositional strategy to resolve). Their poor performance on these tasks may in part be due to the nature of supervised learning which assumes training and testing data to be drawn from the same distribution. We implement a meta-learning augmented version of supervised learning whose objective directly optimizes for out-of-distribution generalization. We construct pairs of tasks for meta-learning by sub-sampling existing training data. Each pair of tasks is constructed to contain relevant examples, as determined by a similarity metric, in an effort to inhibit models from memorizing their input. Experimental results on the COGS and SCAN datasets show that our similarity-driven meta-learning can improve generalization performance.", "author": "Henry Conklin; Bailin Wang; Kenny Smith; Ivan Titov", "authorids": "/h/henry-conklin/; /b/bailin-wang/; /k/kenny-smith/; /i/ivan-titov/", "bibtex": "@inproceedings{conklin-etal-2021-meta,\n title = \"Meta-Learning to Compositionally Generalize\",\n author = \"Conklin, Henry and\n Wang, Bailin and\n Smith, Kenny and\n Titov, Ivan\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.258/\",\n doi = \"10.18653/v1/2021.acl-long.258\",\n pages = \"3322--3335\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.258.pdf", "site": "https://aclanthology.org/2021.acl-long.258/", "pdf_size": 485358, "gs_citation": 84, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=14005159970224900498&as_sdt=5,31&sciodt=0,31&hl=en", "gs_version_total": 6, "aff": "University of Edinburgh; University of Edinburgh; University of Edinburgh; University of Edinburgh+University of Amsterdam", "aff_domain": "ed.ac.uk;ed.ac.uk;ed.ac.uk;inf.ed.ac.uk", "email": "ed.ac.uk;ed.ac.uk;ed.ac.uk;inf.ed.ac.uk", "github": "", "project": "", "author_num": 4, "aff_unique_index": "0;0;0;0+1", "aff_unique_norm": "University of Edinburgh;University of Amsterdam", "aff_unique_dep": ";", "aff_unique_url": "https://www.ed.ac.uk;https://www.uva.nl", "aff_unique_abbr": "Edinburgh;UvA", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0;0;0;0+1", "aff_country_unique": "United Kingdom;Netherlands" }, { "id": "2021.acl-long.409", "title": "Meta-Learning with Variational Semantic Memory for Word Sense Disambiguation", "track": "main", "status": "Long", "award": false, "abstract": "A critical challenge faced by supervised word sense disambiguation (WSD) is the lack of large annotated datasets with sufficient coverage of words in their diversity of senses. This inspired recent research on few-shot WSD using meta-learning. While such work has successfully applied meta-learning to learn new word senses from very few examples, its performance still lags behind its fully-supervised counterpart. Aiming to further close this gap, we propose a model of semantic memory for WSD in a meta-learning setting. Semantic memory encapsulates prior experiences seen throughout the lifetime of the model, which aids better generalization in limited data settings. Our model is based on hierarchical variational inference and incorporates an adaptive memory update rule via a hypernetwork. We show our model advances the state of the art in few-shot WSD, supports effective learning in extremely data scarce (e.g. one-shot) scenarios and produces meaning prototypes that capture similar senses of distinct words.", "author": "Yingjun Du; Nithin Holla; Xiantong Zhen; Cees Snoek; Ekaterina Shutova", "authorids": "/y/yingjun-du/; /n/nithin-holla/; /x/xiantong-zhen/; /c/cees-snoek/; /e/ekaterina-shutova/", "bibtex": "@inproceedings{du-etal-2021-meta,\n title = \"Meta-Learning with Variational Semantic Memory for Word Sense Disambiguation\",\n author = \"Du, Yingjun and\n Holla, Nithin and\n Zhen, Xiantong and\n Snoek, Cees and\n Shutova, Ekaterina\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.409/\",\n doi = \"10.18653/v1/2021.acl-long.409\",\n pages = \"5254--5268\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.409.pdf", "site": "https://aclanthology.org/2021.acl-long.409/", "pdf_size": 983737, "gs_citation": 13, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=15123690440516969871&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 6, "aff": "University of Amsterdam; Amberscript + University of Amsterdam; University of Amsterdam; University of Amsterdam; University of Amsterdam", "aff_domain": "uva.nl;gmail.com;uva.nl;uva.nl;uva.nl", "email": "uva.nl;gmail.com;uva.nl;uva.nl;uva.nl", "github": "", "project": "", "author_num": 5, "aff_unique_index": "0;1+0;0;0;0", "aff_unique_norm": "University of Amsterdam;Amberscript", "aff_unique_dep": ";", "aff_unique_url": "https://www.uva.nl;", "aff_unique_abbr": "UvA;", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0;0;0;0;0", "aff_country_unique": "Netherlands;" }, { "id": "2021.acl-long.524", "title": "Metaphor Generation with Conceptual Mappings", "track": "main", "status": "Long", "award": false, "abstract": "Generating metaphors is a difficult task as it requires understanding nuanced relationships between abstract concepts. In this paper, we aim to generate a metaphoric sentence given a literal expression by replacing relevant verbs. Guided by conceptual metaphor theory, we propose to control the generation process by encoding conceptual mappings between cognitive domains to generate meaningful metaphoric expressions. To achieve this, we develop two methods: 1) using FrameNet-based embeddings to learn mappings between domains and applying them at the lexical level (CM-Lex), and 2) deriving source/target pairs to train a controlled seq-to-seq generation model (CM-BART). We assess our methods through automatic and human evaluation for basic metaphoricity and conceptual metaphor presence. We show that the unsupervised CM-Lex model is competitive with recent deep learning metaphor generation systems, and CM-BART outperforms all other models both in automatic and human evaluations.", "author": "Kevin Stowe; Tuhin Chakrabarty; Nanyun Peng; Smaranda Muresan; Iryna Gurevych", "authorids": "/k/kevin-stowe/; /t/tuhin-chakrabarty/; /n/nanyun-peng/; /s/smaranda-muresan/; /i/iryna-gurevych/", "bibtex": "@inproceedings{stowe-etal-2021-metaphor,\n title = \"Metaphor Generation with Conceptual Mappings\",\n author = \"Stowe, Kevin and\n Chakrabarty, Tuhin and\n Peng, Nanyun and\n Muresan, Smaranda and\n Gurevych, Iryna\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.524/\",\n doi = \"10.18653/v1/2021.acl-long.524\",\n pages = \"6724--6736\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.524.pdf", "site": "https://aclanthology.org/2021.acl-long.524/", "pdf_size": 488961, "gs_citation": 55, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=14089505704706775197&as_sdt=5,33&sciodt=0,33&hl=en", "gs_version_total": 6, "aff": "Ubiquitous Knowledge Processing Lab, Technical University of Darmstadt; Columbia University; University of California Los Angeles; Columbia University; Ubiquitous Knowledge Processing Lab, Technical University of Darmstadt", "aff_domain": "tu-darmstadt.de;cs.columbia.edu;cs.ucla.edu;cs.columbia.edu;tu-darmstadt.de", "email": "tu-darmstadt.de;cs.columbia.edu;cs.ucla.edu;cs.columbia.edu;tu-darmstadt.de", "github": "https://github.com/UKPLab/acl2021-metaphor-generation-conceptual", "project": "", "author_num": 5, "aff_unique_index": "0;1;2;1;0", "aff_unique_norm": "Technical University of Darmstadt;Columbia University;University of California, Los Angeles", "aff_unique_dep": "Ubiquitous Knowledge Processing Lab;;", "aff_unique_url": "https://www.tu-darmstadt.de;https://www.columbia.edu;https://www.ucla.edu", "aff_unique_abbr": "TUD;Columbia;UCLA", "aff_campus_unique_index": "1", "aff_campus_unique": ";Los Angeles", "aff_country_unique_index": "0;1;1;1;0", "aff_country_unique": "Germany;United States" }, { "id": "2021.acl-long.527", "title": "Mid-Air Hand Gestures for Post-Editing of Machine Translation", "track": "main", "status": "Long", "award": false, "abstract": "To translate large volumes of text in a globally connected world, more and more translators are integrating machine translation (MT) and post-editing (PE) into their translation workflows to generate publishable quality translations. While this process has been shown to save time and reduce errors, the task of translation is changing from mostly text production from scratch to fixing errors within useful but partly incorrect MT output. This is affecting the interface design of translation tools, where better support for text editing tasks is required. Here, we present the first study that investigates the usefulness of mid-air hand gestures in combination with the keyboard (GK) for text editing in PE of MT. Guided by a gesture elicitation study with 14 freelance translators, we develop a prototype supporting mid-air hand gestures for cursor placement, text selection, deletion, and reordering. These gestures combined with the keyboard facilitate all editing types required for PE. An evaluation of the prototype shows that the average editing duration of GK is only slightly slower than the standard mouse and keyboard (MK), even though participants are very familiar with the latter, and relative novices to the former. Furthermore, the qualitative analysis shows positive attitudes towards hand gestures for PE, especially when manipulating single words.", "author": "Rashad Albo Jamara; Nico Herbig; Antonio Kr\u00fcger; Josef van Genabith", "authorids": "/r/rashad-albo-jamara/; /n/nico-herbig/; /a/antonio-kruger/; /j/josef-van-genabith/", "bibtex": "@inproceedings{albo-jamara-etal-2021-mid,\n title = \"Mid-Air Hand Gestures for Post-Editing of Machine Translation\",\n author = {Albo Jamara, Rashad and\n Herbig, Nico and\n Kr{\\\"u}ger, Antonio and\n van Genabith, Josef},\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.527/\",\n doi = \"10.18653/v1/2021.acl-long.527\",\n pages = \"6763--6773\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.527.pdf", "site": "https://aclanthology.org/2021.acl-long.527/", "pdf_size": 8649789, "gs_citation": 3, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=12291409222952751073&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 3, "aff": "German Research Center for Artificial Intelligence (DFKI), Saarland Informatics Campus, Germany + Department of Language Science and Technology, Saarland University, Germany; German Research Center for Artificial Intelligence (DFKI), Saarland Informatics Campus, Germany; German Research Center for Artificial Intelligence (DFKI), Saarland Informatics Campus, Germany; German Research Center for Artificial Intelligence (DFKI), Saarland Informatics Campus, Germany + Department of Language Science and Technology, Saarland University, Germany", "aff_domain": "gmail.com;dfki.de;dfki.de;dfki.de", "email": "gmail.com;dfki.de;dfki.de;dfki.de", "github": "", "project": "", "author_num": 4, "aff_unique_index": "0+1;0;0;0+1", "aff_unique_norm": "German Research Center for Artificial Intelligence;Saarland University", "aff_unique_dep": "DFKI;Department of Language Science and Technology", "aff_unique_url": "https://www.dFKI.de;https://www.uni-saarland.de", "aff_unique_abbr": "DFKI;Saarland U", "aff_campus_unique_index": "0;0;0;0", "aff_campus_unique": "Saarland;", "aff_country_unique_index": "0+0;0;0;0+0", "aff_country_unique": "Germany" }, { "id": "2021.acl-long.564", "title": "Mind Your Outliers! Investigating the Negative Impact of Outliers on Active Learning for Visual Question Answering", "track": "main", "status": "Long", "award": true, "abstract": "Active learning promises to alleviate the massive data needs of supervised machine learning: it has successfully improved sample efficiency by an order of magnitude on traditional tasks like topic classification and object recognition. However, we uncover a striking contrast to this promise: across 5 models and 4 datasets on the task of visual question answering, a wide variety of active learning approaches fail to outperform random selection. To understand this discrepancy, we profile 8 active learning methods on a per-example basis, and identify the problem as collective outliers \u2013 groups of examples that active learning methods prefer to acquire but models fail to learn (e.g., questions that ask about text in images or require external knowledge). Through systematic ablation experiments and qualitative visualizations, we verify that collective outliers are a general phenomenon responsible for degrading pool-based active learning. Notably, we show that active learning sample efficiency increases significantly as the number of collective outliers in the active learning pool decreases. We conclude with a discussion and prescriptive recommendations for mitigating the effects of these outliers in future work.", "author": "Siddharth Karamcheti; Ranjay Krishna; Li Fei-Fei; Christopher Manning", "authorids": "/s/siddharth-karamcheti/; /r/ranjay-krishna/; /l/li-fei-fei/; /c/christopher-d-manning/", "bibtex": "@inproceedings{karamcheti-etal-2021-mind,\n title = \"Mind Your Outliers! Investigating the Negative Impact of Outliers on Active Learning for Visual Question Answering\",\n author = \"Karamcheti, Siddharth and\n Krishna, Ranjay and\n Fei-Fei, Li and\n Manning, Christopher\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.564/\",\n doi = \"10.18653/v1/2021.acl-long.564\",\n pages = \"7265--7281\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.564.pdf", "site": "https://aclanthology.org/2021.acl-long.564/", "pdf_size": 2539312, "gs_citation": 103, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=5362822775365302764&as_sdt=5,39&sciodt=0,39&hl=en", "gs_version_total": 6, "aff": "Department of Computer Science, Stanford University; Department of Computer Science, Stanford University; Department of Computer Science, Stanford University; Department of Computer Science, Stanford University", "aff_domain": "cs.stanford.edu;cs.stanford.edu;cs.stanford.edu;cs.stanford.edu", "email": "cs.stanford.edu;cs.stanford.edu;cs.stanford.edu;cs.stanford.edu", "github": "", "project": "https://vizwiz.org/; https://taptapsee.com/; https://www.bemyeyes.com/; https://camfindapp.com/", "author_num": 4, "aff_unique_index": "0;0;0;0", "aff_unique_norm": "Stanford University", "aff_unique_dep": "Department of Computer Science", "aff_unique_url": "https://www.stanford.edu", "aff_unique_abbr": "Stanford", "aff_campus_unique_index": "0;0;0;0", "aff_campus_unique": "Stanford", "aff_country_unique_index": "0;0;0;0", "aff_country_unique": "United States" }, { "id": "2021.acl-long.203", "title": "Missing Modality Imagination Network for Emotion Recognition with Uncertain Missing Modalities", "track": "main", "status": "Long", "award": false, "abstract": "Multimodal fusion has been proved to improve emotion recognition performance in previous works. However, in real-world applications, we often encounter the problem of missing modality, and which modalities will be missing is uncertain. It makes the fixed multimodal fusion fail in such cases. In this work, we propose a unified model, Missing Modality Imagination Network (MMIN), to deal with the uncertain missing modality problem. MMIN learns robust joint multimodal representations, which can predict the representation of any missing modality given available modalities under different missing modality conditions. Comprehensive experiments on two benchmark datasets demonstrate that the unified MMIN model significantly improves emotion recognition performance under both uncertain missing-modality testing conditions and full-modality ideal testing condition. The code will be available at https://github.com/AIM3-RUC/MMIN.", "author": "Jinming Zhao; Ruichen Li; Qin Jin", "authorids": "/j/jinming-zhao/; /r/ruichen-li/; /q/qin-jin/", "bibtex": "@inproceedings{zhao-etal-2021-missing,\n title = \"Missing Modality Imagination Network for Emotion Recognition with Uncertain Missing Modalities\",\n author = \"Zhao, Jinming and\n Li, Ruichen and\n Jin, Qin\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.203/\",\n doi = \"10.18653/v1/2021.acl-long.203\",\n pages = \"2608--2618\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.203.pdf", "site": "https://aclanthology.org/2021.acl-long.203/", "pdf_size": 2074709, "gs_citation": 172, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=15616427545929165900&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 2, "aff": "School of Information, Renmin University of China; School of Information, Renmin University of China; School of Information, Renmin University of China", "aff_domain": "ruc.edu.cn;ruc.edu.cn;ruc.edu.cn", "email": "ruc.edu.cn;ruc.edu.cn;ruc.edu.cn", "github": "https://github.com/AIM3-RUC/MMIN", "project": "", "author_num": 3, "aff_unique_index": "0;0;0", "aff_unique_norm": "Renmin University of China", "aff_unique_dep": "School of Information", "aff_unique_url": "http://www.ruc.edu.cn", "aff_unique_abbr": "RUC", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0;0;0", "aff_country_unique": "China" }, { "id": "2021.acl-long.168", "title": "Mitigating Bias in Session-based Cyberbullying Detection: A Non-Compromising Approach", "track": "main", "status": "Long", "award": false, "abstract": "The element of repetition in cyberbullying behavior has directed recent computational studies toward detecting cyberbullying based on a social media session. In contrast to a single text, a session may consist of an initial post and an associated sequence of comments. Yet, emerging efforts to enhance the performance of session-based cyberbullying detection have largely overlooked unintended social biases in existing cyberbullying datasets. For example, a session containing certain demographic-identity terms (e.g., \u201cgay\u201d or \u201cblack\u201d) is more likely to be classified as an instance of cyberbullying. In this paper, we first show evidence of such bias in models trained on sessions collected from different social media platforms (e.g., Instagram). We then propose a context-aware and model-agnostic debiasing strategy that leverages a reinforcement learning technique, without requiring any extra resources or annotations apart from a pre-defined set of sensitive triggers commonly used for identifying cyberbullying instances. Empirical evaluations show that the proposed strategy can simultaneously alleviate the impacts of the unintended biases and improve the detection performance.", "author": "Lu Cheng; Ahmadreza Mosallanezhad; Yasin Silva; Deborah Hall; Huan Liu", "authorids": "/l/lu-cheng/; /a/ahmadreza-mosallanezhad/; /y/yasin-silva/; /d/deborah-hall/; /h/huan-liu/", "bibtex": "@inproceedings{cheng-etal-2021-mitigating,\n title = \"Mitigating Bias in Session-based Cyberbullying Detection: A Non-Compromising Approach\",\n author = \"Cheng, Lu and\n Mosallanezhad, Ahmadreza and\n Silva, Yasin and\n Hall, Deborah and\n Liu, Huan\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.168/\",\n doi = \"10.18653/v1/2021.acl-long.168\",\n pages = \"2158--2168\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.168.pdf", "site": "https://aclanthology.org/2021.acl-long.168/", "pdf_size": 471320, "gs_citation": 45, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=12464325002300925717&as_sdt=5,33&sciodt=0,33&hl=en", "gs_version_total": 8, "aff": "Computer Science and Engineering, Arizona State University; Computer Science and Engineering, Arizona State University; Mathematical and Natural Sciences, Arizona State University; Social and Behavioral Sciences, Arizona State University; Computer Science and Engineering, Arizona State University", "aff_domain": "asu.edu;asu.edu;asu.edu;asu.edu;asu.edu", "email": "asu.edu;asu.edu;asu.edu;asu.edu;asu.edu", "github": "", "project": "", "author_num": 5, "aff_unique_index": "0;0;0;0;0", "aff_unique_norm": "Arizona State University", "aff_unique_dep": "Computer Science and Engineering", "aff_unique_url": "https://www.asu.edu", "aff_unique_abbr": "ASU", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0;0;0;0;0", "aff_country_unique": "United States" }, { "id": "2021.acl-long.444", "title": "Modeling Bilingual Conversational Characteristics for Neural Chat Translation", "track": "main", "status": "Long", "award": false, "abstract": "Neural chat translation aims to translate bilingual conversational text, which has a broad application in international exchanges and cooperation. Despite the impressive performance of sentence-level and context-aware Neural Machine Translation (NMT), there still remain challenges to translate bilingual conversational text due to its inherent characteristics such as role preference, dialogue coherence, and translation consistency. In this paper, we aim to promote the translation quality of conversational text by modeling the above properties. Specifically, we design three latent variational modules to learn the distributions of bilingual conversational characteristics. Through sampling from these learned distributions, the latent variables, tailored for role preference, dialogue coherence, and translation consistency, are incorporated into the NMT model for better translation. We evaluate our approach on the benchmark dataset BConTrasT (English<->German) and a self-collected bilingual dialogue corpus, named BMELD (English<->Chinese). Extensive experiments show that our approach notably boosts the performance over strong baselines by a large margin and significantly surpasses some state-of-the-art context-aware NMT models in terms of BLEU and TER. Additionally, we make the BMELD dataset publicly available for the research community.", "author": "Yunlong Liang; Fandong Meng; Yufeng Chen; Jinan Xu; Jie Zhou", "authorids": "/y/yunlong-liang/; /f/fandong-meng/; /y/yufeng-chen/; /j/jinan-xu/; /j/jie-zhou/", "bibtex": "@inproceedings{liang-etal-2021-modeling,\n title = \"Modeling Bilingual Conversational Characteristics for Neural Chat Translation\",\n author = \"Liang, Yunlong and\n Meng, Fandong and\n Chen, Yufeng and\n Xu, Jinan and\n Zhou, Jie\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.444/\",\n doi = \"10.18653/v1/2021.acl-long.444\",\n pages = \"5711--5724\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.444.pdf", "site": "https://aclanthology.org/2021.acl-long.444/", "pdf_size": 1668023, "gs_citation": 33, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=8220884313817469971&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 4, "aff": "Beijing Key Lab of Traffic Data Analysis and Mining, Beijing Jiaotong University, Beijing, China + Pattern Recognition Center, WeChat AI, Tencent Inc, China; Pattern Recognition Center, WeChat AI, Tencent Inc, China; Beijing Key Lab of Traffic Data Analysis and Mining, Beijing Jiaotong University, Beijing, China; Beijing Key Lab of Traffic Data Analysis and Mining, Beijing Jiaotong University, Beijing, China; Pattern Recognition Center, WeChat AI, Tencent Inc, China", "aff_domain": "bjtu.edu.cn;tencent.com;bjtu.edu.cn;bjtu.edu.cn;tencent.com", "email": "bjtu.edu.cn;tencent.com;bjtu.edu.cn;bjtu.edu.cn;tencent.com", "github": "https://github.com/XL2248/CPCC", "project": "", "author_num": 5, "aff_unique_index": "0+1;1;0;0;1", "aff_unique_norm": "Beijing Jiaotong University;Tencent Inc", "aff_unique_dep": "Beijing Key Lab of Traffic Data Analysis and Mining;Pattern Recognition Center, WeChat AI", "aff_unique_url": "http://www.bjtu.edu.cn;https://www.tencent.com", "aff_unique_abbr": "BJTU;Tencent", "aff_campus_unique_index": "0;0;0", "aff_campus_unique": "Beijing;", "aff_country_unique_index": "0+0;0;0;0;0", "aff_country_unique": "China" }, { "id": "2021.acl-short.110", "title": "Modeling Discriminative Representations for Out-of-Domain Detection with Supervised Contrastive Learning", "track": "main", "status": "Short", "award": false, "abstract": "Detecting Out-of-Domain (OOD) or unknown intents from user queries is essential in a task-oriented dialog system. A key challenge of OOD detection is to learn discriminative semantic features. Traditional cross-entropy loss only focuses on whether a sample is correctly classified, and does not explicitly distinguish the margins between categories. In this paper, we propose a supervised contrastive learning objective to minimize intra-class variance by pulling together in-domain intents belonging to the same class and maximize inter-class variance by pushing apart samples from different classes. Besides, we employ an adversarial augmentation mechanism to obtain pseudo diverse views of a sample in the latent space. Experiments on two public datasets prove the effectiveness of our method capturing discriminative representations for OOD detection.", "author": "Zhiyuan Zeng; Keqing He; Yuanmeng Yan; Zijun Liu; Yanan Wu; Hong Xu; Huixing Jiang; Weiran Xu", "authorids": "/z/zhiyuan-zeng/; /k/keqing-he/; /y/yuanmeng-yan/; /z/zijun-liu/; /y/yanan-wu/; /h/hong-xu/; /h/huixing-jiang/; /w/weiran-xu/", "bibtex": "@inproceedings{zeng-etal-2021-modeling,\n title = \"Modeling Discriminative Representations for Out-of-Domain Detection with Supervised Contrastive Learning\",\n author = \"Zeng, Zhiyuan and\n He, Keqing and\n Yan, Yuanmeng and\n Liu, Zijun and\n Wu, Yanan and\n Xu, Hong and\n Jiang, Huixing and\n Xu, Weiran\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-short.110/\",\n doi = \"10.18653/v1/2021.acl-short.110\",\n pages = \"870--878\"\n}", "pdf": "https://aclanthology.org/2021.acl-short.110.pdf", "site": "https://aclanthology.org/2021.acl-short.110/", "pdf_size": 539047, "gs_citation": 66, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=5551351944902925278&as_sdt=5,33&sciodt=0,33&hl=en", "gs_version_total": 4, "aff": "Pattern Recognition & Intelligent System Laboratory, Beijing University of Posts and Telecommunications, Beijing, China; Meituan Group, Beijing, China; Pattern Recognition & Intelligent System Laboratory, Beijing University of Posts and Telecommunications, Beijing, China; Pattern Recognition & Intelligent System Laboratory, Beijing University of Posts and Telecommunications, Beijing, China; Pattern Recognition & Intelligent System Laboratory, Beijing University of Posts and Telecommunications, Beijing, China; Pattern Recognition & Intelligent System Laboratory, Beijing University of Posts and Telecommunications, Beijing, China; Meituan Group, Beijing, China; Pattern Recognition & Intelligent System Laboratory, Beijing University of Posts and Telecommunications, Beijing, China", "aff_domain": "bupt.edu.cn;meituan.com;bupt.edu.cn;bupt.edu.cn;bupt.edu.cn;bupt.edu.cn;meituan.com;bupt.edu.cn", "email": "bupt.edu.cn;meituan.com;bupt.edu.cn;bupt.edu.cn;bupt.edu.cn;bupt.edu.cn;meituan.com;bupt.edu.cn", "github": "https://github.com/parZival27/supervised-contrastive-learning-for-out-of-domain-detection", "project": "", "author_num": 8, "aff_unique_index": "0;1;0;0;0;0;1;0", "aff_unique_norm": "Beijing University of Posts and Telecommunications;Meituan Group", "aff_unique_dep": "Pattern Recognition & Intelligent System Laboratory;", "aff_unique_url": "http://www.bupt.edu.cn/;https://www.meituan.com", "aff_unique_abbr": "BUPT;Meituan", "aff_campus_unique_index": "0;0;0;0;0;0;0;0", "aff_campus_unique": "Beijing", "aff_country_unique_index": "0;0;0;0;0;0;0;0", "aff_country_unique": "China" }, { "id": "2021.acl-long.160", "title": "Modeling Fine-Grained Entity Types with Box Embeddings", "track": "main", "status": "Long", "award": false, "abstract": "Neural entity typing models typically represent fine-grained entity types as vectors in a high-dimensional space, but such spaces are not well-suited to modeling these types\u2019 complex interdependencies. We study the ability of box embeddings, which embed concepts as d-dimensional hyperrectangles, to capture hierarchies of types even when these relationships are not defined explicitly in the ontology. Our model represents both types and entity mentions as boxes. Each mention and its context are fed into a BERT-based model to embed that mention in our box space; essentially, this model leverages typological clues present in the surface text to hypothesize a type representation for the mention. Box containment can then be used to derive both the posterior probability of a mention exhibiting a given type and the conditional probability relations between types themselves. We compare our approach with a vector-based typing model and observe state-of-the-art performance on several entity typing benchmarks. In addition to competitive typing performance, our box-based model shows better performance in prediction consistency (predicting a supertype and a subtype together) and confidence (i.e., calibration), demonstrating that the box-based model captures the latent type hierarchies better than the vector-based model does.", "author": "Yasumasa Onoe; Michael Boratko; Andrew McCallum; Greg Durrett", "authorids": "/y/yasumasa-onoe/; /m/michael-boratko/; /a/andrew-mccallum/; /g/greg-durrett/", "bibtex": "@inproceedings{onoe-etal-2021-modeling,\n title = \"Modeling Fine-Grained Entity Types with Box Embeddings\",\n author = \"Onoe, Yasumasa and\n Boratko, Michael and\n McCallum, Andrew and\n Durrett, Greg\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.160/\",\n doi = \"10.18653/v1/2021.acl-long.160\",\n pages = \"2051--2064\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.160.pdf", "site": "https://aclanthology.org/2021.acl-long.160/", "pdf_size": 4982176, "gs_citation": 80, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=4833679633077937974&as_sdt=5,44&sciodt=0,44&hl=en", "gs_version_total": 5, "aff": "The University of Texas at Austin; University of Massachusetts Amherst; Google Research + University of Massachusetts Amherst; The University of Texas at Austin", "aff_domain": "cs.utexas.edu;cs.utexas.edu;cs.umass.edu;google.com", "email": "cs.utexas.edu;cs.utexas.edu;cs.umass.edu;google.com", "github": "https://github.com/yasumasaonoe/Box4Types", "project": "", "author_num": 4, "aff_unique_index": "0;1;2+1;0", "aff_unique_norm": "University of Texas at Austin;University of Massachusetts Amherst;Google", "aff_unique_dep": ";;Google Research", "aff_unique_url": "https://www.utexas.edu;https://www.umass.edu;https://research.google", "aff_unique_abbr": "UT Austin;UMass Amherst;Google Research", "aff_campus_unique_index": "0;1;2+1;0", "aff_campus_unique": "Austin;Amherst;Mountain View", "aff_country_unique_index": "0;0;0+0;0", "aff_country_unique": "United States" }, { "id": "2021.acl-long.52", "title": "Modeling Language Usage and Listener Engagement in Podcasts", "track": "main", "status": "Long", "award": false, "abstract": "While there is an abundance of advice to podcast creators on how to speak in ways that engage their listeners, there has been little data-driven analysis of podcasts that relates linguistic style with engagement. In this paper, we investigate how various factors \u2013 vocabulary diversity, distinctiveness, emotion, and syntax, among others \u2013 correlate with engagement, based on analysis of the creators\u2019 written descriptions and transcripts of the audio. We build models with different textual representations, and show that the identified features are highly predictive of engagement. Our analysis tests popular wisdom about stylistic elements in high-engagement podcasts, corroborating some pieces of advice and adding new perspectives on others.", "author": "Sravana Reddy; Mariya Lazarova; Yongze Yu; Rosie Jones", "authorids": "/s/sravana-reddy/; /m/mariya-lazarova/; /y/yongze-yu/; /r/rosie-jones/", "bibtex": "@inproceedings{reddy-etal-2021-modeling,\n title = \"Modeling Language Usage and Listener Engagement in Podcasts\",\n author = \"Reddy, Sravana and\n Lazarova, Mariya and\n Yu, Yongze and\n Jones, Rosie\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.52/\",\n doi = \"10.18653/v1/2021.acl-long.52\",\n pages = \"632--643\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.52.pdf", "site": "https://aclanthology.org/2021.acl-long.52/", "pdf_size": 271506, "gs_citation": 12, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=4243738378039449330&as_sdt=5,33&sciodt=0,33&hl=en", "gs_version_total": 4, "aff": "Spotify; Spotify; Spotify; Spotify", "aff_domain": "spotify.com;gmail.com;spotify.com;spotify.com", "email": "spotify.com;gmail.com;spotify.com;spotify.com", "github": "", "project": "", "author_num": 4, "aff_unique_index": "0;0;0;0", "aff_unique_norm": "Spotify", "aff_unique_dep": "", "aff_unique_url": "https://www.spotify.com", "aff_unique_abbr": "Spotify", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0;0;0;0", "aff_country_unique": "Sweden" }, { "id": "2021.acl-short.46", "title": "Modeling Task-Aware MIMO Cardinality for Efficient Multilingual Neural Machine Translation", "track": "main", "status": "Short", "award": false, "abstract": "Neural machine translation has achieved great success in bilingual settings, as well as in multilingual settings. With the increase of the number of languages, multilingual systems tend to underperform their bilingual counterparts. Model capacity has been found crucial for massively multilingual NMT to support language pairs with varying typological characteristics. Previous work increases the modeling capacity by deepening or widening the Transformer. However, modeling cardinality based on aggregating a set of transformations with the same topology has been proven more effective than going deeper or wider when increasing capacity. In this paper, we propose to efficiently increase the capacity for multilingual NMT by increasing the cardinality. Unlike previous work which feeds the same input to several transformations and merges their outputs into one, we present a Multi-Input-Multi-Output (MIMO) architecture that allows each transformation of the block to have its own input. We also present a task-aware attention mechanism to learn to selectively utilize individual transformations from a set of transformations for different translation directions. Our model surpasses previous work and establishes a new state-of-the-art on the large scale OPUS-100 corpus while being 1.31 times as fast.", "author": "Hongfei Xu; Qiuhui Liu; Josef van Genabith; Deyi Xiong", "authorids": "/h/hongfei-xu/; /q/qiuhui-liu/; /j/josef-van-genabith/; /d/deyi-xiong/", "bibtex": "@inproceedings{xu-etal-2021-modeling,\n title = \"Modeling Task-Aware {MIMO} Cardinality for Efficient Multilingual Neural Machine Translation\",\n author = \"Xu, Hongfei and\n Liu, Qiuhui and\n van Genabith, Josef and\n Xiong, Deyi\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-short.46/\",\n doi = \"10.18653/v1/2021.acl-short.46\",\n pages = \"361--367\"\n}", "pdf": "https://aclanthology.org/2021.acl-short.46.pdf", "site": "https://aclanthology.org/2021.acl-short.46/", "pdf_size": 313783, "gs_citation": 8, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=17861677113850987904&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 2, "aff": "DFKI and Saarland University, Informatics Campus, Saarland, Germany; China Mobile Online Services, Henan, China; DFKI and Saarland University, Informatics Campus, Saarland, Germany; Tianjin University, Tianjin, China + Global Tone Communication Technology Co., Ltd.", "aff_domain": "foxmail.com;foxmail.com;dfki.de;tju.edu.cn", "email": "foxmail.com;foxmail.com;dfki.de;tju.edu.cn", "github": "", "project": "", "author_num": 4, "aff_unique_index": "0;1;0;2+3", "aff_unique_norm": "DFKI;China Mobile Online Services;Tianjin University;Global Tone Communication Technology", "aff_unique_dep": "Informatics;;;", "aff_unique_url": "https://www.dFKI.de;;http://www.tju.edu.cn;", "aff_unique_abbr": "DFKI;;Tianjin U;", "aff_campus_unique_index": "0;0;2", "aff_campus_unique": "Saarland;;Tianjin", "aff_country_unique_index": "0;1;0;1+1", "aff_country_unique": "Germany;China" }, { "id": "2021.acl-long.255", "title": "Modeling Transitions of Focal Entities for Conversational Knowledge Base Question Answering", "track": "main", "status": "Long", "award": false, "abstract": "Conversational KBQA is about answering a sequence of questions related to a KB. Follow-up questions in conversational KBQA often have missing information referring to entities from the conversation history. In this paper, we propose to model these implied entities, which we refer to as the focal entities of the conversation. We propose a novel graph-based model to capture the transitions of focal entities and apply a graph neural network to derive a probability distribution of focal entities for each question, which is then combined with a standard KBQA module to perform answer ranking. Our experiments on two datasets demonstrate the effectiveness of our proposed method.", "author": "Yunshi Lan; Jing Jiang", "authorids": "/y/yunshi-lan/; /j/jing-jiang/", "bibtex": "@inproceedings{lan-jiang-2021-modeling,\n title = \"Modeling Transitions of Focal Entities for Conversational Knowledge Base Question Answering\",\n author = \"Lan, Yunshi and\n Jiang, Jing\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.255/\",\n doi = \"10.18653/v1/2021.acl-long.255\",\n pages = \"3288--3297\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.255.pdf", "site": "https://aclanthology.org/2021.acl-long.255/", "pdf_size": 654559, "gs_citation": 49, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=9341867980533276630&as_sdt=5,44&sciodt=0,44&hl=en", "gs_version_total": 4, "aff": "Singapore Management University; Singapore Management University", "aff_domain": "smu.edu.sg;smu.edu.sg", "email": "smu.edu.sg;smu.edu.sg", "github": "", "project": "", "author_num": 2, "aff_unique_index": "0;0", "aff_unique_norm": "Singapore Management University", "aff_unique_dep": "", "aff_unique_url": "https://www.smu.edu.sg", "aff_unique_abbr": "SMU", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0;0", "aff_country_unique": "Singapore" }, { "id": "2021.acl-long.17", "title": "Modularized Interaction Network for Named Entity Recognition", "track": "main", "status": "Long", "award": false, "abstract": "Although the existing Named Entity Recognition (NER) models have achieved promising performance, they suffer from certain drawbacks. The sequence labeling-based NER models do not perform well in recognizing long entities as they focus only on word-level information, while the segment-based NER models which focus on processing segment instead of single word are unable to capture the word-level dependencies within the segment. Moreover, as boundary detection and type prediction may cooperate with each other for the NER task, it is also important for the two sub-tasks to mutually reinforce each other by sharing their information. In this paper, we propose a novel Modularized Interaction Network (MIN) model which utilizes both segment-level information and word-level dependencies, and incorporates an interaction mechanism to support information sharing between boundary detection and type prediction to enhance the performance for the NER task. We have conducted extensive experiments based on three NER benchmark datasets. The performance results have shown that the proposed MIN model has outperformed the current state-of-the-art models.", "author": "Fei Li; Zheng Wang; Siu Cheung Hui; Lejian Liao; Dandan Song; Jing Xu; Guoxiu He; Meihuizi Jia", "authorids": "/f/fei-li/; /z/zheng-wang/; /s/siu-cheung-hui/; /l/lejian-liao/; /d/dandan-song/; /j/jing-xu/; /g/guoxiu-he/; /m/meihuizi-jia/", "bibtex": "@inproceedings{li-etal-2021-modularized,\n title = \"Modularized Interaction Network for Named Entity Recognition\",\n author = \"Li, Fei and\n Wang, Zheng and\n Hui, Siu Cheung and\n Liao, Lejian and\n Song, Dandan and\n Xu, Jing and\n He, Guoxiu and\n Jia, Meihuizi\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.17/\",\n doi = \"10.18653/v1/2021.acl-long.17\",\n pages = \"200--209\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.17.pdf", "site": "https://aclanthology.org/2021.acl-long.17/", "pdf_size": 472180, "gs_citation": 40, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=15479265054073002700&as_sdt=5,33&sciodt=0,33&hl=en", "gs_version_total": 6, "aff": "Beijing Institute of Technology, China; Nanyang Technological University, Singapore; Nanyang Technological University, Singapore; Beijing Institute of Technology, China; Beijing Institute of Technology, China; Beijing Institute of Technology, China; Wuhan University, China; Beijing Institute of Technology, China", "aff_domain": "bit.edu.cn;ntu.edu.sg;ntu.edu.sg;bit.edu.cn;bit.edu.cn;bit.edu.cn;whu.edu.cn;bit.edu.cn", "email": "bit.edu.cn;ntu.edu.sg;ntu.edu.sg;bit.edu.cn;bit.edu.cn;bit.edu.cn;whu.edu.cn;bit.edu.cn", "github": "", "project": "", "author_num": 8, "aff_unique_index": "0;1;1;0;0;0;2;0", "aff_unique_norm": "Beijing Institute of Technology;Nanyang Technological University;Wuhan University", "aff_unique_dep": ";;", "aff_unique_url": "http://www.bit.edu.cn/;https://www.ntu.edu.sg;http://www.whu.edu.cn", "aff_unique_abbr": "BIT;NTU;WHU", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0;1;1;0;0;0;0;0", "aff_country_unique": "China;Singapore" }, { "id": "2021.acl-long.94", "title": "More Identifiable yet Equally Performant Transformers for Text Classification", "track": "main", "status": "Long", "award": false, "abstract": "Interpretability is an important aspect of the trustworthiness of a model\u2019s predictions. Transformer\u2019s predictions are widely explained by the attention weights, i.e., a probability distribution generated at its self-attention unit (head). Current empirical studies provide shreds of evidence that attention weights are not explanations by proving that they are not unique. A recent study showed theoretical justifications to this observation by proving the non-identifiability of attention weights. For a given input to a head and its output, if the attention weights generated in it are unique, we call the weights identifiable. In this work, we provide deeper theoretical analysis and empirical observations on the identifiability of attention weights. Ignored in the previous works, we find the attention weights are more identifiable than we currently perceive by uncovering the hidden role of the key vector. However, the weights are still prone to be non-unique attentions that make them unfit for interpretation. To tackle this issue, we provide a variant of the encoder layer that decouples the relationship between key and value vector and provides identifiable weights up to the desired length of the input. We prove the applicability of such variations by providing empirical justifications on varied text classification tasks. The implementations are available at https://github.com/declare-lab/identifiable-transformers.", "author": "Rishabh Bhardwaj; Navonil Majumder; Soujanya Poria; Eduard Hovy", "authorids": "/r/rishabh-bhardwaj/; /n/navonil-majumder/; /s/soujanya-poria/; /e/eduard-hovy/", "bibtex": "@inproceedings{bhardwaj-etal-2021-identifiable,\n title = \"More Identifiable yet Equally Performant Transformers for Text Classification\",\n author = \"Bhardwaj, Rishabh and\n Majumder, Navonil and\n Poria, Soujanya and\n Hovy, Eduard\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.94/\",\n doi = \"10.18653/v1/2021.acl-long.94\",\n pages = \"1172--1182\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.94.pdf", "site": "https://aclanthology.org/2021.acl-long.94/", "pdf_size": 1231387, "gs_citation": 9, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=10556204430436314287&as_sdt=5,44&sciodt=0,44&hl=en", "gs_version_total": 5, "aff": "Singapore University of Technology and Design, Singapore; Singapore University of Technology and Design, Singapore; Singapore University of Technology and Design, Singapore; Carnegie Mellon University, Pittsburgh, PA, USA", "aff_domain": "mymail.sutd.edu.sg;sutd.edu.sg;sutd.edu.sg;cs.cmu.edu", "email": "mymail.sutd.edu.sg;sutd.edu.sg;sutd.edu.sg;cs.cmu.edu", "github": "https://github.com/declare-lab/identifiable-transformers", "project": "", "author_num": 4, "aff_unique_index": "0;0;0;1", "aff_unique_norm": "Singapore University of Technology and Design;Carnegie Mellon University", "aff_unique_dep": ";", "aff_unique_url": "https://www.sutd.edu.sg;https://www.cmu.edu", "aff_unique_abbr": "SUTD;CMU", "aff_campus_unique_index": "1", "aff_campus_unique": ";Pittsburgh", "aff_country_unique_index": "0;0;0;1", "aff_country_unique": "Singapore;United States" }, { "id": "2021.acl-short.70", "title": "More than Text: Multi-modal Chinese Word Segmentation", "track": "main", "status": "Short", "award": false, "abstract": "Chinese word segmentation (CWS) is undoubtedly an important basic task in natural language processing. Previous works only focus on the textual modality, but there are often audio and video utterances (such as news broadcast and face-to-face dialogues), where textual, acoustic and visual modalities normally exist. To this end, we attempt to combine the multi-modality (mainly the converted text and actual voice information) to perform CWS. In this paper, we annotate a new dataset for CWS containing text and audio. Moreover, we propose a time-dependent multi-modal interactive model based on Transformer framework to integrate multi-modal information for word sequence labeling. The experimental results on three different training sets show the effectiveness of our approach with fusing text and audio.", "author": "Dong Zhang; Zheng Hu; Shoushan Li; Hanqian Wu; Qiaoming Zhu; Guodong Zhou", "authorids": "/d/dong-zhang/; /z/zheng-hu/; /s/shoushan-li/; /h/hanqian-wu/; /q/qiaoming-zhu/; /g/guodong-zhou/", "bibtex": "@inproceedings{zhang-etal-2021-text,\n title = \"More than Text: Multi-modal {C}hinese Word Segmentation\",\n author = \"Zhang, Dong and\n Hu, Zheng and\n Li, Shoushan and\n Wu, Hanqian and\n Zhu, Qiaoming and\n Zhou, Guodong\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-short.70/\",\n doi = \"10.18653/v1/2021.acl-short.70\",\n pages = \"550--557\"\n}", "pdf": "https://aclanthology.org/2021.acl-short.70.pdf", "site": "https://aclanthology.org/2021.acl-short.70/", "pdf_size": 401849, "gs_citation": 4, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=15097908751138546647&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 3, "aff": "School of Computer Science and Technology, Soochow University, China; School of Computer Science and Engineering, Southeast University, China; School of Computer Science and Technology, Soochow University, China; School of Computer Science and Engineering, Southeast University, China; School of Computer Science and Technology, Soochow University, China; School of Computer Science and Technology, Soochow University, China", "aff_domain": "suda.edu.cn;seu.edu.cn;suda.edu.cn;seu.edu.cn;suda.edu.cn;suda.edu.cn", "email": "suda.edu.cn;seu.edu.cn;suda.edu.cn;seu.edu.cn;suda.edu.cn;suda.edu.cn", "github": "", "project": "", "author_num": 6, "aff_unique_index": "0;1;0;1;0;0", "aff_unique_norm": "Soochow University;Southeast University", "aff_unique_dep": "School of Computer Science and Technology;School of Computer Science and Engineering", "aff_unique_url": "https://eng.suda.edu.cn/;https://www.seu.edu.cn/", "aff_unique_abbr": "Soochow U;SEU", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0;0;0;0;0;0", "aff_country_unique": "China" }, { "id": "2021.acl-long.453", "title": "MulDA: A Multilingual Data Augmentation Framework for Low-Resource Cross-Lingual NER", "track": "main", "status": "Long", "award": false, "abstract": "Named Entity Recognition (NER) for low-resource languages is a both practical and challenging research problem. This paper addresses zero-shot transfer for cross-lingual NER, especially when the amount of source-language training data is also limited. The paper first proposes a simple but effective labeled sequence translation method to translate source-language training data to target languages and avoids problems such as word order change and entity span determination. With the source-language data as well as the translated data, a generation-based multilingual data augmentation method is introduced to further increase diversity by generating synthetic labeled data in multiple languages. These augmented data enable the language model based NER models to generalize better with both the language-specific features from the target-language synthetic data and the language-independent features from multilingual synthetic data. An extensive set of experiments were conducted to demonstrate encouraging cross-lingual transfer performance of the new research on a wide variety of target languages.", "author": "Linlin Liu; Bosheng Ding; Lidong Bing; Shafiq Joty; Luo Si; Chunyan Miao", "authorids": "/l/linlin-liu/; /b/bosheng-ding/; /l/lidong-bing/; /s/shafiq-joty/; /l/luo-si/; /c/chunyan-miao/", "bibtex": "@inproceedings{liu-etal-2021-mulda,\n title = \"{M}ul{DA}: A Multilingual Data Augmentation Framework for Low-Resource Cross-Lingual {NER}\",\n author = \"Liu, Linlin and\n Ding, Bosheng and\n Bing, Lidong and\n Joty, Shafiq and\n Si, Luo and\n Miao, Chunyan\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.453/\",\n doi = \"10.18653/v1/2021.acl-long.453\",\n pages = \"5834--5846\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.453.pdf", "site": "https://aclanthology.org/2021.acl-long.453/", "pdf_size": 1288024, "gs_citation": 86, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=3957470411679220313&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 3, "aff": "Nanyang Technological University, Singapore+DAMO Academy, Alibaba Group; Nanyang Technological University, Singapore+DAMO Academy, Alibaba Group; DAMO Academy, Alibaba Group; Nanyang Technological University, Singapore; DAMO Academy, Alibaba Group; Nanyang Technological University, Singapore", "aff_domain": "alibaba-inc.com;alibaba-inc.com;alibaba-inc.com;alibaba-inc.com;ntu.edu.sg;ntu.edu.sg", "email": "alibaba-inc.com;alibaba-inc.com;alibaba-inc.com;alibaba-inc.com;ntu.edu.sg;ntu.edu.sg", "github": "", "project": "https://ntunlpsg.github.io/project/mulda/", "author_num": 6, "aff_unique_index": "0+1;0+1;1;0;1;0", "aff_unique_norm": "Nanyang Technological University;Alibaba Group", "aff_unique_dep": ";DAMO Academy", "aff_unique_url": "https://www.ntu.edu.sg;https://www.alibaba-group.com", "aff_unique_abbr": "NTU;Alibaba", "aff_campus_unique_index": ";", "aff_campus_unique": "", "aff_country_unique_index": "0+1;0+1;1;0;1;0", "aff_country_unique": "Singapore;China" }, { "id": "2021.acl-long.23", "title": "Multi-Head Highly Parallelized LSTM Decoder for Neural Machine Translation", "track": "main", "status": "Long", "award": false, "abstract": "One of the reasons Transformer translation models are popular is that self-attention networks for context modelling can be easily parallelized at sequence level. However, the computational complexity of a self-attention network is O(n2), increasing quadratically with sequence length. By contrast, the complexity of LSTM-based approaches is only O(n). In practice, however, LSTMs are much slower to train than self-attention networks as they cannot be parallelized at sequence level: to model context, the current LSTM state relies on the full LSTM computation of the preceding state. This has to be computed n times for a sequence of length n. The linear transformations involved in the LSTM gate and state computations are the major cost factors in this. To enable sequence-level parallelization of LSTMs, we approximate full LSTM context modelling by computing hidden states and gates with the current input and a simple bag-of-words representation of the preceding tokens context. This allows us to compute each input step efficiently in parallel, avoiding the formerly costly sequential linear transformations. We then connect the outputs of each parallel step with computationally cheap element-wise computations. We call this the Highly Parallelized LSTM. To further constrain the number of LSTM parameters, we compute several small HPLSTMs in parallel like multi-head attention in the Transformer. The experiments show that our MHPLSTM decoder achieves significant BLEU improvements, while being even slightly faster than the self-attention network in training, and much faster than the standard LSTM.", "author": "Hongfei Xu; Qiuhui Liu; Josef van Genabith; Deyi Xiong; Meng Zhang", "authorids": "/h/hongfei-xu/; /q/qiuhui-liu/; /j/josef-van-genabith/; /d/deyi-xiong/; /m/meng-zhang/", "bibtex": "@inproceedings{xu-etal-2021-multi,\n title = \"Multi-Head Highly Parallelized {LSTM} Decoder for Neural Machine Translation\",\n author = \"Xu, Hongfei and\n Liu, Qiuhui and\n van Genabith, Josef and\n Xiong, Deyi and\n Zhang, Meng\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.23/\",\n doi = \"10.18653/v1/2021.acl-long.23\",\n pages = \"273--282\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.23.pdf", "site": "https://aclanthology.org/2021.acl-long.23/", "pdf_size": 563613, "gs_citation": 15, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=11845371590907546243&as_sdt=5,44&sciodt=0,44&hl=en", "gs_version_total": 3, "aff": "DFKI and Saarland University, Informatics Campus, Saarland, Germany; China Mobile Online Services, Henan, China; DFKI and Saarland University, Informatics Campus, Saarland, Germany + Tianjin University, Tianjin, China + Global Tone Communication Technology Co., Ltd.; Tianjin University, Tianjin, China + Global Tone Communication Technology Co., Ltd.; Huawei Noah\u2019s Ark Lab", "aff_domain": "foxmail.com;foxmail.com;dfki.de;tju.edu.cn;huawei.com", "email": "foxmail.com;foxmail.com;dfki.de;tju.edu.cn;huawei.com", "github": "", "project": "", "author_num": 5, "aff_unique_index": "0;1;0+2+3;2+3;4", "aff_unique_norm": "DFKI;China Mobile Online Services;Tianjin University;Global Tone Communication Technology;Huawei", "aff_unique_dep": "Informatics;;;;Noah\u2019s Ark Lab", "aff_unique_url": "https://www.dFKI.de;;http://www.tju.edu.cn;;https://www.huawei.com", "aff_unique_abbr": "DFKI;;Tianjin U;;Huawei", "aff_campus_unique_index": "0;0+2;2", "aff_campus_unique": "Saarland;;Tianjin", "aff_country_unique_index": "0;1;0+1+1;1+1;1", "aff_country_unique": "Germany;China" }, { "id": "2021.acl-long.495", "title": "Multi-Label Few-Shot Learning for Aspect Category Detection", "track": "main", "status": "Long", "award": false, "abstract": "Aspect category detection (ACD) in sentiment analysis aims to identify the aspect categories mentioned in a sentence. In this paper, we formulate ACD in the few-shot learning scenario. However, existing few-shot learning approaches mainly focus on single-label predictions. These methods can not work well for the ACD task since a sentence may contain multiple aspect categories. Therefore, we propose a multi-label few-shot learning method based on the prototypical network. To alleviate the noise, we design two effective attention mechanisms. The support-set attention aims to extract better prototypes by removing irrelevant aspects. The query-set attention computes multiple prototype-specific representations for each query instance, which are then used to compute accurate distances with the corresponding prototypes. To achieve multi-label inference, we further learn a dynamic threshold per instance by a policy network. Extensive experimental results on three datasets demonstrate that the proposed method significantly outperforms strong baselines.", "author": "Mengting Hu; Shiwan Zhao; Honglei Guo; Chao Xue; Hang Gao; Tiegang Gao; Renhong Cheng; Zhong Su", "authorids": "/m/mengting-hu/; /s/shiwan-zhao/; /h/honglei-guo/; /c/chao-xue/; /h/hang-gao/; /t/tiegang-gao/; /r/renhong-cheng/; /z/zhong-su/", "bibtex": "@inproceedings{hu-etal-2021-multi-label,\n title = \"Multi-Label Few-Shot Learning for Aspect Category Detection\",\n author = \"Hu, Mengting and\n Zhao, Shiwan and\n Guo, Honglei and\n Xue, Chao and\n Gao, Hang and\n Gao, Tiegang and\n Cheng, Renhong and\n Su, Zhong\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.495/\",\n doi = \"10.18653/v1/2021.acl-long.495\",\n pages = \"6330--6340\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.495.pdf", "site": "https://aclanthology.org/2021.acl-long.495/", "pdf_size": 1327733, "gs_citation": 52, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=6621240521019377603&as_sdt=5,48&sciodt=0,48&hl=en", "gs_version_total": 7, "aff": "Nankai University; IBM Research - China; JD Explore Academy; IBM Research - China; Nankai University; Nankai University; Nankai University; IBM Research - China", "aff_domain": "mail.nankai.edu.cn;cn.ibm.com;cn.ibm.com;jd.com;nankai.edu.cn;nankai.edu.cn;mail.nankai.edu.cn;cn.ibm.com", "email": "mail.nankai.edu.cn;cn.ibm.com;cn.ibm.com;jd.com;nankai.edu.cn;nankai.edu.cn;mail.nankai.edu.cn;cn.ibm.com", "github": "", "project": "", "author_num": 8, "aff_unique_index": "0;1;2;1;0;0;0;1", "aff_unique_norm": "Nankai University;IBM Research;JD Explore Academy", "aff_unique_dep": ";Research;", "aff_unique_url": "http://www.nankai.edu.cn;https://www.ibm.com/research;", "aff_unique_abbr": "NKU;IBM;", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0;0;0;0;0;0;0", "aff_country_unique": "China;" }, { "id": "2021.acl-short.122", "title": "Multi-Scale Progressive Attention Network for Video Question Answering", "track": "main", "status": "Short", "award": false, "abstract": "Understanding the multi-scale visual information in a video is essential for Video Question Answering (VideoQA). Therefore, we propose a novel Multi-Scale Progressive Attention Network (MSPAN) to achieve relational reasoning between cross-scale video information. We construct clips of different lengths to represent different scales of the video. Then, the clip-level features are aggregated into node features by using max-pool, and a graph is generated for each scale of clips. For cross-scale feature interaction, we design a message passing strategy between adjacent scale graphs, i.e., top-down scale interaction and bottom-up scale interaction. Under the question\u2019s guidance of progressive attention, we realize the fusion of all-scale video features. Experimental evaluations on three benchmarks: TGIF-QA, MSVD-QA and MSRVTT-QA show our method has achieved state-of-the-art performance.", "author": "Zhicheng Guo; Jiaxuan Zhao; Licheng Jiao; Xu Liu; Lingling Li", "authorids": "/z/zhicheng-guo-xidian/; /j/jiaxuan-zhao/; /l/licheng-jiao/; /x/xu-liu/; /l/lingling-li/", "bibtex": "@inproceedings{guo-etal-2021-multi,\n title = \"Multi-Scale Progressive Attention Network for Video Question Answering\",\n author = \"Guo, Zhicheng and\n Zhao, Jiaxuan and\n Jiao, Licheng and\n Liu, Xu and\n Li, Lingling\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-short.122/\",\n doi = \"10.18653/v1/2021.acl-short.122\",\n pages = \"973--978\"\n}", "pdf": "https://aclanthology.org/2021.acl-short.122.pdf", "site": "https://aclanthology.org/2021.acl-short.122/", "pdf_size": 636391, "gs_citation": 23, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=9882749115594344148&as_sdt=5,44&sciodt=0,44&hl=en", "gs_version_total": 3, "aff": "School of Artificial Intelligence, Xidian University, Xi\u2019an, Shaanxi Province, 710071, China; School of Artificial Intelligence, Xidian University, Xi\u2019an, Shaanxi Province, 710071, China; School of Artificial Intelligence, Xidian University, Xi\u2019an, Shaanxi Province, 710071, China; School of Artificial Intelligence, Xidian University, Xi\u2019an, Shaanxi Province, 710071, China; School of Artificial Intelligence, Xidian University, Xi\u2019an, Shaanxi Province, 710071, China", "aff_domain": "stu.xidian.edu.cn;stu.xidian.edu.cn;mail.xidian.edu.cn;xidian.edu.cn;xidian.edu.cn", "email": "stu.xidian.edu.cn;stu.xidian.edu.cn;mail.xidian.edu.cn;xidian.edu.cn;xidian.edu.cn", "github": "", "project": "", "author_num": 5, "aff_unique_index": "0;0;0;0;0", "aff_unique_norm": "Xidian University", "aff_unique_dep": "School of Artificial Intelligence", "aff_unique_url": "http://www.xidian.edu.cn", "aff_unique_abbr": "Xidian", "aff_campus_unique_index": "0;0;0;0;0", "aff_campus_unique": "Xi'an", "aff_country_unique_index": "0;0;0;0;0", "aff_country_unique": "China" }, { "id": "2021.acl-long.89", "title": "Multi-Task Retrieval for Knowledge-Intensive Tasks", "track": "main", "status": "Long", "award": false, "abstract": "Retrieving relevant contexts from a large corpus is a crucial step for tasks such as open-domain question answering and fact checking. Although neural retrieval outperforms traditional methods like tf-idf and BM25, its performance degrades considerably when applied to out-of-domain data. Driven by the question of whether a neural retrieval model can be _universal_ and perform robustly on a wide variety of problems, we propose a multi-task trained model. Our approach not only outperforms previous methods in the few-shot setting, but also rivals specialised neural retrievers, even when in-domain training data is abundant. With the help of our retriever, we improve existing models for downstream tasks and closely match or improve the state of the art on multiple benchmarks.", "author": "Jean Maillard; Vladimir Karpukhin; Fabio Petroni; Wen-tau Yih; Barlas Oguz; Veselin Stoyanov; Gargi Ghosh", "authorids": "/j/jean-maillard/; /v/vladimir-karpukhin/; /f/fabio-petroni/; /w/wen-tau-yih/; /b/barlas-oguz/; /v/veselin-stoyanov/; /g/gargi-ghosh/", "bibtex": "@inproceedings{maillard-etal-2021-multi,\n title = \"Multi-Task Retrieval for Knowledge-Intensive Tasks\",\n author = \"Maillard, Jean and\n Karpukhin, Vladimir and\n Petroni, Fabio and\n Yih, Wen-tau and\n Oguz, Barlas and\n Stoyanov, Veselin and\n Ghosh, Gargi\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.89/\",\n doi = \"10.18653/v1/2021.acl-long.89\",\n pages = \"1098--1111\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.89.pdf", "site": "https://aclanthology.org/2021.acl-long.89/", "pdf_size": 3925374, "gs_citation": 65, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=3084837804935330080&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 4, "aff": "Facebook AI; Facebook AI; Facebook AI; Facebook AI; Facebook AI; Facebook AI; Facebook AI", "aff_domain": "fb.com;fb.com;fb.com;fb.com;fb.com;fb.com;fb.com", "email": "fb.com;fb.com;fb.com;fb.com;fb.com;fb.com;fb.com", "github": "", "project": "", "author_num": 7, "aff_unique_index": "0;0;0;0;0;0;0", "aff_unique_norm": "Facebook", "aff_unique_dep": "Facebook AI", "aff_unique_url": "https://www.facebook.com", "aff_unique_abbr": "Facebook AI", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0;0;0;0;0;0;0", "aff_country_unique": "United States" }, { "id": "2021.acl-long.32", "title": "Multi-TimeLine Summarization (MTLS): Improving Timeline Summarization by Generating Multiple Summaries", "track": "main", "status": "Long", "award": false, "abstract": "In this paper, we address a novel task, Multiple TimeLine Summarization (MTLS), which extends the flexibility and versatility of Time-Line Summarization (TLS). Given any collection of time-stamped news articles, MTLS automatically discovers important yet different stories and generates a corresponding time-line for each story. To achieve this, we propose a novel unsupervised summarization framework based on two-stage affinity propagation. We also introduce a quantitative evaluation measure for MTLS based on previousTLS evaluation methods. Experimental results show that our MTLS framework demonstrates high effectiveness and MTLS task can give bet-ter results than TLS.", "author": "Yi Yu; Adam Jatowt; Antoine Doucet; Kazunari Sugiyama; Masatoshi Yoshikawa", "authorids": "/y/yi-yu/; /a/adam-jatowt/; /a/antoine-doucet/; /k/kazunari-sugiyama/; /m/masatoshi-yoshikawa/", "bibtex": "@inproceedings{yu-etal-2021-multi,\n title = \"Multi-{T}ime{L}ine Summarization ({MTLS}): Improving Timeline Summarization by Generating Multiple Summaries\",\n author = \"Yu, Yi and\n Jatowt, Adam and\n Doucet, Antoine and\n Sugiyama, Kazunari and\n Yoshikawa, Masatoshi\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.32/\",\n doi = \"10.18653/v1/2021.acl-long.32\",\n pages = \"377--387\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.32.pdf", "site": "https://aclanthology.org/2021.acl-long.32/", "pdf_size": 2352200, "gs_citation": 42, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=17497697837913527112&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 3, "aff": "Kyoto University, Japan; University of Innsbruck, Austria; University of La Rochelle, France; Kyoto University, Japan; Kyoto University, Japan", "aff_domain": "db.soc.i.kyoto-u.ac.jp;uibk.ac.at;univ-lr.fr;i.kyoto-u.ac.jp;i.kyoto-u.ac.jp", "email": "db.soc.i.kyoto-u.ac.jp;uibk.ac.at;univ-lr.fr;i.kyoto-u.ac.jp;i.kyoto-u.ac.jp", "github": "", "project": "", "author_num": 5, "aff_unique_index": "0;1;2;0;0", "aff_unique_norm": "Kyoto University;University of Innsbruck;University of La Rochelle", "aff_unique_dep": ";;", "aff_unique_url": "https://www.kyoto-u.ac.jp;https://www.uibk.ac.at;https://www.univ-larochelle.fr", "aff_unique_abbr": "Kyoto U;UIBK;ULR", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0;1;2;0;0", "aff_country_unique": "Japan;Austria;France" }, { "id": "2021.acl-long.207", "title": "Multi-View Cross-Lingual Structured Prediction with Minimum Supervision", "track": "main", "status": "Long", "award": false, "abstract": "In structured prediction problems, cross-lingual transfer learning is an efficient way to train quality models for low-resource languages, and further improvement can be obtained by learning from multiple source languages. However, not all source models are created equal and some may hurt performance on the target language. Previous work has explored the similarity between source and target sentences as an approximate measure of strength for different source models. In this paper, we propose a multi-view framework, by leveraging a small number of labeled target sentences, to effectively combine multiple source models into an aggregated source view at different granularity levels (language, sentence, or sub-structure), and transfer it to a target view based on a task-specific model. By encouraging the two views to interact with each other, our framework can dynamically adjust the confidence level of each source model and improve the performance of both views during training. Experiments for three structured prediction tasks on sixteen data sets show that our framework achieves significant improvement over all existing approaches, including these with access to additional source language data.", "author": "Zechuan Hu; Yong Jiang; Nguyen Bach; Tao Wang; Zhongqiang Huang; Fei Huang; Kewei Tu", "authorids": "/z/zechuan-hu/; /y/yong-jiang/; /n/nguyen-bach/; /t/tao-wang/; /z/zhongqiang-huang/; /f/fei-huang/; /k/kewei-tu/", "bibtex": "@inproceedings{hu-etal-2021-multi,\n title = \"Multi-View Cross-Lingual Structured Prediction with Minimum Supervision\",\n author = \"Hu, Zechuan and\n Jiang, Yong and\n Bach, Nguyen and\n Wang, Tao and\n Huang, Zhongqiang and\n Huang, Fei and\n Tu, Kewei\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.207/\",\n doi = \"10.18653/v1/2021.acl-long.207\",\n pages = \"2661--2674\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.207.pdf", "site": "https://aclanthology.org/2021.acl-long.207/", "pdf_size": 1382609, "gs_citation": 7, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=4951982788537227925&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 5, "aff": "School of Information Science and Technology, ShanghaiTech University + Shanghai Engineering Research Center of Intelligent Vision and Imaging + Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences + University of Chinese Academy of Sciences; DAMO Academy, Alibaba Group; DAMO Academy, Alibaba Group; DAMO Academy, Alibaba Group; DAMO Academy, Alibaba Group; DAMO Academy, Alibaba Group; School of Information Science and Technology, ShanghaiTech University + Shanghai Engineering Research Center of Intelligent Vision and Imaging + Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences + University of Chinese Academy of Sciences", "aff_domain": "shanghaitech.edu.cn;alibaba-inc.com; ; ; ; ;shanghaitech.edu.cn", "email": "shanghaitech.edu.cn;alibaba-inc.com; ; ; ; ;shanghaitech.edu.cn", "github": "", "project": "", "author_num": 7, "aff_unique_index": "0+1+2+3;4;4;4;4;4;0+1+2+3", "aff_unique_norm": "ShanghaiTech University;Shanghai Engineering Research Center of Intelligent Vision and Imaging;Shanghai Institute of Microsystem and Information Technology;University of Chinese Academy of Sciences;Alibaba Group", "aff_unique_dep": "School of Information Science and Technology;;;;DAMO Academy", "aff_unique_url": "https://www.shanghaitech.edu.cn;;http://www.sim.cas.cn;http://www.ucas.ac.cn;https://www.alibaba-group.com", "aff_unique_abbr": "ShanghaiTech;;SIM;UCAS;Alibaba", "aff_campus_unique_index": "0+0;0+0", "aff_campus_unique": "Shanghai;", "aff_country_unique_index": "0+0+0+0;0;0;0;0;0;0+0+0+0", "aff_country_unique": "China" }, { "id": "2021.acl-long.513", "title": "Multi-hop Graph Convolutional Network with High-order Chebyshev Approximation for Text Reasoning", "track": "main", "status": "Long", "award": false, "abstract": "Graph convolutional network (GCN) has become popular in various natural language processing (NLP) tasks with its superiority in long-term and non-consecutive word interactions. However, existing single-hop graph reasoning in GCN may miss some important non-consecutive dependencies. In this study, we define the spectral graph convolutional network with the high-order dynamic Chebyshev approximation (HDGCN), which augments the multi-hop graph reasoning by fusing messages aggregated from direct and long-term dependencies into one convolutional layer. To alleviate the over-smoothing in high-order Chebyshev approximation, a multi-vote-based cross-attention (MVCAttn) with linear computation complexity is also proposed. The empirical results on four transductive and inductive NLP tasks and the ablation study verify the efficacy of the proposed model.", "author": "Shuoran Jiang; Qingcai Chen; Xin Liu; Baotian Hu; Lisai Zhang", "authorids": "/s/shuoran-jiang/; /q/qingcai-chen/; /x/xin-liu/; /b/baotian-hu/; /l/lisai-zhang/", "bibtex": "@inproceedings{jiang-etal-2021-multi,\n title = \"Multi-hop Graph Convolutional Network with High-order Chebyshev Approximation for Text Reasoning\",\n author = \"Jiang, Shuoran and\n Chen, Qingcai and\n Liu, Xin and\n Hu, Baotian and\n Zhang, Lisai\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.513/\",\n doi = \"10.18653/v1/2021.acl-long.513\",\n pages = \"6563--6573\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.513.pdf", "site": "https://aclanthology.org/2021.acl-long.513/", "pdf_size": 8685473, "gs_citation": 7, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=448354062254464943&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 4, "aff": "Harbin Institute of Technology, Shenzhen; Harbin Institute of Technology, Shenzhen; Harbin Institute of Technology, Shenzhen; Harbin Institute of Technology, Shenzhen; Harbin Institute of Technology, Shenzhen", "aff_domain": "gmail.com;hit.edu.cn;gmail.com;hit.edu.cn;gmail.com", "email": "gmail.com;hit.edu.cn;gmail.com;hit.edu.cn;gmail.com", "github": "https://github.com/MathIsAll/HDGCN-pytorch", "project": "", "author_num": 5, "aff_unique_index": "0;0;0;0;0", "aff_unique_norm": "Harbin Institute of Technology", "aff_unique_dep": "", "aff_unique_url": "http://en.hhit.edu.cn/", "aff_unique_abbr": "HIT", "aff_campus_unique_index": "0;0;0;0;0", "aff_campus_unique": "Shenzhen", "aff_country_unique_index": "0;0;0;0;0", "aff_country_unique": "China" }, { "id": "2021.acl-long.461", "title": "Multi-perspective Coherent Reasoning for Helpfulness Prediction of Multimodal Reviews", "track": "main", "status": "Long", "award": false, "abstract": "As more and more product reviews are posted in both text and images, Multimodal Review Analysis (MRA) becomes an attractive research topic. Among the existing review analysis tasks, helpfulness prediction on review text has become predominant due to its importance for e-commerce platforms and online shops, i.e. helping customers quickly acquire useful product information. This paper proposes a new task Multimodal Review Helpfulness Prediction (MRHP) aiming to analyze the review helpfulness from text and visual modalities. Meanwhile, a novel Multi-perspective Coherent Reasoning method (MCR) is proposed to solve the MRHP task, which conducts joint reasoning over texts and images from both the product and the review, and aggregates the signals to predict the review helpfulness. Concretely, we first propose a product-review coherent reasoning module to measure the intra- and inter-modal coherence between the target product and the review. In addition, we also devise an intra-review coherent reasoning module to identify the coherence between the text content and images of the review, which is a piece of strong evidence for review helpfulness prediction. To evaluate the effectiveness of MCR, we present two newly collected multimodal review datasets as benchmark evaluation resources for the MRHP task. Experimental results show that our MCR method can lead to a performance increase of up to 8.5% as compared to the best performing text-only model. The source code and datasets can be obtained from https://github.com/jhliu17/MCR.", "author": "Junhao Liu; Zhen Hai; Min Yang; Lidong Bing", "authorids": "/j/junhao-liu/; /z/zhen-hai/; /m/min-yang/; /l/lidong-bing/", "bibtex": "@inproceedings{liu-etal-2021-multi-perspective,\n title = \"Multi-perspective Coherent Reasoning for Helpfulness Prediction of Multimodal Reviews\",\n author = \"Liu, Junhao and\n Hai, Zhen and\n Yang, Min and\n Bing, Lidong\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.461/\",\n doi = \"10.18653/v1/2021.acl-long.461\",\n pages = \"5927--5936\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.461.pdf", "site": "https://aclanthology.org/2021.acl-long.461/", "pdf_size": 3471387, "gs_citation": 23, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=3672180034254181277&as_sdt=5,33&sciodt=0,33&hl=en", "gs_version_total": 2, "aff": "Shenzhen Key Laboratory for High Performance Data Mining, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences+University of Chinese Academy of Sciences+DAMO Academy, Alibaba Group; DAMO Academy, Alibaba Group; Shenzhen Key Laboratory for High Performance Data Mining, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences; DAMO Academy, Alibaba Group", "aff_domain": "siat.ac.cn;alibaba-inc.com;siat.ac.cn;alibaba-inc.com", "email": "siat.ac.cn;alibaba-inc.com;siat.ac.cn;alibaba-inc.com", "github": "https://github.com/jhliu17/MCR", "project": "", "author_num": 4, "aff_unique_index": "0+1+2;2;0;2", "aff_unique_norm": "Chinese Academy of Sciences;University of Chinese Academy of Sciences;Alibaba Group", "aff_unique_dep": "Shenzhen Institute of Advanced Technology;;DAMO Academy", "aff_unique_url": "http://www.siat.ac.cn;http://www.ucas.ac.cn;https://www.alibaba-group.com", "aff_unique_abbr": "CAS;UCAS;Alibaba", "aff_campus_unique_index": "0;0", "aff_campus_unique": "Shenzhen;", "aff_country_unique_index": "0+0+0;0;0;0", "aff_country_unique": "China" }, { "id": "2021.acl-long.199", "title": "Multi-stage Pre-training over Simplified Multimodal Pre-training Models", "track": "main", "status": "Long", "award": false, "abstract": "Multimodal pre-training models, such as LXMERT, have achieved excellent results in downstream tasks. However, current pre-trained models require large amounts of training data and have huge model sizes, which make them impossible to apply in low-resource situations. How to obtain similar or even better performance than a larger model under the premise of less pre-training data and smaller model size has become an important problem. In this paper, we propose a new Multi-stage Pre-training (MSP) method, which uses information at different granularities from word, phrase to sentence in both texts and images to pre-train a model in stages. We also design several different pre-training tasks suitable for the information granularity in different stage in order to efficiently capture the diverse knowledge from a limited corpus. We take a Simplified LXMERT (LXMERT-S) which is with 45.9% parameters of the original LXMERT model and only 11.44% of the original pre-training data as the testbed of our MSP method. Experimental results show that our method achieves comparable performance to the original LXMERT model in all downstream tasks, and even outperforms the original model in Image-Text Retrieval task.", "author": "Tongtong Liu; Fangxiang Feng; Xiaojie Wang", "authorids": "/t/tongtong-liu/; /f/fangxiang-feng/; /x/xiaojie-wang/", "bibtex": "@inproceedings{liu-etal-2021-multi,\n title = \"Multi-stage Pre-training over Simplified Multimodal Pre-training Models\",\n author = \"Liu, Tongtong and\n Feng, Fangxiang and\n Wang, Xiaojie\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.199/\",\n doi = \"10.18653/v1/2021.acl-long.199\",\n pages = \"2556--2565\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.199.pdf", "site": "https://aclanthology.org/2021.acl-long.199/", "pdf_size": 1290589, "gs_citation": 13, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=7942884641292008169&as_sdt=5,31&sciodt=0,31&hl=en", "gs_version_total": 4, "aff": "Beijing University of Posts and Telecommunications; Beijing University of Posts and Telecommunications; Beijing University of Posts and Telecommunications", "aff_domain": "bupt.edu.cn;bupt.edu.cn;bupt.edu.cn", "email": "bupt.edu.cn;bupt.edu.cn;bupt.edu.cn", "github": "", "project": "", "author_num": 3, "aff_unique_index": "0;0;0", "aff_unique_norm": "Beijing University of Posts and Telecommunications", "aff_unique_dep": "", "aff_unique_url": "http://www.bupt.edu.cn/", "aff_unique_abbr": "BUPT", "aff_campus_unique_index": "0;0;0", "aff_campus_unique": "Beijing", "aff_country_unique_index": "0;0;0", "aff_country_unique": "China" }, { "id": "2021.acl-long.249", "title": "MultiMET: A Multimodal Dataset for Metaphor Understanding", "track": "main", "status": "Long", "award": false, "abstract": "Metaphor involves not only a linguistic phenomenon, but also a cognitive phenomenon structuring human thought, which makes understanding it challenging. As a means of cognition, metaphor is rendered by more than texts alone, and multimodal information in which vision/audio content is integrated with the text can play an important role in expressing and understanding metaphor. However, previous metaphor processing and understanding has focused on texts, partly due to the unavailability of large-scale datasets with ground truth labels of multimodal metaphor. In this paper, we introduce MultiMET, a novel multimodal metaphor dataset to facilitate understanding metaphorical information from multimodal text and image. It contains 10,437 text-image pairs from a range of sources with multimodal annotations of the occurrence of metaphors, domain relations, sentiments metaphors convey, and author intents. MultiMET opens the door to automatic metaphor understanding by investigating multimodal cues and their interplay. Moreover, we propose a range of strong baselines and show the importance of combining multimodal cues for metaphor understanding. MultiMET will be released publicly for research.", "author": "Dongyu Zhang; Minghao Zhang; Heting Zhang; Liang Yang; Hongfei Lin", "authorids": "/d/dongyu-zhang/; /m/minghao-zhang/; /h/heting-zhang/; /l/liang-yang/; /h/hongfei-lin/", "bibtex": "@inproceedings{zhang-etal-2021-multimet,\n title = \"{M}ulti{MET}: A Multimodal Dataset for Metaphor Understanding\",\n author = \"Zhang, Dongyu and\n Zhang, Minghao and\n Zhang, Heting and\n Yang, Liang and\n Lin, Hongfei\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.249/\",\n doi = \"10.18653/v1/2021.acl-long.249\",\n pages = \"3214--3225\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.249.pdf", "site": "https://aclanthology.org/2021.acl-long.249/", "pdf_size": 1039807, "gs_citation": 56, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=3367374376532874950&as_sdt=5,44&sciodt=0,44&hl=en", "gs_version_total": 4, "aff": "Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province, School of Software, Dalian University of Technology, China; Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province, School of Software, Dalian University of Technology, China; Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province, School of Software, Dalian University of Technology, China; School of Computer Science and Technology, Dalian University of Technology, China; School of Computer Science and Technology, Dalian University of Technology, China", "aff_domain": "; ; ; ;dlut.edu.cn", "email": "; ; ; ;dlut.edu.cn", "github": "", "project": "", "author_num": 5, "aff_unique_index": "0;0;0;0;0", "aff_unique_norm": "Dalian University of Technology", "aff_unique_dep": "School of Software", "aff_unique_url": "http://www.dlut.edu.cn/", "aff_unique_abbr": "DUT", "aff_campus_unique_index": "1;1", "aff_campus_unique": ";Dalian", "aff_country_unique_index": "0;0;0;0;0", "aff_country_unique": "China" }, { "id": "2021.acl-short.31", "title": "Multilingual Agreement for Multilingual Neural Machine Translation", "track": "main", "status": "Short", "award": false, "abstract": "Although multilingual neural machine translation (MNMT) enables multiple language translations, the training process is based on independent multilingual objectives. Most multilingual models can not explicitly exploit different language pairs to assist each other, ignoring the relationships among them. In this work, we propose a novel agreement-based method to encourage multilingual agreement among different translation directions, which minimizes the differences among them. We combine the multilingual training objectives with the agreement term by randomly substituting some fragments of the source language with their counterpart translations of auxiliary languages. To examine the effectiveness of our method, we conduct experiments on the multilingual translation task of 10 language pairs. Experimental results show that our method achieves significant improvements over the previous multilingual baselines.", "author": "Jian Yang; Yuwei Yin; Shuming Ma; Haoyang Huang; Dongdong Zhang; Zhoujun Li; Furu Wei", "authorids": "/j/jian-yang/; /y/yuwei-yin/; /s/shuming-ma/; /h/haoyang-huang/; /d/dongdong-zhang/; /z/zhoujun-li/; /f/furu-wei/", "bibtex": "@inproceedings{yang-etal-2021-multilingual,\n title = \"Multilingual Agreement for Multilingual Neural Machine Translation\",\n author = \"Yang, Jian and\n Yin, Yuwei and\n Ma, Shuming and\n Huang, Haoyang and\n Zhang, Dongdong and\n Li, Zhoujun and\n Wei, Furu\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-short.31/\",\n doi = \"10.18653/v1/2021.acl-short.31\",\n pages = \"233--239\"\n}", "pdf": "https://aclanthology.org/2021.acl-short.31.pdf", "site": "https://aclanthology.org/2021.acl-short.31/", "pdf_size": 3499562, "gs_citation": 29, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=14049069166559122178&as_sdt=5,33&sciodt=0,33&hl=en", "gs_version_total": 4, "aff": "State Key Lab of Software Development Environment, Beihang University; State Key Lab of Software Development Environment, Beihang University; Microsoft Research Asia; Microsoft Research Asia; Microsoft Research Asia; State Key Lab of Software Development Environment, Beihang University; Microsoft Research Asia", "aff_domain": "buaa.edu.cn;buaa.edu.cn;microsoft.com;microsoft.com;microsoft.com;buaa.edu.cn;microsoft.com", "email": "buaa.edu.cn;buaa.edu.cn;microsoft.com;microsoft.com;microsoft.com;buaa.edu.cn;microsoft.com", "github": "", "project": "", "author_num": 7, "aff_unique_index": "0;0;1;1;1;0;1", "aff_unique_norm": "Beihang University;Microsoft Research", "aff_unique_dep": "State Key Lab of Software Development Environment;Research", "aff_unique_url": "http://www.buaa.edu.cn;https://www.microsoft.com/en-us/research/group/asia", "aff_unique_abbr": "Beihang;MSR Asia", "aff_campus_unique_index": "1;1;1;1", "aff_campus_unique": ";Asia", "aff_country_unique_index": "0;0;0;0;0;0;0", "aff_country_unique": "China" }, { "id": "2021.acl-long.68", "title": "Multilingual Speech Translation from Efficient Finetuning of Pretrained Models", "track": "main", "status": "Long", "award": false, "abstract": "We present a simple yet effective approach to build multilingual speech-to-text (ST) translation through efficient transfer learning from a pretrained speech encoder and text decoder. Our key finding is that a minimalistic LNA (LayerNorm and Attention) finetuning can achieve zero-shot crosslingual and cross-modality transfer ability by only finetuning 10 50% of the pretrained parameters. This effectively leverages large pretrained models at low training cost such as wav2vec 2.0 for acoustic modeling, and mBART for multilingual text generation. This sets a new state-of-the-art for 36 translation directions (and surpassing cascaded ST for 26 of them) on the large-scale multilingual ST benchmark CoVoST 2 (+6.4 BLEU on average for En-X directions and +6.7 BLEU for X-En directions). Our approach demonstrates strong zero-shot performance in a many-to-many multilingual model (+5.6 BLEU on average across 28 non-English directions), making it an appealing approach for attaining high-quality speech translation with improved parameter and data efficiency.", "author": "Xian Li; Changhan Wang; Yun Tang; Chau Tran; Yuqing Tang; Juan Pino; Alexei Baevski; Alexis Conneau; Michael Auli", "authorids": "/x/xian-li/; /c/changhan-wang/; /y/yun-tang/; /c/chau-tran/; /y/yuqing-tang/; /j/juan-pino/; /a/alexei-baevski/; /a/alexis-conneau/; /m/michael-auli/", "bibtex": "@inproceedings{li-etal-2021-multilingual,\n title = \"Multilingual Speech Translation from Efficient Finetuning of Pretrained Models\",\n author = \"Li, Xian and\n Wang, Changhan and\n Tang, Yun and\n Tran, Chau and\n Tang, Yuqing and\n Pino, Juan and\n Baevski, Alexei and\n Conneau, Alexis and\n Auli, Michael\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.68/\",\n doi = \"10.18653/v1/2021.acl-long.68\",\n pages = \"827--838\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.68.pdf", "site": "https://aclanthology.org/2021.acl-long.68/", "pdf_size": 406814, "gs_citation": 151, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=5463269314521359781&as_sdt=5,44&sciodt=0,44&hl=en", "gs_version_total": 6, "aff": "Facebook AI; Facebook AI; Facebook AI; Facebook AI; Facebook AI; Facebook AI; Facebook AI; Facebook AI; Facebook AI", "aff_domain": "fb.com;fb.com;fb.com;fb.com;fb.com;fb.com;fb.com;fb.com;fb.com", "email": "fb.com;fb.com;fb.com;fb.com;fb.com;fb.com;fb.com;fb.com;fb.com", "github": "", "project": "", "author_num": 9, "aff_unique_index": "0;0;0;0;0;0;0;0;0", "aff_unique_norm": "Facebook", "aff_unique_dep": "Facebook AI", "aff_unique_url": "https://www.facebook.com", "aff_unique_abbr": "Facebook AI", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0;0;0;0;0;0;0;0;0", "aff_country_unique": "United States" }, { "id": "2021.acl-long.526", "title": "Multimodal Multi-Speaker Merger & Acquisition Financial Modeling: A New Task, Dataset, and Neural Baselines", "track": "main", "status": "Long", "award": false, "abstract": "Risk prediction is an essential task in financial markets. Merger and Acquisition (M&A) calls provide key insights into the claims made by company executives about the restructuring of the financial firms. Extracting vocal and textual cues from M&A calls can help model the risk associated with such financial activities. To aid the analysis of M&A calls, we curate a dataset of conference call transcripts and their corresponding audio recordings for the time period ranging from 2016 to 2020. We introduce M3ANet, a baseline architecture that takes advantage of the multimodal multi-speaker input to forecast the financial risk associated with the M&A calls. Empirical results prove that the task is challenging, with the pro-posed architecture performing marginally better than strong BERT-based baselines. We release the M3A dataset and benchmark models to motivate future research on this challenging problem domain.", "author": "Ramit Sawhney; Mihir Goyal; Prakhar Goel; Puneet Mathur; Rajiv Ratn Shah", "authorids": "/r/ramit-sawhney/; /m/mihir-goyal/; /p/prakhar-goel/; /p/puneet-mathur/; /r/rajiv-shah/", "bibtex": "@inproceedings{sawhney-etal-2021-multimodal,\n title = \"Multimodal Multi-Speaker Merger {\\&} Acquisition Financial Modeling: A New Task, Dataset, and Neural Baselines\",\n author = \"Sawhney, Ramit and\n Goyal, Mihir and\n Goel, Prakhar and\n Mathur, Puneet and\n Shah, Rajiv Ratn\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.526/\",\n doi = \"10.18653/v1/2021.acl-long.526\",\n pages = \"6751--6762\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.526.pdf", "site": "https://aclanthology.org/2021.acl-long.526/", "pdf_size": 693732, "gs_citation": 18, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=15026441540583251275&as_sdt=40005&sciodt=0,10&hl=en", "gs_version_total": 2, "aff": "Department of Computer Engineering, IIIT Delhi; Department of Computer Engineering, IIIT Delhi; Department of Computer Engineering, IIIT Delhi; University of Maryland, College Park; Department of Computer Engineering, IIIT Delhi", "aff_domain": "iiitd.ac.in;iiitd.ac.in;iiitd.ac.in;umd.edu;iiitd.ac.in", "email": "iiitd.ac.in;iiitd.ac.in;iiitd.ac.in;umd.edu;iiitd.ac.in", "github": "", "project": "https://www.investopedia.com/mergers-and-acquisitions", "author_num": 5, "aff_unique_index": "0;0;0;1;0", "aff_unique_norm": "IIIT Delhi;University of Maryland", "aff_unique_dep": "Department of Computer Engineering;", "aff_unique_url": "https://www.iiitdelhi.ac.in;https://www/umd.edu", "aff_unique_abbr": "IIITD;UMD", "aff_campus_unique_index": "1", "aff_campus_unique": ";College Park", "aff_country_unique_index": "0;0;0;1;0", "aff_country_unique": "India;United States" }, { "id": "2021.acl-long.28", "title": "Multimodal Sentiment Detection Based on Multi-channel Graph Neural Networks", "track": "main", "status": "Long", "award": false, "abstract": "With the popularity of smartphones, we have witnessed the rapid proliferation of multimodal posts on various social media platforms. We observe that the multimodal sentiment expression has specific global characteristics, such as the interdependencies of objects or scenes within the image. However, most previous studies only considered the representation of a single image-text post and failed to capture the global co-occurrence characteristics of the dataset. In this paper, we propose Multi-channel Graph Neural Networks with Sentiment-awareness (MGNNS) for image-text sentiment detection. Specifically, we first encode different modalities to capture hidden representations. Then, we introduce multi-channel graph neural networks to learn multimodal representations based on the global characteristics of the dataset. Finally, we implement multimodal in-depth fusion with the multi-head attention mechanism to predict the sentiment of image-text pairs. Extensive experiments conducted on three publicly available datasets demonstrate the effectiveness of our approach for multimodal sentiment detection.", "author": "Xiaocui Yang; Shi Feng; Yifei Zhang; Daling Wang", "authorids": "/x/xiaocui-yang/; /s/shi-feng/; /y/yifei-zhang/; /d/daling-wang/", "bibtex": "@inproceedings{yang-etal-2021-multimodal,\n title = \"Multimodal Sentiment Detection Based on Multi-channel Graph Neural Networks\",\n author = \"Yang, Xiaocui and\n Feng, Shi and\n Zhang, Yifei and\n Wang, Daling\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.28/\",\n doi = \"10.18653/v1/2021.acl-long.28\",\n pages = \"328--339\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.28.pdf", "site": "https://aclanthology.org/2021.acl-long.28/", "pdf_size": 1375627, "gs_citation": 117, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=589521936894732564&as_sdt=5,33&sciodt=0,33&hl=en", "gs_version_total": 3, "aff": "School of Computer Science and Engineering, Northeastern University, China; School of Computer Science and Engineering, Northeastern University, China; School of Computer Science and Engineering, Northeastern University, China; School of Computer Science and Engineering, Northeastern University, China", "aff_domain": "stumail.neu.edu.cn;cse.neu.edu.cn;cse.neu.edu.cn;cse.neu.edu.cn", "email": "stumail.neu.edu.cn;cse.neu.edu.cn;cse.neu.edu.cn;cse.neu.edu.cn", "github": "", "project": "", "author_num": 4, "aff_unique_index": "0;0;0;0", "aff_unique_norm": "Northeastern University", "aff_unique_dep": "School of Computer Science and Engineering", "aff_unique_url": "http://www.neu.edu.cn/", "aff_unique_abbr": "NEU", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0;0;0;0", "aff_country_unique": "China" }, { "id": "2021.acl-short.14", "title": "N-Best ASR Transformer: Enhancing SLU Performance using Multiple ASR Hypotheses", "track": "main", "status": "Short", "award": false, "abstract": "Spoken Language Understanding (SLU) systems parse speech into semantic structures like dialog acts and slots. This involves the use of an Automatic Speech Recognizer (ASR) to transcribe speech into multiple text alternatives (hypotheses). Transcription errors, ordinary in ASRs, impact downstream SLU performance negatively. Common approaches to mitigate such errors involve using richer information from the ASR, either in form of N-best hypotheses or word-lattices. We hypothesize that transformer models will learn better with a simpler utterance representation using the concatenation of the N-best ASR alternatives, where each alternative is separated by a special delimiter [SEP]. In our work, we test our hypothesis by using the concatenated N-best ASR alternatives as the input to the transformer encoder models, namely BERT and XLM-RoBERTa, and achieve equivalent performance to the prior state-of-the-art model on DSTC2 dataset. We also show that our approach significantly outperforms the prior state-of-the-art when subjected to the low data regime. Additionally, this methodology is accessible to users of third-party ASR APIs which do not provide word-lattice information.", "author": "Karthik Ganesan; Pakhi Bamdev; Jaivarsan B; Amresh Venugopal; Abhinav Tushar", "authorids": "/k/karthik-ganesan/; /p/pakhi-bamdev/; /j/jaivarsan-b/; /a/amresh-venugopal/; /a/abhinav-tushar/", "bibtex": "@inproceedings{ganesan-etal-2021-n,\n title = \"N-Best {ASR} Transformer: Enhancing {SLU} Performance using Multiple {ASR} Hypotheses\",\n author = \"Ganesan, Karthik and\n Bamdev, Pakhi and\n B, Jaivarsan and\n Venugopal, Amresh and\n Tushar, Abhinav\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-short.14/\",\n doi = \"10.18653/v1/2021.acl-short.14\",\n pages = \"93--98\"\n}", "pdf": "https://aclanthology.org/2021.acl-short.14.pdf", "site": "https://aclanthology.org/2021.acl-short.14/", "pdf_size": 427148, "gs_citation": 21, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=1208316185855165284&as_sdt=400005&sciodt=0,14&hl=en", "gs_version_total": 3, "aff": "Vernacular.ai; Vernacular.ai; Vernacular.ai; Vernacular.ai; Vernacular.ai", "aff_domain": "gmail.com;gmail.com;vernacular.ai;vernacular.ai;vernacular.ai", "email": "gmail.com;gmail.com;vernacular.ai;vernacular.ai;vernacular.ai", "github": "", "project": "", "author_num": 5, "aff_unique_index": "0;0;0;0;0", "aff_unique_norm": "Vernacular.ai", "aff_unique_dep": "", "aff_unique_url": "https://vernacular.ai", "aff_unique_abbr": "", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0;0;0;0;0", "aff_country_unique": "United States" }, { "id": "2021.acl-long.205", "title": "N-ary Constituent Tree Parsing with Recursive Semi-Markov Model", "track": "main", "status": "Long", "award": false, "abstract": "In this paper, we study the task of graph-based constituent parsing in the setting that binarization is not conducted as a pre-processing step, where a constituent tree may consist of nodes with more than two children. Previous graph-based methods on this setting typically generate hidden nodes with the dummy label inside the n-ary nodes, in order to transform the tree into a binary tree for prediction. The limitation is that the hidden nodes break the sibling relations of the n-ary node\u2019s children. Consequently, the dependencies of such sibling constituents might not be accurately modeled and is being ignored. To solve this limitation, we propose a novel graph-based framework, which is called \u201crecursive semi-Markov model\u201d. The main idea is to utilize 1-order semi-Markov model to predict the immediate children sequence of a constituent candidate, which then recursively serves as a child candidate of its parent. In this manner, the dependencies of sibling constituents can be described by 1-order transition features, which solves the above limitation. Through experiments, the proposed framework obtains the F1 of 95.92% and 92.50% on the datasets of PTB and CTB 5.1 respectively. Specially, the recursive semi-Markov model shows advantages in modeling nodes with more than two children, whose average F1 can be improved by 0.3-1.1 points in PTB and 2.3-6.8 points in CTB 5.1.", "author": "Xin Xin; Jinlong Li; Zeqi Tan", "authorids": "/x/xin-xin/; /j/jinlong-li/; /z/zeqi-tan/", "bibtex": "@inproceedings{xin-etal-2021-n,\n title = \"N-ary Constituent Tree Parsing with Recursive Semi-{M}arkov Model\",\n author = \"Xin, Xin and\n Li, Jinlong and\n Tan, Zeqi\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.205/\",\n doi = \"10.18653/v1/2021.acl-long.205\",\n pages = \"2631--2642\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.205.pdf", "site": "https://aclanthology.org/2021.acl-long.205/", "pdf_size": 1292814, "gs_citation": 17, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=15882414456859181372&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 2, "aff": "School of Computer Science and Technology, Beijing Institute of Technology, Beijing, China + BJ ER Center of HVLIPCCA, BIT, Beijing, China; School of Computer Science and Technology, Beijing Institute of Technology, Beijing, China + BJ ER Center of HVLIPCCA, BIT, Beijing, China; Zhejiang University, Hangzhou, China", "aff_domain": "bit.edu.cn;bit.edu.cn;zju.edu.cn", "email": "bit.edu.cn;bit.edu.cn;zju.edu.cn", "github": "", "project": "", "author_num": 3, "aff_unique_index": "0+0;0+0;1", "aff_unique_norm": "Beijing Institute of Technology;Zhejiang University", "aff_unique_dep": "School of Computer Science and Technology;", "aff_unique_url": "http://www.bit.edu.cn;http://www.zju.edu.cn", "aff_unique_abbr": "BIT;ZJU", "aff_campus_unique_index": "0+0;0+0;1", "aff_campus_unique": "Beijing;Hangzhou", "aff_country_unique_index": "0+0;0+0;0", "aff_country_unique": "China" }, { "id": "2021.acl-long.140", "title": "Named Entity Recognition with Small Strongly Labeled and Large Weakly Labeled Data", "track": "main", "status": "Long", "award": false, "abstract": "Weak supervision has shown promising results in many natural language processing tasks, such as Named Entity Recognition (NER). Existing work mainly focuses on learning deep NER models only with weak supervision, i.e., without any human annotation, and shows that by merely using weakly labeled data, one can achieve good performance, though still underperforms fully supervised NER with manually/strongly labeled data. In this paper, we consider a more practical scenario, where we have both a small amount of strongly labeled data and a large amount of weakly labeled data. Unfortunately, we observe that weakly labeled data does not necessarily improve, or even deteriorate the model performance (due to the extensive noise in the weak labels) when we train deep NER models over a simple or weighted combination of the strongly labeled and weakly labeled data. To address this issue, we propose a new multi-stage computational framework \u2013 NEEDLE with three essential ingredients: (1) weak label completion, (2) noise-aware loss function, and (3) final fine-tuning over the strongly labeled data. Through experiments on E-commerce query NER and Biomedical NER, we demonstrate that NEEDLE can effectively suppress the noise of the weak labels and outperforms existing methods. In particular, we achieve new SOTA F1-scores on 3 Biomedical NER datasets: BC5CDR-chem 93.74, BC5CDR-disease 90.69, NCBI-disease 92.28.", "author": "Haoming Jiang; Danqing Zhang; Tianyu Cao; Bing Yin; Tuo Zhao", "authorids": "/h/haoming-jiang/; /d/danqing-zhang/; /t/tianyu-cao/; /b/bing-yin/; /t/tuo-zhao/", "bibtex": "@inproceedings{jiang-etal-2021-named,\n title = \"Named Entity Recognition with Small Strongly Labeled and Large Weakly Labeled Data\",\n author = \"Jiang, Haoming and\n Zhang, Danqing and\n Cao, Tianyu and\n Yin, Bing and\n Zhao, Tuo\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.140/\",\n doi = \"10.18653/v1/2021.acl-long.140\",\n pages = \"1775--1789\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.140.pdf", "site": "https://aclanthology.org/2021.acl-long.140/", "pdf_size": 1922035, "gs_citation": 48, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=6533272004521721319&as_sdt=5,33&sciodt=0,33&hl=en", "gs_version_total": 8, "aff": "Georgia Institute of Technology, Atlanta, GA, USA+Amazon.com Inc, Palo Alto, CA, USA; Amazon.com Inc, Palo Alto, CA, USA; Amazon.com Inc, Palo Alto, CA, USA; Amazon.com Inc, Palo Alto, CA, USA; Georgia Institute of Technology, Atlanta, GA, USA", "aff_domain": "gatech.edu;amazon.com;amazon.com;amazon.com;gatech.edu", "email": "gatech.edu;amazon.com;amazon.com;amazon.com;gatech.edu", "github": "", "project": "", "author_num": 5, "aff_unique_index": "0+1;1;1;1;0", "aff_unique_norm": "Georgia Institute of Technology;Amazon.com Inc", "aff_unique_dep": ";", "aff_unique_url": "https://www.gatech.edu;https://www.amazon.com", "aff_unique_abbr": "Georgia Tech;Amazon", "aff_campus_unique_index": "0+1;1;1;1;0", "aff_campus_unique": "Atlanta;Palo Alto", "aff_country_unique_index": "0+0;0;0;0;0", "aff_country_unique": "United States" }, { "id": "2021.acl-long.275", "title": "Nested Named Entity Recognition via Explicitly Excluding the Influence of the Best Path", "track": "main", "status": "Long", "award": false, "abstract": "This paper presents a novel method for nested named entity recognition. As a layered method, our method extends the prior second-best path recognition method by explicitly excluding the influence of the best path. Our method maintains a set of hidden states at each time step and selectively leverages them to build a different potential function for recognition at each level. In addition, we demonstrate that recognizing innermost entities first results in better performance than the conventional outermost entities first scheme. We provide extensive experimental results on ACE2004, ACE2005, and GENIA datasets to show the effectiveness and efficiency of our proposed method.", "author": "Yiran Wang; Hiroyuki Shindo; Yuji Matsumoto; Taro Watanabe", "authorids": "/y/yiran-wang/; /h/hiroyuki-shindo/; /y/yuji-matsumoto/; /t/taro-watanabe/", "bibtex": "@inproceedings{wang-etal-2021-nested,\n title = \"Nested Named Entity Recognition via Explicitly Excluding the Influence of the Best Path\",\n author = \"Wang, Yiran and\n Shindo, Hiroyuki and\n Matsumoto, Yuji and\n Watanabe, Taro\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.275/\",\n doi = \"10.18653/v1/2021.acl-long.275\",\n pages = \"3547--3557\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.275.pdf", "site": "https://aclanthology.org/2021.acl-long.275/", "pdf_size": 445996, "gs_citation": 28, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=2806848371976713213&as_sdt=40000005&sciodt=0,22&hl=en", "gs_version_total": 7, "aff": "National Institute of Information and Communications Technology (NICT), Kyoto, Japan; Nara Institute of Science and Technology (NAIST), Nara, Japan; RIKEN Center for Advanced Intelligence Project (AIP), Tokyo, Japan; Nara Institute of Science and Technology (NAIST), Nara, Japan", "aff_domain": "nict.go.jp;is.naist.jp;riken.jp;is.naist.jp", "email": "nict.go.jp;is.naist.jp;riken.jp;is.naist.jp", "github": "", "project": "", "author_num": 4, "aff_unique_index": "0;1;2;1", "aff_unique_norm": "National Institute of Information and Communications Technology;Nara Institute of Science and Technology;RIKEN Center for Advanced Intelligence Project", "aff_unique_dep": ";;Advanced Intelligence Project", "aff_unique_url": "https://www.nict.go.jp;https://www.naist.jp;https://aipcenter.riken.jp/en/", "aff_unique_abbr": "NICT;NAIST;RIKEN AIP", "aff_campus_unique_index": "0;1;2;1", "aff_campus_unique": "Kyoto;Nara;Tokyo", "aff_country_unique_index": "0;0;0;0", "aff_country_unique": "Japan" }, { "id": "2021.acl-long.209", "title": "Neural Bi-Lexicalized PCFG Induction", "track": "main", "status": "Long", "award": false, "abstract": "Neural lexicalized PCFGs (L-PCFGs) have been shown effective in grammar induction. However, to reduce computational complexity, they make a strong independence assumption on the generation of the child word and thus bilexical dependencies are ignored. In this paper, we propose an approach to parameterize L-PCFGs without making implausible independence assumptions. Our approach directly models bilexical dependencies and meanwhile reduces both learning and representation complexities of L-PCFGs. Experimental results on the English WSJ dataset confirm the effectiveness of our approach in improving both running speed and unsupervised parsing performance.", "author": "Songlin Yang; Yanpeng Zhao; Kewei Tu", "authorids": "/s/songlin-yang/; /y/yanpeng-zhao/; /k/kewei-tu/", "bibtex": "@inproceedings{yang-etal-2021-neural,\n title = \"Neural Bi-Lexicalized {PCFG} Induction\",\n author = \"Yang, Songlin and\n Zhao, Yanpeng and\n Tu, Kewei\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.209/\",\n doi = \"10.18653/v1/2021.acl-long.209\",\n pages = \"2688--2699\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.209.pdf", "site": "https://aclanthology.org/2021.acl-long.209/", "pdf_size": 686983, "gs_citation": 22, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=6776268593258038234&as_sdt=5,33&sciodt=0,33&hl=en", "gs_version_total": 6, "aff": "School of Information Science and Technology, ShanghaiTech University + Shanghai Engineering Research Center of Intelligent Vision and Imaging + Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences + University of Chinese Academy of Sciences; ILCC, University of Edinburgh; School of Information Science and Technology, ShanghaiTech University + Shanghai Engineering Research Center of Intelligent Vision and Imaging + Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences + University of Chinese Academy of Sciences", "aff_domain": "shanghaitech.edu.cn;gmail.com;shanghaitech.edu.cn", "email": "shanghaitech.edu.cn;gmail.com;shanghaitech.edu.cn", "github": "", "project": "", "author_num": 3, "aff_unique_index": "0+1+2+3;4;0+1+2+3", "aff_unique_norm": "ShanghaiTech University;Shanghai Engineering Research Center of Intelligent Vision and Imaging;Shanghai Institute of Microsystem and Information Technology;University of Chinese Academy of Sciences;University of Edinburgh", "aff_unique_dep": "School of Information Science and Technology;;;;ILCC", "aff_unique_url": "https://www.shanghaitech.edu.cn;;http://www.sim.cas.cn;http://www.ucas.ac.cn;https://www.ed.ac.uk", "aff_unique_abbr": "ShanghaiTech;;SIM;UCAS;Edinburgh", "aff_campus_unique_index": "0+0;2;0+0", "aff_campus_unique": "Shanghai;;Edinburgh", "aff_country_unique_index": "0+0+0+0;1;0+0+0+0", "aff_country_unique": "China;United Kingdom" }, { "id": "2021.acl-long.567", "title": "Neural Machine Translation with Monolingual Translation Memory", "track": "main", "status": "Long", "award": true, "abstract": "Prior work has proved that Translation Memory (TM) can boost the performance of Neural Machine Translation (NMT). In contrast to existing work that uses bilingual corpus as TM and employs source-side similarity search for memory retrieval, we propose a new framework that uses monolingual memory and performs learnable memory retrieval in a cross-lingual manner. Our framework has unique advantages. First, the cross-lingual memory retriever allows abundant monolingual data to be TM. Second, the memory retriever and NMT model can be jointly optimized for the ultimate translation goal. Experiments show that the proposed method obtains substantial improvements. Remarkably, it even outperforms strong TM-augmented NMT baselines using bilingual TM. Owning to the ability to leverage monolingual data, our model also demonstrates effectiveness in low-resource and domain adaptation scenarios.", "author": "Deng Cai; Yan Wang; Huayang Li; Wai Lam; Lemao Liu", "authorids": "/d/deng-cai/; /y/yan-wang/; /h/huayang-li/; /w/wai-lam/; /l/lemao-liu/", "bibtex": "@inproceedings{cai-etal-2021-neural,\n title = \"Neural Machine Translation with Monolingual Translation Memory\",\n author = \"Cai, Deng and\n Wang, Yan and\n Li, Huayang and\n Lam, Wai and\n Liu, Lemao\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.567/\",\n doi = \"10.18653/v1/2021.acl-long.567\",\n pages = \"7307--7318\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.567.pdf", "site": "https://aclanthology.org/2021.acl-long.567/", "pdf_size": 1362150, "gs_citation": 111, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=12723364133386202887&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 6, "aff": "The Chinese University of Hong Kong; The Chinese University of Hong Kong + Tencent AI Lab; Tencent AI Lab; The Chinese University of Hong Kong; Tencent AI Lab", "aff_domain": "gmail.com;se.cuhk.edu.hk;tencent.com;tencent.com;tencent.com", "email": "gmail.com;se.cuhk.edu.hk;tencent.com;tencent.com;tencent.com", "github": "", "project": "", "author_num": 5, "aff_unique_index": "0;0+1;1;0;1", "aff_unique_norm": "The Chinese University of Hong Kong;Tencent", "aff_unique_dep": ";Tencent AI Lab", "aff_unique_url": "https://www.cuhk.edu.hk;https://ai.tencent.com", "aff_unique_abbr": "CUHK;Tencent AI Lab", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0;0+0;0;0;0", "aff_country_unique": "China" }, { "id": "2021.acl-short.35", "title": "Neural Retrieval for Question Answering with Cross-Attention Supervised Data Augmentation", "track": "main", "status": "Short", "award": false, "abstract": "Early fusion models with cross-attention have shown better-than-human performance on some question answer benchmarks, while it is a poor fit for retrieval since it prevents pre-computation of the answer representations. We present a supervised data mining method using an accurate early fusion model to improve the training of an efficient late fusion retrieval model. We first train an accurate classification model with cross-attention between questions and answers. The cross-attention model is then used to annotate additional passages in order to generate weighted training examples for a neural retrieval model. The resulting retrieval model with additional data significantly outperforms retrieval models directly trained with gold annotations on Precision at N (P@N) and Mean Reciprocal Rank (MRR).", "author": "Yinfei Yang; Ning Jin; Kuo Lin; Mandy Guo; Daniel Cer", "authorids": "/y/yinfei-yang/; /n/ning-jin/; /k/kuo-lin/; /m/mandy-guo/; /d/daniel-cer/", "bibtex": "@inproceedings{yang-etal-2021-neural-retrieval,\n title = \"Neural Retrieval for Question Answering with Cross-Attention Supervised Data Augmentation\",\n author = \"Yang, Yinfei and\n Jin, Ning and\n Lin, Kuo and\n Guo, Mandy and\n Cer, Daniel\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-short.35/\",\n doi = \"10.18653/v1/2021.acl-short.35\",\n pages = \"263--268\"\n}", "pdf": "https://aclanthology.org/2021.acl-short.35.pdf", "site": "https://aclanthology.org/2021.acl-short.35/", "pdf_size": 560822, "gs_citation": 32, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=11084817905376013240&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 7, "aff": "Google Research, Mountain View, CA, USA; Google Cloud AI, Sunnyvale, CA, USA; Google Cloud AI, Sunnyvale, CA, USA; Google Research, Mountain View, CA, USA; Google Research, Mountain View, CA, USA", "aff_domain": "google.com;google.com;google.com;google.com;google.com", "email": "google.com;google.com;google.com;google.com;google.com", "github": "", "project": "", "author_num": 5, "aff_unique_index": "0;1;1;0;0", "aff_unique_norm": "Google;Google Cloud AI", "aff_unique_dep": "Google Research;Google Cloud AI", "aff_unique_url": "https://research.google;https://cloud.google.com/ai", "aff_unique_abbr": "Google;Google Cloud AI", "aff_campus_unique_index": "0;1;1;0;0", "aff_campus_unique": "Mountain View;Sunnyvale", "aff_country_unique_index": "0;0;0;0;0", "aff_country_unique": "United States" }, { "id": "2021.acl-long.339", "title": "Neural Stylistic Response Generation with Disentangled Latent Variables", "track": "main", "status": "Long", "award": false, "abstract": "Generating open-domain conversational responses in the desired style usually suffers from the lack of parallel data in the style. Meanwhile, using monolingual stylistic data to increase style intensity often leads to the expense of decreasing content relevance. In this paper, we propose to disentangle the content and style in latent space by diluting sentence-level information in style representations. Combining the desired style representation and a response content representation will then obtain a stylistic response. Our approach achieves a higher BERT-based style intensity score and comparable BLEU scores, compared with baselines. Human evaluation results show that our approach significantly improves style intensity and maintains content relevance.", "author": "Qingfu Zhu; Wei-Nan Zhang; Ting Liu; William Yang Wang", "authorids": "/q/qingfu-zhu/; /w/weinan-zhang/; /t/ting-liu/; /w/william-yang-wang/", "bibtex": "@inproceedings{zhu-etal-2021-neural,\n title = \"Neural Stylistic Response Generation with Disentangled Latent Variables\",\n author = \"Zhu, Qingfu and\n Zhang, Wei-Nan and\n Liu, Ting and\n Wang, William Yang\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.339/\",\n doi = \"10.18653/v1/2021.acl-long.339\",\n pages = \"4391--4401\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.339.pdf", "site": "https://aclanthology.org/2021.acl-long.339/", "pdf_size": 846607, "gs_citation": 13, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=11636782571891017771&as_sdt=400005&sciodt=0,14&hl=en", "gs_version_total": 2, "aff": "Harbin Institute of Technology, Harbin, China; Harbin Institute of Technology, Harbin, China; Harbin Institute of Technology, Harbin, China; University of California, Santa Barbara, USA", "aff_domain": "ir.hit.edu.cn;ir.hit.edu.cn;ir.hit.edu.cn;cs.ucsb.edu", "email": "ir.hit.edu.cn;ir.hit.edu.cn;ir.hit.edu.cn;cs.ucsb.edu", "github": "", "project": "", "author_num": 4, "aff_unique_index": "0;0;0;1", "aff_unique_norm": "Harbin Institute of Technology;University of California, Santa Barbara", "aff_unique_dep": ";", "aff_unique_url": "http://www.hit.edu.cn/;https://www.ucsb.edu", "aff_unique_abbr": "HIT;UCSB", "aff_campus_unique_index": "0;0;0;1", "aff_campus_unique": "Harbin;Santa Barbara", "aff_country_unique_index": "0;0;0;1", "aff_country_unique": "China;United States" }, { "id": "2021.acl-long.531", "title": "Neural semi-Markov CRF for Monolingual Word Alignment", "track": "main", "status": "Long", "award": false, "abstract": "Monolingual word alignment is important for studying fine-grained editing operations (i.e., deletion, addition, and substitution) in text-to-text generation tasks, such as paraphrase generation, text simplification, neutralizing biased language, etc. In this paper, we present a novel neural semi-Markov CRF alignment model, which unifies word and phrase alignments through variable-length spans. We also create a new benchmark with human annotations that cover four different text genres to evaluate monolingual word alignment models in more realistic settings. Experimental results show that our proposed model outperforms all previous approaches for monolingual word alignment as well as a competitive QA-based baseline, which was previously only applied to bilingual data. Our model demonstrates good generalizability to three out-of-domain datasets and shows great utility in two downstream applications: automatic text simplification and sentence pair classification tasks.", "author": "Wuwei Lan; Chao Jiang; Wei Xu", "authorids": "/w/wuwei-lan/; /c/chao-jiang/; /w/wei-xu/", "bibtex": "@inproceedings{lan-etal-2021-neural,\n title = \"Neural semi-{M}arkov {CRF} for Monolingual Word Alignment\",\n author = \"Lan, Wuwei and\n Jiang, Chao and\n Xu, Wei\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.531/\",\n doi = \"10.18653/v1/2021.acl-long.531\",\n pages = \"6815--6828\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.531.pdf", "site": "https://aclanthology.org/2021.acl-long.531/", "pdf_size": 1750154, "gs_citation": 23, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=12612315729957133782&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 7, "aff": "Department of Computer Science and Engineering, Ohio State University; School of Interactive Computing, Georgia Institute of Technology; School of Interactive Computing, Georgia Institute of Technology", "aff_domain": "osu.edu;cc.gatech.edu;cc.gatech.edu", "email": "osu.edu;cc.gatech.edu;cc.gatech.edu", "github": "https://github.com/chaojiang06/neural-Jacana", "project": "", "author_num": 3, "aff_unique_index": "0;1;1", "aff_unique_norm": "Ohio State University;Georgia Institute of Technology", "aff_unique_dep": "Department of Computer Science and Engineering;School of Interactive Computing", "aff_unique_url": "https://www.osu.edu;https://www.gatech.edu", "aff_unique_abbr": "OSU;Georgia Tech", "aff_campus_unique_index": "1;1", "aff_campus_unique": ";Atlanta", "aff_country_unique_index": "0;0;0", "aff_country_unique": "United States" }, { "id": "2021.acl-short.100", "title": "Neural-Symbolic Commonsense Reasoner with Relation Predictors", "track": "main", "status": "Short", "award": false, "abstract": "Commonsense reasoning aims to incorporate sets of commonsense facts, retrieved from Commonsense Knowledge Graphs (CKG), to draw conclusion about ordinary situations. The dynamic nature of commonsense knowledge postulates models capable of performing multi-hop reasoning over new situations. This feature also results in having large-scale sparse Knowledge Graphs, where such reasoning process is needed to predict relations between new events. However, existing approaches in this area are limited by considering CKGs as a limited set of facts, thus rendering them unfit for reasoning over new unseen situations and events. In this paper, we present a neural-symbolic reasoner, which is capable of reasoning over large-scale dynamic CKGs. The logic rules for reasoning over CKGs are learned during training by our model. In addition to providing interpretable explanation, the learned logic rules help to generalise prediction to newly introduced events. Experimental results on the task of link prediction on CKGs prove the effectiveness of our model by outperforming the state-of-the-art models.", "author": "Farhad Moghimifar; Lizhen Qu; Terry Yue Zhuo; Gholamreza Haffari; Mahsa Baktashmotlagh", "authorids": "/f/farhad-moghimifar/; /l/lizhen-qu/; /t/terry-yue-zhuo/; /g/gholamreza-haffari/; /m/mahsa-baktashmotlagh/", "bibtex": "@inproceedings{moghimifar-etal-2021-neural,\n title = \"Neural-Symbolic Commonsense Reasoner with Relation Predictors\",\n author = \"Moghimifar, Farhad and\n Qu, Lizhen and\n Zhuo, Terry Yue and\n Haffari, Gholamreza and\n Baktashmotlagh, Mahsa\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-short.100/\",\n doi = \"10.18653/v1/2021.acl-short.100\",\n pages = \"797--802\"\n}", "pdf": "https://aclanthology.org/2021.acl-short.100.pdf", "site": "https://aclanthology.org/2021.acl-short.100/", "pdf_size": 1381630, "gs_citation": 16, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=13547583126621245542&as_sdt=5,33&sciodt=0,33&hl=en", "gs_version_total": 8, "aff": "The School of ITEE, The University of Queensland, Australia; Faculty of Information Technology, Monash University, Australia; School of CSE, The University of New South Wales, Australia; Faculty of Information Technology, Monash University, Australia; The School of ITEE, The University of Queensland, Australia", "aff_domain": "uq.edu.au;monash.edu;unsw.edu.au;monash.edu;uq.edu.au", "email": "uq.edu.au;monash.edu;unsw.edu.au;monash.edu;uq.edu.au", "github": "", "project": "", "author_num": 5, "aff_unique_index": "0;1;2;1;0", "aff_unique_norm": "The University of Queensland;Monash University;The University of New South Wales", "aff_unique_dep": "School of ITEE;Faculty of Information Technology;School of CSE", "aff_unique_url": "https://www.uq.edu.au;https://www.monash.edu;https://www.unsw.edu.au", "aff_unique_abbr": "UQ;Monash;UNSW", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0;0;0;0;0", "aff_country_unique": "Australia" }, { "id": "2021.acl-long.456", "title": "Neural-Symbolic Solver for Math Word Problems with Auxiliary Tasks", "track": "main", "status": "Long", "award": false, "abstract": "Previous math word problem solvers following the encoder-decoder paradigm fail to explicitly incorporate essential math symbolic constraints, leading to unexplainable and unreasonable predictions. Herein, we propose Neural-Symbolic Solver (NS-Solver) to explicitly and seamlessly incorporate different levels of symbolic constraints by auxiliary tasks. Our NS-Solver consists of a problem reader to encode problems, a programmer to generate symbolic equations, and a symbolic executor to obtain answers. Along with target expression supervision, our solver is also optimized via 4 new auxiliary objectives to enforce different symbolic reasoning: a) self-supervised number prediction task predicting both number quantity and number locations; b) commonsense constant prediction task predicting what prior knowledge (e.g. how many legs a chicken has) is required; c) program consistency checker computing the semantic loss between predicted equation and target equation to ensure reasonable equation mapping; d) duality exploiting task exploiting the quasi-duality between symbolic equation generation and problem\u2019s part-of-speech generation to enhance the understanding ability of a solver. Besides, to provide a more realistic and challenging benchmark for developing a universal and scalable solver, we also construct a new largescale MWP benchmark CM17K consisting of 4 kinds of MWPs (arithmetic, one-unknown linear, one-unknown non-linear, equation set) with more than 17K samples. Extensive experiments on Math23K and our CM17k demonstrate the superiority of our NS-Solver compared to state-of-the-art methods.", "author": "Jinghui Qin; Xiaodan Liang; Yining Hong; Jianheng Tang; Liang Lin", "authorids": "/j/jinghui-qin/; /x/xiaodan-liang/; /y/yining-hong/; /j/jianheng-tang/; /l/liang-lin/", "bibtex": "@inproceedings{qin-etal-2021-neural,\n title = \"Neural-Symbolic Solver for Math Word Problems with Auxiliary Tasks\",\n author = \"Qin, Jinghui and\n Liang, Xiaodan and\n Hong, Yining and\n Tang, Jianheng and\n Lin, Liang\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.456/\",\n doi = \"10.18653/v1/2021.acl-long.456\",\n pages = \"5870--5881\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.456.pdf", "site": "https://aclanthology.org/2021.acl-long.456/", "pdf_size": 531063, "gs_citation": 67, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=8517391361551934428&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 6, "aff": "Sun Yat-sen University; Sun Yat-sen University+Dark Matter AI Inc.; University of California, Los Angeles; Sun Yat-sen University; Sun Yat-sen University+Dark Matter AI Inc.", "aff_domain": "mail2.sysu.edu.cn;gmail.com;gmail.com;cs.ucla.edu;ieee.org", "email": "mail2.sysu.edu.cn;gmail.com;gmail.com;cs.ucla.edu;ieee.org", "github": "https://github.com/QinJinghui/NS-Solver", "project": "", "author_num": 5, "aff_unique_index": "0;0+1;2;0;0+1", "aff_unique_norm": "Sun Yat-sen University;Dark Matter AI Inc.;University of California, Los Angeles", "aff_unique_dep": ";;", "aff_unique_url": "http://www.sysu.edu.cn/;;https://www.ucla.edu", "aff_unique_abbr": "SYSU;;UCLA", "aff_campus_unique_index": ";1;", "aff_campus_unique": ";Los Angeles", "aff_country_unique_index": "0;0+1;1;0;0+1", "aff_country_unique": "China;United States" }, { "id": "2021.acl-long.287", "title": "NeuralWOZ: Learning to Collect Task-Oriented Dialogue via Model-Based Simulation", "track": "main", "status": "Long", "award": false, "abstract": "We propose NeuralWOZ, a novel dialogue collection framework that uses model-based dialogue simulation. NeuralWOZ has two pipelined models, Collector and Labeler. Collector generates dialogues from (1) user\u2019s goal instructions, which are the user context and task constraints in natural language, and (2) system\u2019s API call results, which is a list of possible query responses for user requests from the given knowledge base. Labeler annotates the generated dialogue by formulating the annotation as a multiple-choice problem, in which the candidate labels are extracted from goal instructions and API call results. We demonstrate the effectiveness of the proposed method in the zero-shot domain transfer learning for dialogue state tracking. In the evaluation, the synthetic dialogue corpus generated from NeuralWOZ achieves a new state-of-the-art with improvements of 4.4% point joint goal accuracy on average across domains, and improvements of 5.7% point of zero-shot coverage against the MultiWOZ 2.1 dataset.", "author": "Sungdong Kim; Minsuk Chang; Sang-Woo Lee", "authorids": "/s/sungdong-kim/; /m/minsuk-chang/; /s/sang-woo-lee/", "bibtex": "@inproceedings{kim-etal-2021-neuralwoz,\n title = \"{N}eural{WOZ}: Learning to Collect Task-Oriented Dialogue via Model-Based Simulation\",\n author = \"Kim, Sungdong and\n Chang, Minsuk and\n Lee, Sang-Woo\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.287/\",\n doi = \"10.18653/v1/2021.acl-long.287\",\n pages = \"3704--3717\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.287.pdf", "site": "https://aclanthology.org/2021.acl-long.287/", "pdf_size": 1380099, "gs_citation": 20, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=3982086795183379087&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 3, "aff": "NA VER AI Lab1+NA VER Clova2; NA VER AI Lab1+NA VER Clova2; NA VER AI Lab1+NA VER Clova2", "aff_domain": "navercorp.com;navercorp.com;navercorp.com", "email": "navercorp.com;navercorp.com;navercorp.com", "github": "github.com/naver-ai/neuralwoz", "project": "", "author_num": 3, "aff_unique_index": "0+1;0+1;0+1", "aff_unique_norm": "NAVER AI Lab;NAVER Clova", "aff_unique_dep": "AI Lab;", "aff_unique_url": "https://www.naver.com;https://www.naver.com", "aff_unique_abbr": "NAVER AI Lab;NAVER", "aff_campus_unique_index": ";;", "aff_campus_unique": "", "aff_country_unique_index": "0+0;0+0;0+0", "aff_country_unique": "South Korea" }, { "id": "2021.acl-long.270", "title": "Novel Slot Detection: A Benchmark for Discovering Unknown Slot Types in the Task-Oriented Dialogue System", "track": "main", "status": "Long", "award": false, "abstract": "Existing slot filling models can only recognize pre-defined in-domain slot types from a limited slot set. In the practical application, a reliable dialogue system should know what it does not know. In this paper, we introduce a new task, Novel Slot Detection (NSD), in the task-oriented dialogue system. NSD aims to discover unknown or out-of-domain slot types to strengthen the capability of a dialogue system based on in-domain training data. Besides, we construct two public NSD datasets, propose several strong NSD baselines, and establish a benchmark for future work. Finally, we conduct exhaustive experiments and qualitative analysis to comprehend key challenges and provide new guidance for future directions.", "author": "Yanan Wu; Zhiyuan Zeng; Keqing He; Hong Xu; Yuanmeng Yan; Huixing Jiang; Weiran Xu", "authorids": "/y/yanan-wu/; /z/zhiyuan-zeng/; /k/keqing-he/; /h/hong-xu/; /y/yuanmeng-yan/; /h/huixing-jiang/; /w/weiran-xu/", "bibtex": "@inproceedings{wu-etal-2021-novel,\n title = \"Novel Slot Detection: A Benchmark for Discovering Unknown Slot Types in the Task-Oriented Dialogue System\",\n author = \"Wu, Yanan and\n Zeng, Zhiyuan and\n He, Keqing and\n Xu, Hong and\n Yan, Yuanmeng and\n Jiang, Huixing and\n Xu, Weiran\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.270/\",\n doi = \"10.18653/v1/2021.acl-long.270\",\n pages = \"3484--3494\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.270.pdf", "site": "https://aclanthology.org/2021.acl-long.270/", "pdf_size": 563912, "gs_citation": 8, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=6685087657620007511&as_sdt=5,33&sciodt=0,33&hl=en", "gs_version_total": 4, "aff": "Pattern Recognition & Intelligent System Laboratory, Beijing University of Posts and Telecommunications, Beijing, China; Pattern Recognition & Intelligent System Laboratory, Beijing University of Posts and Telecommunications, Beijing, China; Meituan Group, Beijing, China; Pattern Recognition & Intelligent System Laboratory, Beijing University of Posts and Telecommunications, Beijing, China; Pattern Recognition & Intelligent System Laboratory, Beijing University of Posts and Telecommunications, Beijing, China; Meituan Group, Beijing, China; Pattern Recognition & Intelligent System Laboratory, Beijing University of Posts and Telecommunications, Beijing, China", "aff_domain": "bupt.edu.cn;bupt.edu.cn;meituan.com;bupt.edu.cn;bupt.edu.cn;meituan.com;bupt.edu.cn", "email": "bupt.edu.cn;bupt.edu.cn;meituan.com;bupt.edu.cn;bupt.edu.cn;meituan.com;bupt.edu.cn", "github": "https://github.com/ChestnutWYN/ACL20", "project": "", "author_num": 7, "aff_unique_index": "0;0;1;0;0;1;0", "aff_unique_norm": "Beijing University of Posts and Telecommunications;Meituan Group", "aff_unique_dep": "Pattern Recognition & Intelligent System Laboratory;", "aff_unique_url": "http://www.bupt.edu.cn/;https://www.meituan.com", "aff_unique_abbr": "BUPT;Meituan", "aff_campus_unique_index": "0;0;0;0;0;0;0", "aff_campus_unique": "Beijing", "aff_country_unique_index": "0;0;0;0;0;0;0", "aff_country_unique": "China" }, { "id": "2021.acl-long.194", "title": "OTTers: One-turn Topic Transitions for Open-Domain Dialogue", "track": "main", "status": "Long", "award": false, "abstract": "Mixed initiative in open-domain dialogue requires a system to pro-actively introduce new topics. The one-turn topic transition task explores how a system connects two topics in a cooperative and coherent manner. The goal of the task is to generate a \u201cbridging\u201d utterance connecting the new topic to the topic of the previous conversation turn. We are especially interested in commonsense explanations of how a new topic relates to what has been mentioned before. We first collect a new dataset of human one-turn topic transitions, which we callOTTers. We then explore different strategies used by humans when asked to complete such a task, and notice that the use of a bridging utterance to connect the two topics is the approach used the most. We finally show how existing state-of-the-art text generation models can be adapted to this task and examine the performance of these baselines on different splits of the OTTers data.", "author": "Karin Sevegnani; David M. Howcroft; Ioannis Konstas; Verena Rieser", "authorids": "/k/karin-sevegnani/; /d/david-m-howcroft/; /i/ioannis-konstas/; /v/verena-rieser/", "bibtex": "@inproceedings{sevegnani-etal-2021-otters,\n title = \"{OTT}ers: One-turn Topic Transitions for Open-Domain Dialogue\",\n author = \"Sevegnani, Karin and\n Howcroft, David M. and\n Konstas, Ioannis and\n Rieser, Verena\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.194/\",\n doi = \"10.18653/v1/2021.acl-long.194\",\n pages = \"2492--2504\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.194.pdf", "site": "https://aclanthology.org/2021.acl-long.194/", "pdf_size": 525893, "gs_citation": 47, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=8688496741921718683&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 13, "aff": "The Interaction Lab, MACS Heriot-Watt University; The Interaction Lab, MACS Heriot-Watt University; The Interaction Lab, MACS Heriot-Watt University; The Interaction Lab, MACS Heriot-Watt University", "aff_domain": "hw.ac.uk;hw.ac.uk;hw.ac.uk;hw.ac.uk", "email": "hw.ac.uk;hw.ac.uk;hw.ac.uk;hw.ac.uk", "github": "https://github.com/karinseve/OTTers", "project": "", "author_num": 4, "aff_unique_index": "0;0;0;0", "aff_unique_norm": "Heriot-Watt University", "aff_unique_dep": "The Interaction Lab", "aff_unique_url": "https://www.hw.ac.uk", "aff_unique_abbr": "HWU", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0;0;0;0", "aff_country_unique": "United Kingdom" }, { "id": "2021.acl-long.408", "title": "Obtaining Better Static Word Embeddings Using Contextual Embedding Models", "track": "main", "status": "Long", "award": false, "abstract": "The advent of contextual word embeddings \u2014 representations of words which incorporate semantic and syntactic information from their context\u2014has led to tremendous improvements on a wide variety of NLP tasks. However, recent contextual models have prohibitively high computational cost in many use-cases and are often hard to interpret. In this work, we demonstrate that our proposed distillation method, which is a simple extension of CBOW-based training, allows to significantly improve computational efficiency of NLP applications, while outperforming the quality of existing static embeddings trained from scratch as well as those distilled from previously proposed methods. As a side-effect, our approach also allows a fair comparison of both contextual and static embeddings via standard lexical evaluation tasks.", "author": "Prakhar Gupta; Martin Jaggi", "authorids": "/p/prakhar-gupta/; /m/martin-jaggi/", "bibtex": "@inproceedings{gupta-jaggi-2021-obtaining,\n title = \"Obtaining Better Static Word Embeddings Using Contextual Embedding Models\",\n author = \"Gupta, Prakhar and\n Jaggi, Martin\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.408/\",\n doi = \"10.18653/v1/2021.acl-long.408\",\n pages = \"5241--5253\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.408.pdf", "site": "https://aclanthology.org/2021.acl-long.408/", "pdf_size": 399496, "gs_citation": 41, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=17675367125605268514&as_sdt=5,33&sciodt=0,33&hl=en", "gs_version_total": 5, "aff": "EPFL, Switzerland; EPFL, Switzerland", "aff_domain": "epfl.ch;epfl.ch", "email": "epfl.ch;epfl.ch", "github": "", "project": "", "author_num": 2, "aff_unique_index": "0;0", "aff_unique_norm": "\u00c9cole Polytechnique F\u00e9d\u00e9rale de Lausanne", "aff_unique_dep": "", "aff_unique_url": "https://www.epfl.ch", "aff_unique_abbr": "EPFL", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0;0", "aff_country_unique": "Switzerland" }, { "id": "2021.acl-long.368", "title": "On Compositional Generalization of Neural Machine Translation", "track": "main", "status": "Long", "award": false, "abstract": "Modern neural machine translation (NMT) models have achieved competitive performance in standard benchmarks such as WMT. However, there still exist significant issues such as robustness, domain generalization, etc. In this paper, we study NMT models from the perspective of compositional generalization by building a benchmark dataset, CoGnition, consisting of 216k clean and consistent sentence pairs. We quantitatively analyze effects of various factors using compound translation error rate, then demonstrate that the NMT model fails badly on compositional generalization, although it performs remarkably well under traditional metrics.", "author": "Yafu Li; Yongjing Yin; Yulong Chen; Yue Zhang", "authorids": "/y/yafu-li/; /y/yongjing-yin/; /y/yulong-chen/; /y/yue-zhang/", "bibtex": "https://aclanthology.org/2021.acl-long.368.bib", "pdf": "https://aclanthology.org/2021.acl-long.368.pdf", "site": "https://aclanthology.org/2021.acl-long.368/", "gs_citation": 47, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=5613905650251900330&as_sdt=5,33&sciodt=0,33&hl=en", "gs_version_total": 4, "aff": "Zhejiang University + School of Engineering, Westlake University + Institute of Advanced Technology, Westlake Institute for Advanced Study; School of Engineering, Westlake University; School of Engineering, Westlake University; Institute of Advanced Technology, Westlake Institute for Advanced Study", "aff_domain": "gmail.com;westlake.edu.cn;gmail.com;wias.org.cn", "email": "gmail.com;westlake.edu.cn;gmail.com;wias.org.cn", "github": "", "project": "", "author_num": 4, "aff_unique_index": "0+1+2;1;1;2", "aff_unique_norm": "Zhejiang University;Westlake University;Westlake Institute for Advanced Study", "aff_unique_dep": ";School of Engineering;Institute of Advanced Technology", "aff_unique_url": "https://www.zju.edu.cn;https://www.westlake.edu.cn;http://www.wias.org.cn/", "aff_unique_abbr": "ZJU;;WIAS", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0+0+0;0;0;0", "aff_country_unique": "China" }, { "id": "2021.acl-long.106", "title": "On Finding the K-best Non-projective Dependency Trees", "track": "main", "status": "Long", "award": false, "abstract": "The connection between the maximum spanning tree in a directed graph and the best dependency tree of a sentence has been exploited by the NLP community. However, for many dependency parsing schemes, an important detail of this approach is that the spanning tree must have exactly one edge emanating from the root. While work has been done to efficiently solve this problem for finding the one-best dependency tree, no research has attempted to extend this solution to finding the K-best dependency trees. This is arguably a more important extension as a larger proportion of decoded trees will not be subject to the root constraint of dependency trees. Indeed, we show that the rate of root constraint violations increases by an average of 13 times when decoding with K=50 as opposed to K=1. In this paper, we provide a simplification of the K-best spanning tree algorithm of Camerini et al. (1980). Our simplification allows us to obtain a constant time speed-up over the original algorithm. Furthermore, we present a novel extension of the algorithm for decoding the K-best dependency trees of a graph which are subject to a root constraint.", "author": "Ran Zmigrod; Tim Vieira; Ryan Cotterell", "authorids": "/r/ran-zmigrod/; /t/tim-vieira/; /r/ryan-cotterell/", "bibtex": "@inproceedings{zmigrod-etal-2021-finding,\n title = \"On Finding the K-best Non-projective Dependency Trees\",\n author = \"Zmigrod, Ran and\n Vieira, Tim and\n Cotterell, Ryan\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.106/\",\n doi = \"10.18653/v1/2021.acl-long.106\",\n pages = \"1324--1337\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.106.pdf", "site": "https://aclanthology.org/2021.acl-long.106/", "pdf_size": 585478, "gs_citation": 10, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=14722801367538274472&as_sdt=5,44&sciodt=0,44&hl=en", "gs_version_total": 10, "aff": "University of Cambridge; Johns Hopkins University; ETH Z\u00fcrich", "aff_domain": "cam.ac.uk;gmail.com;inf.ethz.ch", "email": "cam.ac.uk;gmail.com;inf.ethz.ch", "github": "https://github.com/rycolab/spanningtrees", "project": "", "author_num": 3, "aff_unique_index": "0;1;2", "aff_unique_norm": "University of Cambridge;Johns Hopkins University;ETH Z\u00fcrich", "aff_unique_dep": ";;", "aff_unique_url": "https://www.cam.ac.uk;https://www.jhu.edu;https://www.ethz.ch", "aff_unique_abbr": "Cambridge;JHU;ETHZ", "aff_campus_unique_index": "0", "aff_campus_unique": "Cambridge;", "aff_country_unique_index": "0;1;2", "aff_country_unique": "United Kingdom;United States;Switzerland" }, { "id": "2021.acl-short.48", "title": "On Orthogonality Constraints for Transformers", "track": "main", "status": "Short", "award": false, "abstract": "Orthogonality constraints encourage matrices to be orthogonal for numerical stability. These plug-and-play constraints, which can be conveniently incorporated into model training, have been studied for popular architectures in natural language processing, such as convolutional neural networks and recurrent neural networks. However, a dedicated study on such constraints for transformers has been absent. To fill this gap, this paper studies orthogonality constraints for transformers, showing the effectiveness with empirical evidence from ten machine translation tasks and two dialogue generation tasks. For example, on the large-scale WMT\u201916 En\u2192De benchmark, simply plugging-and-playing orthogonality constraints on the original transformer model (Vaswani et al., 2017) increases the BLEU from 28.4 to 29.6, coming close to the 29.7 BLEU achieved by the very competitive dynamic convolution (Wu et al., 2019).", "author": "Aston Zhang; Alvin Chan; Yi Tay; Jie Fu; Shuohang Wang; Shuai Zhang; Huajie Shao; Shuochao Yao; Roy Ka-Wei Lee", "authorids": "/a/aston-zhang/; /a/alvin-chan/; /y/yi-tay/; /j/jie-fu/; /s/shuohang-wang/; /s/shuai-zhang/; /h/huajie-shao/; /s/shuochao-yao/; /r/roy-ka-wei-lee/", "bibtex": "@inproceedings{zhang-etal-2021-orthogonality,\n title = \"On Orthogonality Constraints for Transformers\",\n author = \"Zhang, Aston and\n Chan, Alvin and\n Tay, Yi and\n Fu, Jie and\n Wang, Shuohang and\n Zhang, Shuai and\n Shao, Huajie and\n Yao, Shuochao and\n Lee, Roy Ka-Wei\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-short.48/\",\n doi = \"10.18653/v1/2021.acl-short.48\",\n pages = \"375--382\"\n}", "pdf": "https://aclanthology.org/2021.acl-short.48.pdf", "site": "https://aclanthology.org/2021.acl-short.48/", "pdf_size": 297794, "gs_citation": 24, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=5149532910502351204&as_sdt=5,33&sciodt=0,33&hl=en", "gs_version_total": 4, "aff": "AWS; NTU Singapore; Google; Mila, Universit \u00b4e de Montr \u00b4eal; SMU; ETH Z \u00a8urich; UIUC; George Mason University; SUTD", "aff_domain": "astonzhang.com; ; ; ; ; ; ; ; ", "email": "astonzhang.com; ; ; ; ; ; ; ; ", "github": "", "project": "", "author_num": 9, "aff_unique_index": "0;1;2;3;4;5;6;7;8", "aff_unique_norm": "Amazon Web Services;Nanyang Technological University;Google;Universit\u00e9 de Montr\u00e9al;Southern Methodist University;ETH Z\u00fcrich;University of Illinois at Urbana-Champaign;George Mason University;Singapore University of Technology and Design", "aff_unique_dep": ";;;Mila;;;;;", "aff_unique_url": "https://aws.amazon.com;https://www.ntu.edu.sg;https://www.google.com;https://www.mila.quebec;https://www.smu.edu;https://www.ethz.ch;https://www illinois.edu;https://www.gmu.edu;https://www.sutd.edu.sg", "aff_unique_abbr": "AWS;NTU;Google;Mila;SMU;ETHZ;UIUC;GMU;SUTD", "aff_campus_unique_index": "1;2;3;4;5", "aff_campus_unique": ";Singapore;Mountain View;Montr\u00e9al;Z\u00fcrich;Urbana-Champaign", "aff_country_unique_index": "0;1;0;2;0;3;0;0;1", "aff_country_unique": "United States;Singapore;Canada;Switzerland" }, { "id": "2021.acl-short.39", "title": "On Positivity Bias in Negative Reviews", "track": "main", "status": "Short", "award": false, "abstract": "Prior work has revealed that positive words occur more frequently than negative words in human expressions, which is typically attributed to positivity bias, a tendency for people to report positive views of reality. But what about the language used in negative reviews? Consistent with prior work, we show that English negative reviews tend to contain more positive words than negative words, using a variety of datasets. We reconcile this observation with prior findings on the pragmatics of negation, and show that negations are commonly associated with positive words in negative reviews. Furthermore, in negative reviews, the majority of sentences with positive words express negative opinions based on sentiment classifiers, indicating some form of negation.", "author": "Madhusudhan Aithal; Chenhao Tan", "authorids": "/m/madhusudhan-aithal/; /c/chenhao-tan/", "bibtex": "@inproceedings{aithal-tan-2021-positivity,\n title = \"On Positivity Bias in Negative Reviews\",\n author = \"Aithal, Madhusudhan and\n Tan, Chenhao\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-short.39/\",\n doi = \"10.18653/v1/2021.acl-short.39\",\n pages = \"294--304\"\n}", "pdf": "https://aclanthology.org/2021.acl-short.39.pdf", "site": "https://aclanthology.org/2021.acl-short.39/", "pdf_size": 2339137, "gs_citation": 11, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=8981814384085041131&as_sdt=5,33&sciodt=0,33&hl=en", "gs_version_total": 5, "aff": "University of Colorado Boulder; University of Chicago", "aff_domain": "colorado.edu;uchicago.edu", "email": "colorado.edu;uchicago.edu", "github": "", "project": "", "author_num": 2, "aff_unique_index": "0;1", "aff_unique_norm": "University of Colorado;University of Chicago", "aff_unique_dep": ";", "aff_unique_url": "https://www.colorado.edu;https://www.uchicago.edu", "aff_unique_abbr": "CU Boulder;UChicago", "aff_campus_unique_index": "0", "aff_campus_unique": "Boulder;", "aff_country_unique_index": "0;0", "aff_country_unique": "United States" }, { "id": "2021.acl-long.419", "title": "On Sample Based Explanation Methods for NLP: Faithfulness, Efficiency and Semantic Evaluation", "track": "main", "status": "Long", "award": false, "abstract": "In the recent advances of natural language processing, the scale of the state-of-the-art models and datasets is usually extensive, which challenges the application of sample-based explanation methods in many aspects, such as explanation interpretability, efficiency, and faithfulness. In this work, for the first time, we can improve the interpretability of explanations by allowing arbitrary text sequences as the explanation unit. On top of this, we implement a hessian-free method with a model faithfulness guarantee. Finally, to compare our method with the others, we propose a semantic-based evaluation metric that can better align with humans\u2019 judgment of explanations than the widely adopted diagnostic or re-training measures. The empirical results on multiple real data sets demonstrate the proposed method\u2019s superior performance to popular explanation techniques such as Influence Function or TracIn on semantic evaluation.", "author": "Wei Zhang; Ziming Huang; Yada Zhu; Guangnan Ye; Xiaodong Cui; Fan Zhang", "authorids": "/w/wei-zhang/; /z/ziming-huang/; /y/yada-zhu/; /g/guangnan-ye/; /x/xiaodong-cui/; /f/fan-zhang/", "bibtex": "@inproceedings{zhang-etal-2021-sample,\n title = \"On Sample Based Explanation Methods for {NLP}: Faithfulness, Efficiency and Semantic Evaluation\",\n author = \"Zhang, Wei and\n Huang, Ziming and\n Zhu, Yada and\n Ye, Guangnan and\n Cui, Xiaodong and\n Zhang, Fan\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.419/\",\n doi = \"10.18653/v1/2021.acl-long.419\",\n pages = \"5399--5411\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.419.pdf", "site": "https://aclanthology.org/2021.acl-long.419/", "pdf_size": 436472, "gs_citation": -1, "gs_cited_by_link": "", "gs_version_total": 0, "aff": "Wayfair+IBM T.J. Watson Research Center; Sogou Inc+IBM Research Lab; MIT-IBM Watson AI Lab+IBM Research; IBM Research; IBM Research; IBM Data and AI", "aff_domain": "wayfair.com;gmail.com;us.ibm.com;us.ibm.com;us.ibm.com;us.ibm.com", "email": "wayfair.com;gmail.com;us.ibm.com;us.ibm.com;us.ibm.com;us.ibm.com", "github": "", "project": "", "author_num": 6, "aff_unique_index": "0+1;2+1;3+1;1;1;1", "aff_unique_norm": "Wayfair;IBM;Sogou Inc;Massachusetts Institute of Technology", "aff_unique_dep": ";Research Center;;MIT-IBM Watson AI Lab", "aff_unique_url": "https://www.wayfair.com;https://www.ibm.com/research/watson;https://www.sogou.com;https://www.mitibmwatsonailab.org", "aff_unique_abbr": "Wayfair;IBM;Sogou;MIT-IBM AI Lab", "aff_campus_unique_index": "1;;", "aff_campus_unique": ";T.J. Watson", "aff_country_unique_index": "0+0;1+0;0+0;0;0;0", "aff_country_unique": "United States;China" }, { "id": "2021.acl-short.2", "title": "On Training Instance Selection for Few-Shot Neural Text Generation", "track": "main", "status": "Short", "award": false, "abstract": "Large-scale pretrained language models have led to dramatic improvements in text generation. Impressive performance can be achieved by finetuning only on a small number of instances (few-shot setting). Nonetheless, almost all previous work simply applies random sampling to select the few-shot training instances. Little to no attention has been paid to the selection strategies and how they would affect model performance. In this work, we present a study on training instance selection in few-shot neural text generation. The selection decision is made based only on the unlabeled data so as to identify the most worthwhile data points that should be annotated under some budget of labeling cost. Based on the intuition that the few-shot training instances should be diverse and representative of the entire data distribution, we propose a simple selection strategy with K-means clustering. We show that even with the naive clustering-based approach, the generation models consistently outperform random sampling on three text generation tasks: data-to-text generation, document summarization and question generation. The code and training data are made available. We hope that this work will call for more attention on this largely unexplored area.", "author": "Ernie Chang; Xiaoyu Shen; Hui-Syuan Yeh; Vera Demberg", "authorids": "/e/ernie-chang/; /x/xiaoyu-shen/; /h/hui-syuan-yeh/; /v/vera-demberg/", "bibtex": "@inproceedings{chang-etal-2021-training,\n title = \"On Training Instance Selection for Few-Shot Neural Text Generation\",\n author = \"Chang, Ernie and\n Shen, Xiaoyu and\n Yeh, Hui-Syuan and\n Demberg, Vera\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-short.2/\",\n doi = \"10.18653/v1/2021.acl-short.2\",\n pages = \"8--13\"\n}", "pdf": "https://aclanthology.org/2021.acl-short.2.pdf", "site": "https://aclanthology.org/2021.acl-short.2/", "pdf_size": 325700, "gs_citation": 40, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=1314339309669351263&as_sdt=80000005&sciodt=0,23&hl=en", "gs_version_total": 7, "aff": "Dept. of Language Science and Technology, Saarland University; Dept. of Language Science and Technology, Saarland University; Dept. of Language Science and Technology, Saarland University; Dept. of Language Science and Technology, Saarland University", "aff_domain": "coli.uni-saarland.de;coli.uni-saarland.de; ; ", "email": "coli.uni-saarland.de;coli.uni-saarland.de; ; ", "github": "", "project": "https://gitlab.com/erniecyc/few-selector", "author_num": 4, "aff_unique_index": "0;0;0;0", "aff_unique_norm": "Saarland University", "aff_unique_dep": "Dept. of Language Science and Technology", "aff_unique_url": "https://www.uni-saarland.de", "aff_unique_abbr": "Saarland U", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0;0;0;0", "aff_country_unique": "Germany" }, { "id": "2021.acl-long.172", "title": "On the Effectiveness of Adapter-based Tuning for Pretrained Language Model Adaptation", "track": "main", "status": "Long", "award": false, "abstract": "Adapter-based tuning has recently arisen as an alternative to fine-tuning. It works by adding light-weight adapter modules to a pretrained language model (PrLM) and only updating the parameters of adapter modules when learning on a downstream task. As such, it adds only a few trainable parameters per new task, allowing a high degree of parameter sharing. Prior studies have shown that adapter-based tuning often achieves comparable results to fine-tuning. However, existing work only focuses on the parameter-efficient aspect of adapter-based tuning while lacking further investigation on its effectiveness. In this paper, we study the latter. We first show that adapter-based tuning better mitigates forgetting issues than fine-tuning since it yields representations with less deviation from those generated by the initial PrLM. We then empirically compare the two tuning methods on several downstream NLP tasks and settings. We demonstrate that 1) adapter-based tuning outperforms fine-tuning on low-resource and cross-lingual tasks; 2) it is more robust to overfitting and less sensitive to changes in learning rates.", "author": "Ruidan He; Linlin Liu; Hai Ye; Qingyu Tan; Bosheng Ding; Liying Cheng; Jiawei Low; Lidong Bing; Luo Si", "authorids": "/r/ruidan-he/; /l/linlin-liu/; /h/hai-ye/; /q/qingyu-tan/; /b/bosheng-ding/; /l/liying-cheng/; /j/jiawei-low/; /l/lidong-bing/; /l/luo-si/", "bibtex": "@inproceedings{he-etal-2021-effectiveness,\n title = \"On the Effectiveness of Adapter-based Tuning for Pretrained Language Model Adaptation\",\n author = \"He, Ruidan and\n Liu, Linlin and\n Ye, Hai and\n Tan, Qingyu and\n Ding, Bosheng and\n Cheng, Liying and\n Low, Jiawei and\n Bing, Lidong and\n Si, Luo\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.172/\",\n doi = \"10.18653/v1/2021.acl-long.172\",\n pages = \"2208--2222\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.172.pdf", "site": "https://aclanthology.org/2021.acl-long.172/", "pdf_size": 667909, "gs_citation": 224, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=17266049159313375035&as_sdt=5,33&sciodt=0,33&hl=en", "gs_version_total": 4, "aff": "DAMO Academy, Alibaba Group+1; Nanyang Technological University+2; National University of Singapore+3; Singapore University of Technology and Design+4; DAMO Academy, Alibaba Group+1; Nanyang Technological University+2; DAMO Academy, Alibaba Group+1; DAMO Academy, Alibaba Group+1; DAMO Academy, Alibaba Group+1", "aff_domain": "alibaba-inc.com;alibaba-inc.com;comp.nus.edu.sg;alibaba-inc.com;alibaba-inc.com;alibaba-inc.com;alibaba-inc.com;alibaba-inc.com;alibaba-inc.com", "email": "alibaba-inc.com;alibaba-inc.com;comp.nus.edu.sg;alibaba-inc.com;alibaba-inc.com;alibaba-inc.com;alibaba-inc.com;alibaba-inc.com;alibaba-inc.com", "github": "", "project": "", "author_num": 9, "aff_unique_index": "0;2;3;4;0;2;0;0;0", "aff_unique_norm": "Alibaba Group;;Nanyang Technological University;National University of Singapore;Singapore University of Technology and Design", "aff_unique_dep": "DAMO Academy;;;;", "aff_unique_url": "https://www.alibaba-group.com;;https://www.ntu.edu.sg;https://www.nus.edu.sg;https://www.sutd.edu.sg", "aff_unique_abbr": "Alibaba;;NTU;NUS;SUTD", "aff_campus_unique_index": ";;;;;;;;", "aff_campus_unique": "", "aff_country_unique_index": "0;2;2;2;0;2;0;0;0", "aff_country_unique": "China;;Singapore" }, { "id": "2021.acl-long.517", "title": "On the Efficacy of Adversarial Data Collection for Question Answering: Results from a Large-Scale Randomized Study", "track": "main", "status": "Long", "award": false, "abstract": "In adversarial data collection (ADC), a human workforce interacts with a model in real time, attempting to produce examples that elicit incorrect predictions. Researchers hope that models trained on these more challenging datasets will rely less on superficial patterns, and thus be less brittle. However, despite ADC\u2019s intuitive appeal, it remains unclear when training on adversarial datasets produces more robust models. In this paper, we conduct a large-scale controlled study focused on question answering, assigning workers at random to compose questions either (i) adversarially (with a model in the loop); or (ii) in the standard fashion (without a model). Across a variety of models and datasets, we find that models trained on adversarial data usually perform better on other adversarial datasets but worse on a diverse collection of out-of-domain evaluation sets. Finally, we provide a qualitative analysis of adversarial (vs standard) data, identifying key differences and offering guidance for future research.", "author": "Divyansh Kaushik; Douwe Kiela; Zachary C. Lipton; Wen-tau Yih", "authorids": "/d/divyansh-kaushik/; /d/douwe-kiela/; /z/zachary-c-lipton/; /w/wen-tau-yih/", "bibtex": "@inproceedings{kaushik-etal-2021-efficacy,\n title = \"On the Efficacy of Adversarial Data Collection for Question Answering: Results from a Large-Scale Randomized Study\",\n author = \"Kaushik, Divyansh and\n Kiela, Douwe and\n Lipton, Zachary C. and\n Yih, Wen-tau\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.517/\",\n doi = \"10.18653/v1/2021.acl-long.517\",\n pages = \"6618--6633\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.517.pdf", "site": "https://aclanthology.org/2021.acl-long.517/", "pdf_size": 650970, "gs_citation": 42, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=3148530163255757745&as_sdt=5,48&sciodt=0,48&hl=en", "gs_version_total": 4, "aff": "Carnegie Mellon University; Facebook AI Research; Carnegie Mellon University; Facebook AI Research", "aff_domain": "cmu.edu;fb.com;cmu.edu;fb.com", "email": "cmu.edu;fb.com;cmu.edu;fb.com", "github": "https://github.com/facebookresearch/aqa-study", "project": "", "author_num": 4, "aff_unique_index": "0;1;0;1", "aff_unique_norm": "Carnegie Mellon University;Facebook", "aff_unique_dep": ";Facebook AI Research", "aff_unique_url": "https://www.cmu.edu;https://research.facebook.com", "aff_unique_abbr": "CMU;FAIR", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0;0;0;0", "aff_country_unique": "United States" }, { "id": "2021.acl-short.112", "title": "On the Generation of Medical Dialogs for COVID-19", "track": "main", "status": "Short", "award": false, "abstract": "Under the pandemic of COVID-19, people experiencing COVID19-related symptoms have a pressing need to consult doctors. Because of the shortage of medical professionals, many people cannot receive online consultations timely. To address this problem, we aim to develop a medical dialog system that can provide COVID19-related consultations. We collected two dialog datasets \u2013 CovidDialog \u2013 (in English and Chinese respectively) containing conversations between doctors and patients about COVID-19. While the largest of their kind, these two datasets are still relatively small compared with general-domain dialog datasets. Training complex dialog generation models on small datasets bears high risk of overfitting. To alleviate overfitting, we develop a multi-task learning approach, which regularizes the data-deficient dialog generation task with a masked token prediction task. Experiments on the CovidDialog datasets demonstrate the effectiveness of our approach. We perform both human evaluation and automatic evaluation of dialogs generated by our method. Results show that the generated responses are promising in being doctor-like, relevant to conversation history, clinically informative and correct. The code and the data are available at https://github.com/UCSD-AI4H/COVID-Dialogue.", "author": "Meng Zhou; Zechen Li; Bowen Tan; Guangtao Zeng; Wenmian Yang; Xuehai He; Zeqian Ju; Subrato Chakravorty; Shu Chen; Xingyi Yang; Yichen Zhang; Qingyang Wu; Zhou Yu; Kun Xu; Eric Xing; Pengtao Xie", "authorids": "/m/meng-zhou/; /z/zechen-li/; /b/bowen-tan/; /g/guangtao-zeng/; /w/wenmian-yang/; /x/xuehai-he/; /z/zeqian-ju/; /s/subrato-chakravorty/; /s/shu-chen/; /x/xingyi-yang/; /y/yichen-zhang/; /q/qingyang-wu/; /z/zhou-yu/; /k/kun-xu/; /e/eric-xing/; /p/pengtao-xie/", "bibtex": "@inproceedings{zhou-etal-2021-generation,\n title = \"On the Generation of Medical Dialogs for {COVID}-19\",\n author = \"Zhou, Meng and\n Li, Zechen and\n Tan, Bowen and\n Zeng, Guangtao and\n Yang, Wenmian and\n He, Xuehai and\n Ju, Zeqian and\n Chakravorty, Subrato and\n Chen, Shu and\n Yang, Xingyi and\n Zhang, Yichen and\n Wu, Qingyang and\n Yu, Zhou and\n Xu, Kun and\n Xing, Eric and\n Xie, Pengtao\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-short.112/\",\n doi = \"10.18653/v1/2021.acl-short.112\",\n pages = \"886--896\"\n}", "pdf": "https://aclanthology.org/2021.acl-short.112.pdf", "site": "https://aclanthology.org/2021.acl-short.112/", "pdf_size": 270727, "gs_citation": 27, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=8249613921819467416&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 5, "aff": "UC San Diego\u2217; UC San Diego\u2217; CMU\u2020; UC San Diego\u2217; UC San Diego\u2217; UC San Diego\u2217; UC San Diego\u2217; UC San Diego\u2217; UC San Diego\u2217; UC San Diego\u2217; UC San Diego\u2217; UC San Diego\u2217; Columbia University\u22c4; Tencent AI Lab\u2022; CMU\u2020+Mohamed bin Zayed University of Arti\ufb01cial Intelligence\u2021; UC San Diego\u2217", "aff_domain": "eng.ucsd.edu; ; ; ; ; ; ; ; ; ; ; ; ; ; ;eng.ucsd.edu", "email": "eng.ucsd.edu; ; ; ; ; ; ; ; ; ; ; ; ; ; ;eng.ucsd.edu", "github": "https://github.com/UCSD-AI4H/COVID-Dialogue", "project": "", "author_num": 16, "aff_unique_index": "0;0;1;0;0;0;0;0;0;0;0;0;2;3;1+4;0", "aff_unique_norm": "University of California, San Diego;Carnegie Mellon University;Columbia University;Tencent;Mohamed bin Zayed University of Artificial Intelligence", "aff_unique_dep": ";;;Tencent AI Lab;", "aff_unique_url": "https://ucsd.edu;https://www.cmu.edu;https://www.columbia.edu;https://ai.tencent.com;https://mbzuai.ac.ae", "aff_unique_abbr": "UCSD;CMU;Columbia;Tencent AI Lab;MBZUAI", "aff_campus_unique_index": "0;0;0;0;0;0;0;0;0;0;0;;0", "aff_campus_unique": "San Diego;", "aff_country_unique_index": "0;0;0;0;0;0;0;0;0;0;0;0;0;1;0+2;0", "aff_country_unique": "United States;China;United Arab Emirates" }, { "id": "2021.acl-long.354", "title": "One2Set: Generating Diverse Keyphrases as a Set", "track": "main", "status": "Long", "award": false, "abstract": "Recently, the sequence-to-sequence models have made remarkable progress on the task of keyphrase generation (KG) by concatenating multiple keyphrases in a predefined order as a target sequence during training. However, the keyphrases are inherently an unordered set rather than an ordered sequence. Imposing a predefined order will introduce wrong bias during training, which can highly penalize shifts in the order between keyphrases. In this work, we propose a new training paradigm One2Set without predefining an order to concatenate the keyphrases. To fit this paradigm, we propose a novel model that utilizes a fixed set of learned control codes as conditions to generate a set of keyphrases in parallel. To solve the problem that there is no correspondence between each prediction and target during training, we propose a K-step label assignment mechanism via bipartite matching, which greatly increases the diversity and reduces the repetition rate of generated keyphrases. The experimental results on multiple benchmarks demonstrate that our approach significantly outperforms the state-of-the-art methods.", "author": "Jiacheng Ye; Tao Gui; Yichao Luo; Yige Xu; Qi Zhang", "authorids": "/j/jiacheng-ye/; /t/tao-gui/; /y/yichao-luo/; /y/yige-xu/; /q/qi-zhang/", "bibtex": "@inproceedings{ye-etal-2021-one2set,\n title = \"{O}ne2{S}et: {G}enerating Diverse Keyphrases as a Set\",\n author = \"Ye, Jiacheng and\n Gui, Tao and\n Luo, Yichao and\n Xu, Yige and\n Zhang, Qi\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.354/\",\n doi = \"10.18653/v1/2021.acl-long.354\",\n pages = \"4598--4608\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.354.pdf", "site": "https://aclanthology.org/2021.acl-long.354/", "pdf_size": 609101, "gs_citation": 102, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=860186212652515373&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 7, "aff": "School of Computer Science, Fudan University; Institute of Modern Languages and Linguistics, Fudan University; School of Computer Science, Fudan University; School of Computer Science, Fudan University; School of Computer Science, Fudan University", "aff_domain": "fudan.edu.cn;fudan.edu.cn;fudan.edu.cn;fudan.edu.cn;fudan.edu.cn", "email": "fudan.edu.cn;fudan.edu.cn;fudan.edu.cn;fudan.edu.cn;fudan.edu.cn", "github": "", "project": "", "author_num": 5, "aff_unique_index": "0;0;0;0;0", "aff_unique_norm": "Fudan University", "aff_unique_dep": "School of Computer Science", "aff_unique_url": "https://www.fudan.edu.cn", "aff_unique_abbr": "Fudan", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0;0;0;0;0", "aff_country_unique": "China" }, { "id": "2021.acl-long.242", "title": "Online Learning Meets Machine Translation Evaluation: Finding the Best Systems with the Least Human Effort", "track": "main", "status": "Long", "award": false, "abstract": "In Machine Translation, assessing the quality of a large amount of automatic translations can be challenging. Automatic metrics are not reliable when it comes to high performing systems. In addition, resorting to human evaluators can be expensive, especially when evaluating multiple systems. To overcome the latter challenge, we propose a novel application of online learning that, given an ensemble of Machine Translation systems, dynamically converges to the best systems, by taking advantage of the human feedback available. Our experiments on WMT\u201919 datasets show that our online approach quickly converges to the top-3 ranked systems for the language pairs considered, despite the lack of human feedback for many translations.", "author": "V\u00e2nia Mendon\u00e7a; Ricardo Rei; Luisa Coheur; Alberto Sardinha; Ana L\u00facia Santos", "authorids": "/v/vania-mendonca/; /r/ricardo-rei/; /l/luisa-coheur/; /a/alberto-sardinha/; /a/ana-lucia-santos/", "bibtex": "@inproceedings{mendonca-etal-2021-online,\n title = \"{O}nline {L}earning Meets {M}achine {T}ranslation Evaluation: Finding the Best Systems with the Least Human Effort\",\n author = \"Mendon{\\c{c}}a, V{\\^a}nia and\n Rei, Ricardo and\n Coheur, Luisa and\n Sardinha, Alberto and\n Santos, Ana L{\\'u}cia\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.242/\",\n doi = \"10.18653/v1/2021.acl-long.242\",\n pages = \"3105--3117\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.242.pdf", "site": "https://aclanthology.org/2021.acl-long.242/", "pdf_size": 3467446, "gs_citation": 5, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=7022254873352035435&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 9, "aff": "INESC-ID Lisboa, Portugal + Instituto Superior T\u00e9cnico, Universidade de Lisboa, Portugal + Unbabel AI, Lisboa, Portugal; INESC-ID Lisboa, Portugal + Instituto Superior T\u00e9cnico, Universidade de Lisboa, Portugal + Unbabel AI, Lisboa, Portugal; INESC-ID Lisboa, Portugal + Instituto Superior T\u00e9cnico, Universidade de Lisboa, Portugal; Centro de Lingu\u00edstica da Universidade de Lisboa, Portugal + Faculdade de Letras da Universidade de Lisboa, Portugal; Centro de Lingu\u00edstica da Universidade de Lisboa, Portugal + Faculdade de Letras da Universidade de Lisboa, Portugal", "aff_domain": "tecnico.ulisboa.pt;unbabel.com;tecnico.ulisboa.pt;tecnico.ulisboa.pt;letras.ulisboa.pt", "email": "tecnico.ulisboa.pt;unbabel.com;tecnico.ulisboa.pt;tecnico.ulisboa.pt;letras.ulisboa.pt", "github": "https://github.com/vania-mendonca/MTOL", "project": "", "author_num": 5, "aff_unique_index": "0+1+2;0+1+2;0+1;1+1;1+1", "aff_unique_norm": "INESC-ID;Universidade de Lisboa;Unbabel AI", "aff_unique_dep": ";Instituto Superior T\u00e9cnico;", "aff_unique_url": "https://www.inesc-id.pt;https://www IST.utl.pt;https://www.unbabel.com", "aff_unique_abbr": "INESC-ID;IST;", "aff_campus_unique_index": "0+0;0+0;0+0;;", "aff_campus_unique": "Lisboa;", "aff_country_unique_index": "0+0+0;0+0+0;0+0;0+0;0+0", "aff_country_unique": "Portugal" }, { "id": "2021.acl-long.220", "title": "OntoED: Low-resource Event Detection with Ontology Embedding", "track": "main", "status": "Long", "award": false, "abstract": "Event Detection (ED) aims to identify event trigger words from a given text and classify it into an event type. Most current methods to ED rely heavily on training instances, and almost ignore the correlation of event types. Hence, they tend to suffer from data scarcity and fail to handle new unseen event types. To address these problems, we formulate ED as a process of event ontology population: linking event instances to pre-defined event types in event ontology, and propose a novel ED framework entitled OntoED with ontology embedding. We enrich event ontology with linkages among event types, and further induce more event-event correlations. Based on the event ontology, OntoED can leverage and propagate correlation knowledge, particularly from data-rich to data-poor event types. Furthermore, OntoED can be applied to new unseen event types, by establishing linkages to existing ones. Experiments indicate that OntoED is more predominant and robust than previous approaches to ED, especially in data-scarce scenarios.", "author": "Shumin Deng; Ningyu Zhang; Luoqiu Li; Chen Hui; Tou Huaixiao; Mosha Chen; Fei Huang; Huajun Chen", "authorids": "/s/shumin-deng/; /n/ningyu-zhang/; /l/luoqiu-li/; /c/chen-hui/; /t/tou-huaixiao/; /m/mosha-chen/; /f/fei-huang/; /h/huajun-chen/", "bibtex": "@inproceedings{deng-etal-2021-ontoed,\n title = \"{O}nto{ED}: Low-resource Event Detection with Ontology Embedding\",\n author = \"Deng, Shumin and\n Zhang, Ningyu and\n Li, Luoqiu and\n Hui, Chen and\n Huaixiao, Tou and\n Chen, Mosha and\n Huang, Fei and\n Chen, Huajun\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.220/\",\n doi = \"10.18653/v1/2021.acl-long.220\",\n pages = \"2828--2839\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.220.pdf", "site": "https://aclanthology.org/2021.acl-long.220/", "pdf_size": 2004574, "gs_citation": 76, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=13018415867098383561&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 7, "aff": "Zhejiang University & AZFT Joint Lab for Knowledge Engine, China+Hangzhou Innovation Center, Zhejiang University, China; Zhejiang University & AZFT Joint Lab for Knowledge Engine, China+Hangzhou Innovation Center, Zhejiang University, China; Zhejiang University & AZFT Joint Lab for Knowledge Engine, China+Hangzhou Innovation Center, Zhejiang University, China; Alibaba Group, China; Alibaba Group, China; Alibaba Group, China; Alibaba Group, China; Zhejiang University & AZFT Joint Lab for Knowledge Engine, China+Hangzhou Innovation Center, Zhejiang University, China", "aff_domain": "zju.edu.cn;zju.edu.cn;zju.edu.cn;alibaba-inc.com;alibaba-inc.com;alibaba-inc.com;alibaba-inc.com;zju.edu.cn", "email": "zju.edu.cn;zju.edu.cn;zju.edu.cn;alibaba-inc.com;alibaba-inc.com;alibaba-inc.com;alibaba-inc.com;zju.edu.cn", "github": "", "project": "", "author_num": 8, "aff_unique_index": "0+0;0+0;0+0;1;1;1;1;0+0", "aff_unique_norm": "Zhejiang University;Alibaba Group", "aff_unique_dep": "Joint Lab for Knowledge Engine;", "aff_unique_url": "http://www.zju.edu.cn;https://www.alibaba.com", "aff_unique_abbr": "ZJU;Alibaba", "aff_campus_unique_index": "1;1;1;1", "aff_campus_unique": ";Hangzhou", "aff_country_unique_index": "0+0;0+0;0+0;0;0;0;0;0+0", "aff_country_unique": "China" }, { "id": "2021.acl-short.59", "title": "OntoGUM: Evaluating Contextualized SOTA Coreference Resolution on 12 More Genres", "track": "main", "status": "Short", "award": false, "abstract": "SOTA coreference resolution produces increasingly impressive scores on the OntoNotes benchmark. However lack of comparable data following the same scheme for more genres makes it difficult to evaluate generalizability to open domain data. This paper provides a dataset and comprehensive evaluation showing that the latest neural LM based end-to-end systems degrade very substantially out of domain. We make an OntoNotes-like coreference dataset called OntoGUM publicly available, converted from GUM, an English corpus covering 12 genres, using deterministic rules, which we evaluate. Thanks to the rich syntactic and discourse annotations in GUM, we are able to create the largest human-annotated coreference corpus following the OntoNotes guidelines, and the first to be evaluated for consistency with the OntoNotes scheme. Out-of-domain evaluation across 12 genres shows nearly 15-20% degradation for both deterministic and deep learning systems, indicating a lack of generalizability or covert overfitting in existing coreference resolution models.", "author": "Yilun Zhu; Sameer Pradhan; Amir Zeldes", "authorids": "/y/yilun-zhu/; /s/sameer-pradhan/; /a/amir-zeldes/", "bibtex": "@inproceedings{zhu-etal-2021-ontogum,\n title = \"{O}nto{GUM}: Evaluating Contextualized {SOTA} Coreference Resolution on 12 More Genres\",\n author = \"Zhu, Yilun and\n Pradhan, Sameer and\n Zeldes, Amir\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-short.59/\",\n doi = \"10.18653/v1/2021.acl-short.59\",\n pages = \"461--467\"\n}", "pdf": "https://aclanthology.org/2021.acl-short.59.pdf", "site": "https://aclanthology.org/2021.acl-short.59/", "pdf_size": 220320, "gs_citation": 24, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=12975715365512713650&as_sdt=5,33&sciodt=0,33&hl=en", "gs_version_total": 6, "aff": "Department of Linguistics, Georgetown University; Linguistic Data Consortium, University of Pennsylvania + cemantix.org; Department of Linguistics, Georgetown University", "aff_domain": "georgetown.edu;cemantix.org;georgetown.edu", "email": "georgetown.edu;cemantix.org;georgetown.edu", "github": "", "project": "", "author_num": 3, "aff_unique_index": "0;1+2;0", "aff_unique_norm": "Georgetown University;University of Pennsylvania;Cemantix", "aff_unique_dep": "Department of Linguistics;Linguistic Data Consortium;", "aff_unique_url": "https://www.georgetown.edu;https://www.upenn.edu;http://www.cemantix.org", "aff_unique_abbr": "Georgetown;UPenn;", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0;0;0", "aff_country_unique": "United States;" }, { "id": "2021.acl-long.49", "title": "OoMMix: Out-of-manifold Regularization in Contextual Embedding Space for Text Classification", "track": "main", "status": "Long", "award": false, "abstract": "Recent studies on neural networks with pre-trained weights (i.e., BERT) have mainly focused on a low-dimensional subspace, where the embedding vectors computed from input words (or their contexts) are located. In this work, we propose a new approach, called OoMMix, to finding and regularizing the remainder of the space, referred to as out-of-manifold, which cannot be accessed through the words. Specifically, we synthesize the out-of-manifold embeddings based on two embeddings obtained from actually-observed words, to utilize them for fine-tuning the network. A discriminator is trained to detect whether an input embedding is located inside the manifold or not, and simultaneously, a generator is optimized to produce new embeddings that can be easily identified as out-of-manifold by the discriminator. These two modules successfully collaborate in a unified and end-to-end manner for regularizing the out-of-manifold. Our extensive evaluation on various text classification benchmarks demonstrates the effectiveness of our approach, as well as its good compatibility with existing data augmentation techniques which aim to enhance the manifold.", "author": "Seonghyeon Lee; Dongha Lee; Hwanjo Yu", "authorids": "/s/seonghyeon-lee/; /d/dongha-lee/; /h/hwanjo-yu/", "bibtex": "@inproceedings{lee-etal-2021-oommix,\n title = \"{O}o{MM}ix: Out-of-manifold Regularization in Contextual Embedding Space for Text Classification\",\n author = \"Lee, Seonghyeon and\n Lee, Dongha and\n Yu, Hwanjo\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.49/\",\n doi = \"10.18653/v1/2021.acl-long.49\",\n pages = \"590--599\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.49.pdf", "site": "https://aclanthology.org/2021.acl-long.49/", "pdf_size": 2679581, "gs_citation": 4, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=17530605055667071267&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 3, "aff": "Dept. of Computer Science and Engineering, POSTECH, Republic of Korea; Institute of Artificial Intelligence, POSTECH, Republic of Korea; Dept. of Computer Science and Engineering, POSTECH, Republic of Korea", "aff_domain": "postech.ac.kr;postech.ac.kr;postech.ac.kr", "email": "postech.ac.kr;postech.ac.kr;postech.ac.kr", "github": "", "project": "", "author_num": 3, "aff_unique_index": "0;0;0", "aff_unique_norm": "POSTECH", "aff_unique_dep": "Dept. of Computer Science and Engineering", "aff_unique_url": "https://www.postech.ac.kr", "aff_unique_abbr": "POSTECH", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0;0;0", "aff_country_unique": "Republic of Korea" }, { "id": "2021.acl-long.500", "title": "OpenMEVA: A Benchmark for Evaluating Open-ended Story Generation Metrics", "track": "main", "status": "Long", "award": false, "abstract": "Automatic metrics are essential for developing natural language generation (NLG) models, particularly for open-ended language generation tasks such as story generation. However, existing automatic metrics are observed to correlate poorly with human evaluation. The lack of standardized benchmark datasets makes it difficult to fully evaluate the capabilities of a metric and fairly compare different metrics. Therefore, we propose OpenMEVA, a benchmark for evaluating open-ended story generation metrics. OpenMEVA provides a comprehensive test suite to assess the capabilities of metrics, including (a) the correlation with human judgments, (b) the generalization to different model outputs and datasets, (c) the ability to judge story coherence, and (d) the robustness to perturbations. To this end, OpenMEVA includes both manually annotated stories and auto-constructed test examples. We evaluate existing metrics on OpenMEVA and observe that they have poor correlation with human judgments, fail to recognize discourse-level incoherence, and lack inferential knowledge (e.g., causal order between events), the generalization ability and robustness. Our study presents insights for developing NLG models and metrics in further research.", "author": "Jian Guan; Zhexin Zhang; Zhuoer Feng; Zitao Liu; Wenbiao Ding; Xiaoxi Mao; Changjie Fan; Minlie Huang", "authorids": "/j/jian-guan/; /z/zhexin-zhang/; /z/zhuoer-feng/; /z/zitao-liu/; /w/wenbiao-ding/; /x/xiaoxi-mao/; /c/changjie-fan/; /m/minlie-huang/", "bibtex": "@inproceedings{guan-etal-2021-openmeva,\n title = \"{O}pen{MEVA}: A Benchmark for Evaluating Open-ended Story Generation Metrics\",\n author = \"Guan, Jian and\n Zhang, Zhexin and\n Feng, Zhuoer and\n Liu, Zitao and\n Ding, Wenbiao and\n Mao, Xiaoxi and\n Fan, Changjie and\n Huang, Minlie\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.500/\",\n doi = \"10.18653/v1/2021.acl-long.500\",\n pages = \"6394--6407\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.500.pdf", "site": "https://aclanthology.org/2021.acl-long.500/", "pdf_size": 3016092, "gs_citation": 60, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=476903992688144141&as_sdt=5,33&sciodt=0,33&hl=en", "gs_version_total": 7, "aff": "The CoAI group, DCST+Institute for Artificial Intelligence+State Key Lab of Intelligent Technology and Systems+Beijing National Research Center for Information Science and Technology+Tsinghua University, Beijing 100084, China; The CoAI group, DCST+Institute for Artificial Intelligence+State Key Lab of Intelligent Technology and Systems+Beijing National Research Center for Information Science and Technology+Tsinghua University, Beijing 100084, China; The CoAI group, DCST+Institute for Artificial Intelligence+State Key Lab of Intelligent Technology and Systems+Beijing National Research Center for Information Science and Technology+Tsinghua University, Beijing 100084, China; TAL Education Group; TAL Education Group; Netease Fuxi AI Lab; Netease Fuxi AI Lab; The CoAI group, DCST+Institute for Artificial Intelligence+State Key Lab of Intelligent Technology and Systems+Beijing National Research Center for Information Science and Technology+Tsinghua University, Beijing 100084, China", "aff_domain": "mails.tsinghua.edu.cn;mails.tsinghua.edu.cn;mails.tsinghua.edu.cn;gmail.com;100tal.com;corp.netease.com;corp.netease.com;tsinghua.edu.cn", "email": "mails.tsinghua.edu.cn;mails.tsinghua.edu.cn;mails.tsinghua.edu.cn;gmail.com;100tal.com;corp.netease.com;corp.netease.com;tsinghua.edu.cn", "github": "", "project": "", "author_num": 8, "aff_unique_index": "0+1+2+3+4;0+1+2+3+4;0+1+2+3+4;5;5;6;6;0+1+2+3+4", "aff_unique_norm": "DCST;Institute for Artificial Intelligence;State Key Lab of Intelligent Technology and Systems;Beijing National Research Center for Information Science and Technology;Tsinghua University;TAL Education Group;Netease", "aff_unique_dep": "The CoAI group;Artificial Intelligence;;;;;Fuxi AI Lab", "aff_unique_url": ";;;;https://www.tsinghua.edu.cn;https://www.tal.com;https://www.netease.com", "aff_unique_abbr": ";;;;THU;TAL;Netease", "aff_campus_unique_index": "1;1;1;1", "aff_campus_unique": ";Beijing", "aff_country_unique_index": "1+2+2+2;1+2+2+2;1+2+2+2;2;2;2;2;1+2+2+2", "aff_country_unique": ";United States;China" }, { "id": "2021.acl-long.163", "title": "Optimizing Deeper Transformers on Small Datasets", "track": "main", "status": "Long", "award": false, "abstract": "It is a common belief that training deep transformers from scratch requires large datasets. Consequently, for small datasets, people usually use shallow and simple additional layers on top of pre-trained models during fine-tuning. This work shows that this does not always need to be the case: with proper initialization and optimization, the benefits of very deep transformers can carry over to challenging tasks with small datasets, including Text-to-SQL semantic parsing and logical reading comprehension. In particular, we successfully train 48 layers of transformers, comprising 24 fine-tuned layers from pre-trained RoBERTa and 24 relation-aware layers trained from scratch. With fewer training steps and no task-specific pre-training, we obtain the state of the art performance on the challenging cross-domain Text-to-SQL parsing benchmark Spider. We achieve this by deriving a novel Data dependent Transformer Fixed-update initialization scheme (DT-Fixup), inspired by the prior T-Fixup work. Further error analysis shows that increasing depth can help improve generalization on small datasets for hard cases that require reasoning and structural understanding.", "author": "Peng Xu; Dhruv Kumar; Wei Yang; Wenjie Zi; Keyi Tang; Chenyang Huang; Jackie Chi Kit Cheung; Simon J.D. Prince; Yanshuai Cao", "authorids": "/p/peng-xu/; /d/dhruv-kumar/; /w/wei-yang/; /w/wenjie-zi/; /k/keyi-tang/; /c/chenyang-huang/; /j/jackie-chi-kit-cheung/; /s/simon-j-d-prince/; /y/yanshuai-cao/", "bibtex": "@inproceedings{xu-etal-2021-optimizing,\n title = \"Optimizing Deeper Transformers on Small Datasets\",\n author = \"Xu, Peng and\n Kumar, Dhruv and\n Yang, Wei and\n Zi, Wenjie and\n Tang, Keyi and\n Huang, Chenyang and\n Cheung, Jackie Chi Kit and\n Prince, Simon J.D. and\n Cao, Yanshuai\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.163/\",\n doi = \"10.18653/v1/2021.acl-long.163\",\n pages = \"2089--2102\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.163.pdf", "site": "https://aclanthology.org/2021.acl-long.163/", "pdf_size": 575332, "gs_citation": 70, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=1962236217502921399&as_sdt=5,33&sciodt=0,33&hl=en", "gs_version_total": 7, "aff": "Borealis AI+University of Waterloo+McGill University+Canada CIFAR Chair, Mila+University of Alberta; Borealis AI+University of Waterloo+McGill University+Canada CIFAR Chair, Mila+University of Alberta; Borealis AI; Borealis AI; Borealis AI; Borealis AI+University of Alberta; Borealis AI+McGill University+Canada CIFAR Chair, Mila; Borealis AI; Borealis AI", "aff_domain": "borealisai.com;borealisai.com;borealisai.com;borealisai.com;borealisai.com;borealisai.com;uwaterloo.ca;ualberta.ca;cs.mcgill.ca", "email": "borealisai.com;borealisai.com;borealisai.com;borealisai.com;borealisai.com;borealisai.com;uwaterloo.ca;ualberta.ca;cs.mcgill.ca", "github": "https://github.com/BorealisAI/DT-Fixup", "project": "", "author_num": 9, "aff_unique_index": "0+1+2+3+4;0+1+2+3+4;0;0;0;0+4;0+2+3;0;0", "aff_unique_norm": "Borealis AI;University of Waterloo;McGill University;Mila;University of Alberta", "aff_unique_dep": ";;;Canada CIFAR Chair;", "aff_unique_url": "https://www.borealisai.com;https://uwaterloo.ca;https://www.mcgill.ca;https://mila.quebec;https://www.ualberta.ca", "aff_unique_abbr": "Borealis AI;UW;McGill;Mila;UAlberta", "aff_campus_unique_index": ";;;", "aff_campus_unique": "", "aff_country_unique_index": "0+0+0+0+0;0+0+0+0+0;0;0;0;0+0;0+0+0;0;0", "aff_country_unique": "Canada" }, { "id": "2021.acl-long.273", "title": "Out-of-Scope Intent Detection with Self-Supervision and Discriminative Training", "track": "main", "status": "Long", "award": false, "abstract": "Out-of-distribution (OOD) intent detection is of practical importance in task-oriented dialogue systems. Since the distribution of outlier utterances is arbitrary and unknown in the training stage, existing methods commonly rely on strong assumptions on data distribution such as mixture of Gaussians to make inference, resulting in either complex multi-step training procedures or hand-crafted rules such as confidence threshold selection for outlier detection.In this paper, we propose a simple yet effective method to train an OOD intent classifier in a fully end-to-end manner by simulating the test scenario in training, which requires no assumption on data distribution and no additional post-processing or threshold setting. Specifically, we construct a set of pseudo outliers in the training stage, by generating synthetic outliers using inliner features via self-supervision and sampling OOD sentences from easily available open-domain datasets. The pseudo outliers are used to train a discriminative classifier that can be directly applied to and generalize well on the test task. We evaluate our method extensively on four benchmark dialogue datasets and observe significant improvements over state-of-the-art approaches.The source code has been made available at \\url{https://github.com/liam0949/DCLOOS}.", "author": "Li-Ming Zhan; Haowen Liang; Bo Liu; Lu Fan; Albert Y.S. Lam; Xiao-Ming Wu", "authorids": "/l/li-ming-zhan/; /h/haowen-liang/; /b/bo-liu/; /l/lu-fan/; /a/albert-y-s-lam/; /x/xiao-ming-wu/", "bibtex": "@inproceedings{zhan-etal-2021-scope,\n title = \"Out-of-Scope Intent Detection with Self-Supervision and Discriminative Training\",\n author = \"Zhan, Li-Ming and\n Liang, Haowen and\n Liu, Bo and\n Fan, Lu and\n Lam, Albert Y.S. and\n Wu, Xiao-Ming\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.273/\",\n doi = \"10.18653/v1/2021.acl-long.273\",\n pages = \"3521--3532\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.273.pdf", "site": "https://aclanthology.org/2021.acl-long.273/", "pdf_size": 653105, "gs_citation": 77, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=13078428699608452477&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 9, "aff": "Department of Computing, The Hong Kong Polytechnic University, Hong Kong S.A.R.+Fano Labs, Hong Kong S.A.R.; Department of Computing, The Hong Kong Polytechnic University, Hong Kong S.A.R.+Fano Labs, Hong Kong S.A.R.; Department of Computing, The Hong Kong Polytechnic University, Hong Kong S.A.R.+Fano Labs, Hong Kong S.A.R.; Department of Computing, The Hong Kong Polytechnic University, Hong Kong S.A.R.; Fano Labs, Hong Kong S.A.R.; Department of Computing, The Hong Kong Polytechnic University, Hong Kong S.A.R.", "aff_domain": "connect.polyu.edu.hk;connect.polyu.edu.hk;connect.polyu.edu.hk;comp.polyu.edu.hk;fano.ai;comp.polyu.edu.hk", "email": "connect.polyu.edu.hk;connect.polyu.edu.hk;connect.polyu.edu.hk;comp.polyu.edu.hk;fano.ai;comp.polyu.edu.hk", "github": "https://github.com/liam0949/DCLOOS", "project": "", "author_num": 6, "aff_unique_index": "0+1;0+1;0+1;0;1;0", "aff_unique_norm": "The Hong Kong Polytechnic University;Fano Labs", "aff_unique_dep": "Department of Computing;", "aff_unique_url": "https://www.polyu.edu.hk;https://www.fanolabs.com", "aff_unique_abbr": "PolyU;", "aff_campus_unique_index": "0;0;0;0;0", "aff_campus_unique": "Hong Kong;", "aff_country_unique_index": "0+1;0+1;0+1;0;1;0", "aff_country_unique": "Hong Kong S.A.R.;China" }, { "id": "2021.acl-long.30", "title": "PASS: Perturb-and-Select Summarizer for Product Reviews", "track": "main", "status": "Long", "award": false, "abstract": "The product reviews summarization task aims to automatically produce a short summary for a set of reviews of a given product. Such summaries are expected to aggregate a range of different opinions in a concise, coherent and informative manner. This challenging task gives rise to two shortcomings in existing work. First, summarizers tend to favor generic content that appears in reviews for many different products, resulting in template-like, less informative summaries. Second, as reviewers often disagree on the pros and cons of a given product, summarizers sometimes yield inconsistent, self-contradicting summaries. We propose the PASS system (Perturb-and-Select Summarizer) that employs a large pre-trained Transformer-based model (T5 in our case), which follows a few-shot fine-tuning scheme. A key component of the PASS system relies on applying systematic perturbations to the model\u2019s input during inference, which allows it to generate multiple different summaries per product. We develop a method for ranking these summaries according to desired criteria, coherence in our case, enabling our system to almost entirely avoid the problem of self-contradiction. We compare our system against strong baselines on publicly available datasets, and show that it produces summaries which are more informative, diverse and coherent.", "author": "Nadav Oved; Ran Levy", "authorids": "/n/nadav-oved/; /r/ran-levy/", "bibtex": "@inproceedings{oved-levy-2021-pass,\n title = \"{PASS}: Perturb-and-Select Summarizer for Product Reviews\",\n author = \"Oved, Nadav and\n Levy, Ran\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.30/\",\n doi = \"10.18653/v1/2021.acl-long.30\",\n pages = \"351--365\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.30.pdf", "site": "https://aclanthology.org/2021.acl-long.30/", "pdf_size": 457590, "gs_citation": 31, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=378839578459096269&as_sdt=800005&sciodt=0,15&hl=en", "gs_version_total": 4, "aff": "Technion - Israel Institute of Technology; Amazon", "aff_domain": "campus.technion.ac.il;amazon.com", "email": "campus.technion.ac.il;amazon.com", "github": "", "project": "https://registry.opendata.aws/reviews", "author_num": 2, "aff_unique_index": "0;1", "aff_unique_norm": "Technion - Israel Institute of Technology;Amazon.com, Inc.", "aff_unique_dep": ";", "aff_unique_url": "https://www.technion.ac.il/en/;https://www.amazon.com", "aff_unique_abbr": "Technion;Amazon", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0;1", "aff_country_unique": "Israel;United States" }, { "id": "2021.acl-long.7", "title": "PENS: A Dataset and Generic Framework for Personalized News Headline Generation", "track": "main", "status": "Long", "award": false, "abstract": "In this paper, we formulate the personalized news headline generation problem whose goal is to output a user-specific title based on both a user\u2019s reading interests and a candidate news body to be exposed to her. To build up a benchmark for this problem, we publicize a large-scale dataset named PENS (PErsonalized News headlineS). The training set is collected from user impressions logs of Microsoft News, and the test set is manually created by hundreds of native speakers to enable a fair testbed for evaluating models in an offline mode. We propose a generic framework as a preparatory solution to our problem. At its heart, user preference is learned by leveraging the user behavioral data, and three kinds of user preference injections are proposed to personalize a text generator and establish personalized headlines. We investigate our dataset by implementing several state-of-the-art user modeling methods in our framework to demonstrate a benchmark score for the proposed dataset. The dataset is available at https://msnews.github.io/pens.html.", "author": "Xiang Ao; Xiting Wang; Ling Luo; Ying Qiao; Qing He; Xing Xie", "authorids": "/x/xiang-ao/; /x/xiting-wang/; /l/ling-luo/; /y/ying-qiao/; /q/qing-he/; /x/xing-xie/", "bibtex": "@inproceedings{ao-etal-2021-pens,\n title = \"{PENS}: A Dataset and Generic Framework for Personalized News Headline Generation\",\n author = \"Ao, Xiang and\n Wang, Xiting and\n Luo, Ling and\n Qiao, Ying and\n He, Qing and\n Xie, Xing\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.7/\",\n doi = \"10.18653/v1/2021.acl-long.7\",\n pages = \"82--92\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.7.pdf", "site": "https://aclanthology.org/2021.acl-long.7/", "pdf_size": 584995, "gs_citation": 63, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=11098457584717280688&as_sdt=80000005&sciodt=0,23&hl=en", "gs_version_total": 3, "aff": "Key Lab of Intelligent Information Processing of Chinese Academy of Sciences (CAS), Institute of Computing Technology, CAS, Beijing 100190, China+University of Chinese Academy of Sciences, Beijing 100049, China; Microsoft Research Asia; Microsoft; Key Lab of Intelligent Information Processing of Chinese Academy of Sciences (CAS), Institute of Computing Technology, CAS, Beijing 100190, China+University of Chinese Academy of Sciences, Beijing 100049, China; Key Lab of Intelligent Information Processing of Chinese Academy of Sciences (CAS), Institute of Computing Technology, CAS, Beijing 100190, China+University of Chinese Academy of Sciences, Beijing 100049, China; Microsoft Research Asia\u2020", "aff_domain": "ict.ac.cn;microsoft.com;ict.ac.cn;microsoft.com;ict.ac.cn;microsoft.com", "email": "ict.ac.cn;microsoft.com;ict.ac.cn;microsoft.com;ict.ac.cn;microsoft.com", "github": "", "project": "https://msnews.github.io/pens.html", "author_num": 6, "aff_unique_index": "0+1;2;3;0+1;0+1;4", "aff_unique_norm": "Chinese Academy of Sciences;University of Chinese Academy of Sciences;Microsoft Research;Microsoft Corporation;Microsoft Research Asia", "aff_unique_dep": "Institute of Computing Technology;;Research;;Microsoft Research", "aff_unique_url": "http://www.cas.ac.cn;http://www.ucas.ac.cn;https://www.microsoft.com/en-us/research/group/asia;https://www.microsoft.com;https://www.microsoft.com/en-us/research/group/asia", "aff_unique_abbr": "CAS;UCAS;MSR Asia;Microsoft;MSRA", "aff_campus_unique_index": "0+0;1;0+0;0+0", "aff_campus_unique": "Beijing;Asia;", "aff_country_unique_index": "0+0;0;1;0+0;0+0;0", "aff_country_unique": "China;United States" }, { "id": "2021.acl-long.464", "title": "PHMOSpell: Phonological and Morphological Knowledge Guided Chinese Spelling Check", "track": "main", "status": "Long", "award": false, "abstract": "Chinese Spelling Check (CSC) is a challenging task due to the complex characteristics of Chinese characters. Statistics reveal that most Chinese spelling errors belong to phonological or visual errors. However, previous methods rarely utilize phonological and morphological knowledge of Chinese characters or heavily rely on external resources to model their similarities. To address the above issues, we propose a novel end-to-end trainable model called PHMOSpell, which promotes the performance of CSC with multi-modal information. Specifically, we derive pinyin and glyph representations for Chinese characters from audio and visual modalities respectively, which are integrated into a pre-trained language model by a well-designed adaptive gating mechanism. To verify its effectiveness, we conduct comprehensive experiments and ablation tests. Experimental results on three shared benchmarks demonstrate that our model consistently outperforms previous state-of-the-art models.", "author": "Li Huang; Junjie Li; Weiwei Jiang; Zhiyu Zhang; Minchuan Chen; Shaojun Wang; Jing Xiao", "authorids": "/l/li-huang/; /j/junjie-li/; /w/weiwei-jiang/; /z/zhiyu-zhang/; /m/minchuan-chen/; /s/shaojun-wang/; /j/jing-xiao/", "bibtex": "@inproceedings{huang-etal-2021-phmospell,\n title = \"{PHMOS}pell: Phonological and Morphological Knowledge Guided {C}hinese Spelling Check\",\n author = \"Huang, Li and\n Li, Junjie and\n Jiang, Weiwei and\n Zhang, Zhiyu and\n Chen, Minchuan and\n Wang, Shaojun and\n Xiao, Jing\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.464/\",\n doi = \"10.18653/v1/2021.acl-long.464\",\n pages = \"5958--5967\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.464.pdf", "site": "https://aclanthology.org/2021.acl-long.464/", "pdf_size": 941571, "gs_citation": 77, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=8090133582438004461&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 4, "aff": "Fudan University+Ping An Technology; Ping An Technology; Ping An Technology; Ping An Technology; Ping An Technology; Ping An Technology; Ping An Technology", "aff_domain": "gmail.com;gmail.com; ; ; ; ; ", "email": "gmail.com;gmail.com; ; ; ; ; ", "github": "", "project": "", "author_num": 7, "aff_unique_index": "0+1;1;1;1;1;1;1", "aff_unique_norm": "Fudan University;Ping An Technology", "aff_unique_dep": ";", "aff_unique_url": "https://www.fudan.edu.cn;https://www.pingan.com", "aff_unique_abbr": "Fudan;Ping An", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0+0;0;0;0;0;0;0", "aff_country_unique": "China" }, { "id": "2021.acl-long.159", "title": "PIGLeT: Language Grounding Through Neuro-Symbolic Interaction in a 3D World", "track": "main", "status": "Long", "award": false, "abstract": "We propose PIGLeT: a model that learns physical commonsense knowledge through interaction, and then uses this knowledge to ground language. We factorize PIGLeT into a physical dynamics model, and a separate language model. Our dynamics model learns not just what objects are but also what they do: glass cups break when thrown, plastic ones don\u2019t. We then use it as the interface to our language model, giving us a unified model of linguistic form and grounded meaning. PIGLeT can read a sentence, simulate neurally what might happen next, and then communicate that result through a literal symbolic representation, or natural language. Experimental results show that our model effectively learns world dynamics, along with how to communicate them. It is able to correctly forecast what happens next given an English sentence over 80% of the time, outperforming a 100x larger, text-to-text approach by over 10%. Likewise, its natural language summaries of physical interactions are also judged by humans as more accurate than LM alternatives. We present comprehensive analysis showing room for future work.", "author": "Rowan Zellers; Ari Holtzman; Matthew Peters; Roozbeh Mottaghi; Aniruddha Kembhavi; Ali Farhadi; Yejin Choi", "authorids": "/r/rowan-zellers/; /a/ari-holtzman/; /m/matthew-e-peters/; /r/roozbeh-mottaghi/; /a/aniruddha-kembhavi/; /a/ali-farhadi/; /y/yejin-choi/", "bibtex": "https://aclanthology.org/2021.acl-long.159.bib", "pdf": "https://aclanthology.org/2021.acl-long.159.pdf", "site": "https://aclanthology.org/2021.acl-long.159/", "gs_citation": 81, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=9518304828855473860&as_sdt=5,33&sciodt=0,33&hl=en", "gs_version_total": 6, "aff": "Paul G. Allen School of Computer Science & Engineering, University of Washington; Allen Institute for Artificial Intelligence; Allen Institute for Artificial Intelligence; Allen Institute for Artificial Intelligence; Allen Institute for Artificial Intelligence; Allen Institute for Artificial Intelligence; Paul G. Allen School of Computer Science & Engineering, University of Washington", "aff_domain": "gmail.com;westlake.edu.cn;gmail.com;wias.org.cn; ; ; ", "email": "gmail.com;westlake.edu.cn;gmail.com;wias.org.cn; ; ; ", "github": "https://rowanzellers.com/piglet", "project": "", "author_num": 7, "aff_unique_index": "0;1;1;1;1;1;0", "aff_unique_norm": "University of Washington;Allen Institute for Artificial Intelligence", "aff_unique_dep": "Paul G. Allen School of Computer Science & Engineering;", "aff_unique_url": "https://www.washington.edu;https://allenai.org", "aff_unique_abbr": "UW;AI2", "aff_campus_unique_index": "0;0", "aff_campus_unique": "Seattle;", "aff_country_unique_index": "0;0;0;0;0;0;0", "aff_country_unique": "United States" }, { "id": "2021.acl-long.233", "title": "PLOME: Pre-training with Misspelled Knowledge for Chinese Spelling Correction", "track": "main", "status": "Long", "award": false, "abstract": "Chinese spelling correction (CSC) is a task to detect and correct spelling errors in texts. CSC is essentially a linguistic problem, thus the ability of language understanding is crucial to this task. In this paper, we propose a Pre-trained masked Language model with Misspelled knowledgE (PLOME) for CSC, which jointly learns how to understand language and correct spelling errors. To this end, PLOME masks the chosen tokens with similar characters according to a confusion set rather than the fixed token \u201c[MASK]\u201d as in BERT. Besides character prediction, PLOME also introduces pronunciation prediction to learn the misspelled knowledge on phonic level. Moreover, phonological and visual similarity knowledge is important to this task. PLOME utilizes GRU networks to model such knowledge based on characters\u2019 phonics and strokes. Experiments are conducted on widely used benchmarks. Our method achieves superior performance against state-of-the-art approaches by a remarkable margin. We release the source code and pre-trained model for further use by the community (https://github.com/liushulinle/PLOME).", "author": "Shulin Liu; Tao Yang; Tianchi Yue; Feng Zhang; Di Wang", "authorids": "/s/shulin-liu/; /t/tao-yang/; /t/tianchi-yue/; /f/feng-zhang/; /d/di-wang/", "bibtex": "@inproceedings{liu-etal-2021-plome,\n title = \"{PLOME}: Pre-training with Misspelled Knowledge for {C}hinese Spelling Correction\",\n author = \"Liu, Shulin and\n Yang, Tao and\n Yue, Tianchi and\n Zhang, Feng and\n Wang, Di\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.233/\",\n doi = \"10.18653/v1/2021.acl-long.233\",\n pages = \"2991--3000\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.233.pdf", "site": "https://aclanthology.org/2021.acl-long.233/", "pdf_size": 678722, "gs_citation": 124, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=11025706676209066973&as_sdt=5,47&sciodt=0,47&hl=en", "gs_version_total": 3, "aff": "Tencent AI Platform Department, China; Tencent AI Platform Department, China; Tencent AI Platform Department, China; Tencent AI Platform Department, China; Tencent AI Platform Department, China", "aff_domain": "tencent.com;tencent.com;tencent.com;tencent.com;tencent.com", "email": "tencent.com;tencent.com;tencent.com;tencent.com;tencent.com", "github": "https://github.com/liushulinle/PLOME", "project": "", "author_num": 5, "aff_unique_index": "0;0;0;0;0", "aff_unique_norm": "Tencent", "aff_unique_dep": "AI Platform Department", "aff_unique_url": "https://www.tencent.com", "aff_unique_abbr": "Tencent", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0;0;0;0;0", "aff_country_unique": "China" }, { "id": "2021.acl-long.467", "title": "POS-Constrained Parallel Decoding for Non-autoregressive Generation", "track": "main", "status": "Long", "award": false, "abstract": "The multimodality problem has become a major challenge of existing non-autoregressive generation (NAG) systems. A common solution often resorts to sequence-level knowledge distillation by rebuilding the training dataset through autoregressive generation (hereinafter known as \u201cteacher AG\u201d). The success of such methods may largely depend on a latent assumption, i.e., the teacher AG is superior to the NAG model. However, in this work, we experimentally reveal that this assumption does not always hold for the text generation tasks like text summarization and story ending generation. To provide a feasible solution to the multimodality problem of NAG, we propose incorporating linguistic structure (Part-of-Speech sequence in particular) into NAG inference instead of relying on teacher AG. More specifically, the proposed POS-constrained Parallel Decoding (POSPD) method aims at providing a specific POS sequence to constrain the NAG model during decoding. Our experiments demonstrate that POSPD consistently improves NAG models on four text generation tasks to a greater extent compared to knowledge distillation. This observation validates the necessity of exploring the alternatives for sequence-level knowledge distillation.", "author": "Kexin Yang; Wenqiang Lei; Dayiheng Liu; Weizhen Qi; Jiancheng Lv", "authorids": "/k/kexin-yang/; /w/wenqiang-lei/; /d/dayiheng-liu/; /w/weizhen-qi/; /j/jiancheng-lv/", "bibtex": "@inproceedings{yang-etal-2021-pos,\n title = \"{POS}-{C}onstrained {P}arallel {D}ecoding for {N}on-autoregressive {G}eneration\",\n author = \"Yang, Kexin and\n Lei, Wenqiang and\n Liu, Dayiheng and\n Qi, Weizhen and\n Lv, Jiancheng\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.467/\",\n doi = \"10.18653/v1/2021.acl-long.467\",\n pages = \"5990--6000\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.467.pdf", "site": "https://aclanthology.org/2021.acl-long.467/", "pdf_size": 486314, "gs_citation": 17, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=9652772823271631081&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 2, "aff": "College of Computer Science, Sichuan University\u2660; National University of Singapore\u2666; College of Computer Science, Sichuan University\u2660; University of Science and Technology of China\u2663; College of Computer Science, Sichuan University\u2660", "aff_domain": "gmail.com;gmail.com; ; ; ", "email": "gmail.com;gmail.com; ; ; ", "github": "", "project": "", "author_num": 5, "aff_unique_index": "0;1;0;2;0", "aff_unique_norm": "Sichuan University;National University of Singapore;University of Science and Technology of China", "aff_unique_dep": "College of Computer Science;;", "aff_unique_url": "https://www.scu.edu.cn;https://www.nus.edu.sg;http://www.ustc.edu.cn", "aff_unique_abbr": ";NUS;USTC", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0;1;0;0;0", "aff_country_unique": "China;Singapore" }, { "id": "2021.acl-long.424", "title": "PP-Rec: News Recommendation with Personalized User Interest and Time-aware News Popularity", "track": "main", "status": "Long", "award": false, "abstract": "Personalized news recommendation methods are widely used in online news services. These methods usually recommend news based on the matching between news content and user interest inferred from historical behaviors. However, these methods usually have difficulties in making accurate recommendations to cold-start users, and tend to recommend similar news with those users have read. In general, popular news usually contain important information and can attract users with different interests. Besides, they are usually diverse in content and topic. Thus, in this paper we propose to incorporate news popularity information to alleviate the cold-start and diversity problems for personalized news recommendation. In our method, the ranking score for recommending a candidate news to a target user is the combination of a personalized matching score and a news popularity score. The former is used to capture the personalized user interest in news. The latter is used to measure time-aware popularity of candidate news, which is predicted based on news content, recency, and real-time CTR using a unified framework. Besides, we propose a popularity-aware user encoder to eliminate the popularity bias in user behaviors for accurate interest modeling. Experiments on two real-world datasets show our method can effectively improve the accuracy and diversity for news recommendation.", "author": "Tao Qi; Fangzhao Wu; Chuhan Wu; Yongfeng Huang", "authorids": "/t/tao-qi/; /f/fangzhao-wu/; /c/chuhan-wu/; /y/yongfeng-huang/", "bibtex": "@inproceedings{qi-etal-2021-pp,\n title = \"{PP}-Rec: News Recommendation with Personalized User Interest and Time-aware News Popularity\",\n author = \"Qi, Tao and\n Wu, Fangzhao and\n Wu, Chuhan and\n Huang, Yongfeng\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.424/\",\n doi = \"10.18653/v1/2021.acl-long.424\",\n pages = \"5457--5467\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.424.pdf", "site": "https://aclanthology.org/2021.acl-long.424/", "pdf_size": 2330516, "gs_citation": 78, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=16039615333080872535&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 5, "aff": "Department of Electronic Engineering & BNRist, Tsinghua University; Microsoft Research Asia; Department of Electronic Engineering & BNRist, Tsinghua University; Department of Electronic Engineering & BNRist, Tsinghua University", "aff_domain": "gmail.com;gmail.com;gmail.com;mail.tsinghua.edu.cn", "email": "gmail.com;gmail.com;gmail.com;mail.tsinghua.edu.cn", "github": "", "project": "", "author_num": 4, "aff_unique_index": "0;1;0;0", "aff_unique_norm": "Tsinghua University;Microsoft Research", "aff_unique_dep": "Department of Electronic Engineering;Research", "aff_unique_url": "https://www.tsinghua.edu.cn;https://www.microsoft.com/en-us/research/group/asia", "aff_unique_abbr": "THU;MSR Asia", "aff_campus_unique_index": "1", "aff_campus_unique": ";Asia", "aff_country_unique_index": "0;0;0;0", "aff_country_unique": "China" }, { "id": "2021.acl-short.40", "title": "PRAL: A Tailored Pre-Training Model for Task-Oriented Dialog Generation", "track": "main", "status": "Short", "award": false, "abstract": "Large pre-trained language generation models such as GPT-2 have demonstrated their effectiveness as language priors by reaching state-of-the-art results in various language generation tasks. However, the performance of pre-trained models on task-oriented dialog tasks is still under-explored. We propose a Pre-trainedRole Alternating Language model (PRAL), explicitly designed for task-oriented conversational systems. We design several techniques: start position randomization, knowledge distillation, and history discount to improve pre-training performance. In addition, we introduce a high-quality large-scale task-oriented dialog pre-training dataset by post-prossessing13 dialog datasets. We effectively adapt PRALon three downstream tasks. The results show that PRAL outperforms or is on par with state-of-the-art models.", "author": "Jing Gu; Qingyang Wu; Chongruo Wu; Weiyan Shi; Zhou Yu", "authorids": "/j/jing-gu/; /q/qingyang-wu/; /c/chongruo-wu/; /w/weiyan-shi/; /z/zhou-yu/", "bibtex": "@inproceedings{gu-etal-2021-pral,\n title = \"{PRAL}: A Tailored Pre-Training Model for Task-Oriented Dialog Generation\",\n author = \"Gu, Jing and\n Wu, Qingyang and\n Wu, Chongruo and\n Shi, Weiyan and\n Yu, Zhou\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-short.40/\",\n doi = \"10.18653/v1/2021.acl-short.40\",\n pages = \"305--313\"\n}", "pdf": "https://aclanthology.org/2021.acl-short.40.pdf", "site": "https://aclanthology.org/2021.acl-short.40/", "pdf_size": 422716, "gs_citation": 11, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=2800572773935493632&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 2, "aff": "UC Davis; Columbia University; UC Davis; Columbia University; Columbia University", "aff_domain": "ucdavis.edu;columbia.edu;ucdavis.edu;columbia.edu;columbia.edu", "email": "ucdavis.edu;columbia.edu;ucdavis.edu;columbia.edu;columbia.edu", "github": "", "project": "", "author_num": 5, "aff_unique_index": "0;1;0;1;1", "aff_unique_norm": "University of California, Davis;Columbia University", "aff_unique_dep": ";", "aff_unique_url": "https://www.ucdavis.edu;https://www.columbia.edu", "aff_unique_abbr": "UC Davis;Columbia", "aff_campus_unique_index": "0;0", "aff_campus_unique": "Davis;", "aff_country_unique_index": "0;0;0;0;0", "aff_country_unique": "United States" }, { "id": "2021.acl-long.486", "title": "PRGC: Potential Relation and Global Correspondence Based Joint Relational Triple Extraction", "track": "main", "status": "Long", "award": false, "abstract": "Joint extraction of entities and relations from unstructured texts is a crucial task in information extraction. Recent methods achieve considerable performance but still suffer from some inherent limitations, such as redundancy of relation prediction, poor generalization of span-based extraction and inefficiency. In this paper, we decompose this task into three subtasks, Relation Judgement, Entity Extraction and Subject-object Alignment from a novel perspective and then propose a joint relational triple extraction framework based on Potential Relation and Global Correspondence (PRGC). Specifically, we design a component to predict potential relations, which constrains the following entity extraction to the predicted relation subset rather than all relations; then a relation-specific sequence tagging component is applied to handle the overlapping problem between subjects and objects; finally, a global correspondence component is designed to align the subject and object into a triple with low-complexity. Extensive experiments show that PRGC achieves state-of-the-art performance on public benchmarks with higher efficiency and delivers consistent performance gain on complex scenarios of overlapping triples. The source code has been submitted as the supplementary material and will be made publicly available after the blind review.", "author": "Hengyi Zheng; Rui Wen; Xi Chen; Yifan Yang; Yunyan Zhang; Ziheng Zhang; Ningyu Zhang; Bin Qin; Xu Ming; Yefeng Zheng", "authorids": "/h/hengyi-zheng/; /r/rui-wen/; /x/xi-chen/; /y/yifan-yang/; /y/yunyan-zhang/; /z/ziheng-zhang/; /n/ningyu-zhang/; /b/bin-qin/; /x/xu-ming/; /y/yefeng-zheng/", "bibtex": "@inproceedings{zheng-etal-2021-prgc,\n title = \"{PRGC}: Potential Relation and Global Correspondence Based Joint Relational Triple Extraction\",\n author = \"Zheng, Hengyi and\n Wen, Rui and\n Chen, Xi and\n Yang, Yifan and\n Zhang, Yunyan and\n Zhang, Ziheng and\n Zhang, Ningyu and\n Qin, Bin and\n Ming, Xu and\n Zheng, Yefeng\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.486/\",\n doi = \"10.18653/v1/2021.acl-long.486\",\n pages = \"6225--6235\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.486.pdf", "site": "https://aclanthology.org/2021.acl-long.486/", "pdf_size": 2582903, "gs_citation": 248, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=1329106076321053762&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 5, "aff": "College of Electronics and Information Engineering, Shenzhen University+Information Technology Center, Shenzhen University+Tencent Jarvis Lab; Tencent Jarvis Lab; Tencent Jarvis Lab+Platform and Content Group, Tencent; Tencent Jarvis Lab; Tencent Jarvis Lab; Tencent Jarvis Lab; Zhejiang University; Information Technology Center, Shenzhen University; Information Technology Center, Shenzhen University; Tencent Jarvis Lab", "aff_domain": "email.szu.edu.cn;tencent.com;tencent.com;tencent.com;tencent.com;tencent.com; ;szu.edu.cn;szu.edu.cn;tencent.com", "email": "email.szu.edu.cn;tencent.com;tencent.com;tencent.com;tencent.com;tencent.com; ;szu.edu.cn;szu.edu.cn;tencent.com", "github": "https://github.com/hy-struggle/PRGC", "project": "", "author_num": 10, "aff_unique_index": "0+0+1;1;1+1;1;1;1;2;0;0;1", "aff_unique_norm": "Shenzhen University;Tencent;Zhejiang University", "aff_unique_dep": "College of Electronics and Information Engineering;Jarvis Lab;", "aff_unique_url": "http://www.szu.edu.cn/;https://www.tencent.com;https://www.zju.edu.cn", "aff_unique_abbr": "SZU;Tencent;ZJU", "aff_campus_unique_index": "0+0;;0;0", "aff_campus_unique": "Shenzhen;", "aff_country_unique_index": "0+0+0;0;0+0;0;0;0;0;0;0;0", "aff_country_unique": "China" }, { "id": "2021.acl-long.191", "title": "PROTAUGMENT: Unsupervised diverse short-texts paraphrasing for intent detection meta-learning", "track": "main", "status": "Long", "award": false, "abstract": "Recent research considers few-shot intent detection as a meta-learning problem: the model is learning to learn from a consecutive set of small tasks named episodes. In this work, we propose ProtAugment, a meta-learning algorithm for short texts classification (the intent detection task). ProtAugment is a novel extension of Prototypical Networks, that limits overfitting on the bias introduced by the few-shots classification objective at each episode. It relies on diverse paraphrasing: a conditional language model is first fine-tuned for paraphrasing, and diversity is later introduced at the decoding stage at each meta-learning episode. The diverse paraphrasing is unsupervised as it is applied to unlabelled data, and then fueled to the Prototypical Network training objective as a consistency loss. ProtAugment is the state-of-the-art method for intent detection meta-learning, at no extra labeling efforts and without the need to fine-tune a conditional language model on a given application domain.", "author": "Thomas Dopierre; Christophe Gravier; Wilfried Logerais", "authorids": "/t/thomas-dopierre/; /c/christophe-gravier/; /w/wilfried-logerais/", "bibtex": "@inproceedings{dopierre-etal-2021-protaugment,\n title = \"{PROTAUGMENT}: Unsupervised diverse short-texts paraphrasing for intent detection meta-learning\",\n author = \"Dopierre, Thomas and\n Gravier, Christophe and\n Logerais, Wilfried\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.191/\",\n doi = \"10.18653/v1/2021.acl-long.191\",\n pages = \"2454--2466\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.191.pdf", "site": "https://aclanthology.org/2021.acl-long.191/", "pdf_size": 449637, "gs_citation": 42, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=1464322033343201174&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 4, "aff": "Laboratoire Hubert Curien, UMR CNRS 5516, Universit\u00e9 Jean Monnet+Meetic; Laboratoire Hubert Curien, UMR CNRS 5516, Universit\u00e9 Jean Monnet; Meetic", "aff_domain": "univ-st-etienne.fr;univ-st-etienne.fr;meetic-corp.com", "email": "univ-st-etienne.fr;univ-st-etienne.fr;meetic-corp.com", "github": "", "project": "", "author_num": 3, "aff_unique_index": "0+1;0;1", "aff_unique_norm": "Universit\u00e9 Jean Monnet;Meetic", "aff_unique_dep": "Laboratoire Hubert Curien;", "aff_unique_url": "https://www.univ-jean-monnet.fr;https://www.meetic.fr", "aff_unique_abbr": "UJM;Meetic", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0+0;0;0", "aff_country_unique": "France" }, { "id": "2021.acl-long.336", "title": "PairRE: Knowledge Graph Embeddings via Paired Relation Vectors", "track": "main", "status": "Long", "award": false, "abstract": "Distance based knowledge graph embedding methods show promising results on link prediction task, on which two topics have been widely studied: one is the ability to handle complex relations, such as N-to-1, 1-to-N and N-to-N, the other is to encode various relation patterns, such as symmetry/antisymmetry. However, the existing methods fail to solve these two problems at the same time, which leads to unsatisfactory results. To mitigate this problem, we propose PairRE, a model with paired vectors for each relation representation. The paired vectors enable an adaptive adjustment of the margin in loss function to fit for different complex relations. Besides, PairRE is capable of encoding three important relation patterns, symmetry/antisymmetry, inverse and composition. Given simple constraints on relation representations, PairRE can encode subrelation further. Experiments on link prediction benchmarks demonstrate the proposed key capabilities of PairRE. Moreover, We set a new state-of-the-art on two knowledge graph datasets of the challenging Open Graph Benchmark.", "author": "Linlin Chao; Jianshan He; Taifeng Wang; Wei Chu", "authorids": "/l/linlin-chao/; /j/jianshan-he/; /t/taifeng-wang/; /w/wei-chu/", "bibtex": "@inproceedings{chao-etal-2021-pairre,\n title = \"{P}air{RE}: Knowledge Graph Embeddings via Paired Relation Vectors\",\n author = \"Chao, Linlin and\n He, Jianshan and\n Wang, Taifeng and\n Chu, Wei\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.336/\",\n doi = \"10.18653/v1/2021.acl-long.336\",\n pages = \"4360--4369\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.336.pdf", "site": "https://aclanthology.org/2021.acl-long.336/", "pdf_size": 722766, "gs_citation": 187, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=1728496660463234883&as_sdt=5,44&sciodt=0,44&hl=en", "gs_version_total": 4, "aff": "AntGroup; AntGroup; Alibaba Inc; Alibaba Inc", "aff_domain": "antgroup.com;antgroup.com;alibaba-inc.com;alibaba-inc.com", "email": "antgroup.com;antgroup.com;alibaba-inc.com;alibaba-inc.com", "github": "", "project": "", "author_num": 4, "aff_unique_index": "0;0;1;1", "aff_unique_norm": "Ant Group;Alibaba Group Holding Limited", "aff_unique_dep": ";", "aff_unique_url": "https://www.antgroup.com;https://www.alibaba.com", "aff_unique_abbr": "AntGroup;Alibaba", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0;0;0;0", "aff_country_unique": "China" }, { "id": "2021.acl-short.104", "title": "Parameter Selection: Why We Should Pay More Attention to It", "track": "main", "status": "Short", "award": false, "abstract": "The importance of parameter selection in supervised learning is well known. However, due to the many parameter combinations, an incomplete or an insufficient procedure is often applied. This situation may cause misleading or confusing conclusions. In this opinion paper, through an intriguing example we point out that the seriousness goes beyond what is generally recognized. In the topic of multilabel classification for medical code prediction, one influential paper conducted a proper parameter selection on a set, but when moving to a subset of frequently occurring labels, the authors used the same parameters without a separate tuning. The set of frequent labels became a popular benchmark in subsequent studies, which kept pushing the state of the art. However, we discovered that most of the results in these studies cannot surpass the approach in the original paper if a parameter tuning had been conducted at the time. Thus it is unclear how much progress the subsequent developments have actually brought. The lesson clearly indicates that without enough attention on parameter selection, the research progress in our field can be uncertain or even illusive.", "author": "Jie-Jyun Liu; Tsung-Han Yang; Si-An Chen; Chih-Jen Lin", "authorids": "/j/jie-jyun-liu/; /t/tsung-han-yang/; /s/si-an-chen/; /c/chih-jen-lin/", "bibtex": "@inproceedings{liu-etal-2021-parameter,\n title = \"Parameter Selection: Why We Should Pay More Attention to It\",\n author = \"Liu, Jie-Jyun and\n Yang, Tsung-Han and\n Chen, Si-An and\n Lin, Chih-Jen\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-short.104/\",\n doi = \"10.18653/v1/2021.acl-short.104\",\n pages = \"825--830\"\n}", "pdf": "https://aclanthology.org/2021.acl-short.104.pdf", "site": "https://aclanthology.org/2021.acl-short.104/", "pdf_size": 218746, "gs_citation": 7, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=4904252980957875178&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 5, "aff": "ASUS Intelligent Cloud Services; ASUS Intelligent Cloud Services; ASUS Intelligent Cloud Services+National Taiwan University; National Taiwan University", "aff_domain": "asus.com;asus.com;csie.ntu.edu.tw;csie.ntu.edu.tw", "email": "asus.com;asus.com;csie.ntu.edu.tw;csie.ntu.edu.tw", "github": "", "project": "http://www.csie.ntu.edu.tw/~cjlin/papers/parameter_selection", "author_num": 4, "aff_unique_index": "0;0;0+1;1", "aff_unique_norm": "ASUS;National Taiwan University", "aff_unique_dep": "Intelligent Cloud Services;", "aff_unique_url": "https://www.asus.com;https://www.ntu.edu.tw", "aff_unique_abbr": "ASUS;NTU", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0;0;0+0;0", "aff_country_unique": "Taiwan, China" }, { "id": "2021.acl-long.378", "title": "Parameter-Efficient Transfer Learning with Diff Pruning", "track": "main", "status": "Long", "award": false, "abstract": "The large size of pretrained networks makes them difficult to deploy for multiple tasks in storage-constrained settings. Diff pruning enables parameter-efficient transfer learning that scales well with new tasks. The approach learns a task-specific \u201cdiff\u201d vector that extends the original pretrained parameters. This diff vector is adaptively pruned during training with a differentiable approximation to the L0-norm penalty to encourage sparsity. As the number of tasks increases, diff pruning remains parameter-efficient, as it requires storing only a small diff vector for each task. Since it does not require access to all tasks during training, it is attractive in on-device deployment settings where tasks arrive in stream or even from different providers. Diff pruning can match the performance of finetuned baselines on the GLUE benchmark while only modifying 0.5% of the pretrained model\u2019s parameters per task and scales favorably in comparison to popular pruning approaches.", "author": "Demi Guo; Alexander Rush; Yoon Kim", "authorids": "/d/demi-guo/; /a/alexander-m-rush/; /y/yoon-kim/", "bibtex": "@inproceedings{guo-etal-2021-parameter,\n title = \"Parameter-Efficient Transfer Learning with Diff Pruning\",\n author = \"Guo, Demi and\n Rush, Alexander and\n Kim, Yoon\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.378/\",\n doi = \"10.18653/v1/2021.acl-long.378\",\n pages = \"4884--4896\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.378.pdf", "site": "https://aclanthology.org/2021.acl-long.378/", "pdf_size": 535246, "gs_citation": 446, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=2500880548083395687&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 7, "aff": "Harvard University; Cornell University; MIT CSAIL+MIT-IBM Watson AI", "aff_domain": "college.harvard.edu;cornell.edu;mit.edu", "email": "college.harvard.edu;cornell.edu;mit.edu", "github": "https://github.com/dguo98/DiffPruning", "project": "", "author_num": 3, "aff_unique_index": "0;1;2+2", "aff_unique_norm": "Harvard University;Cornell University;Massachusetts Institute of Technology", "aff_unique_dep": ";;Computer Science and Artificial Intelligence Laboratory", "aff_unique_url": "https://www.harvard.edu;https://www.cornell.edu;https://www.csail.mit.edu", "aff_unique_abbr": "Harvard;Cornell;MIT CSAIL", "aff_campus_unique_index": "1", "aff_campus_unique": ";Cambridge", "aff_country_unique_index": "0;0;0+0", "aff_country_unique": "United States" }, { "id": "2021.acl-long.47", "title": "Parameter-efficient Multi-task Fine-tuning for Transformers via Shared Hypernetworks", "track": "main", "status": "Long", "award": false, "abstract": "State-of-the-art parameter-efficient fine-tuning methods rely on introducing adapter modules between the layers of a pretrained language model. However, such modules are trained separately for each task and thus do not enable sharing information across tasks. In this paper, we show that we can learn adapter parameters for all layers and tasks by generating them using shared hypernetworks, which condition on task, adapter position, and layer id in a transformer model. This parameter-efficient multi-task learning framework allows us to achieve the best of both worlds by sharing knowledge across tasks via hypernetworks while enabling the model to adapt to each individual task through task-specific adapters. Experiments on the well-known GLUE benchmark show improved performance in multi-task learning while adding only 0.29% parameters per task. We additionally demonstrate substantial performance improvements in few-shot domain generalization across a variety of tasks. Our code is publicly available in https://github.com/rabeehk/hyperformer.", "author": "Rabeeh Karimi Mahabadi; Sebastian Ruder; Mostafa Dehghani; James Henderson", "authorids": "/r/rabeeh-karimi-mahabadi/; /s/sebastian-ruder/; /m/mostafa-dehghani/; /j/james-henderson/", "bibtex": "@inproceedings{karimi-mahabadi-etal-2021-parameter,\n title = \"Parameter-efficient Multi-task Fine-tuning for Transformers via Shared Hypernetworks\",\n author = \"Karimi Mahabadi, Rabeeh and\n Ruder, Sebastian and\n Dehghani, Mostafa and\n Henderson, James\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.47/\",\n doi = \"10.18653/v1/2021.acl-long.47\",\n pages = \"565--576\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.47.pdf", "site": "https://aclanthology.org/2021.acl-long.47/", "pdf_size": 567316, "gs_citation": 342, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=10483818131118762250&as_sdt=5,44&sciodt=0,44&hl=en", "gs_version_total": 10, "aff": "EPFL University, Idiap Research Institute; DeepMind; Google Brain; Idiap Research Institute", "aff_domain": "epfl.ch;google.com;google.com;idiap.ch", "email": "epfl.ch;google.com;google.com;idiap.ch", "github": "https://github.com/rabeehk/hyperformer", "project": "", "author_num": 4, "aff_unique_index": "0;1;2;3", "aff_unique_norm": "EPFL University;DeepMind;Google;Idiap Research Institute", "aff_unique_dep": ";;Google Brain;", "aff_unique_url": "https://www.epfl.ch;https://deepmind.com;https://brain.google.com;https://www.idiap.ch", "aff_unique_abbr": "EPFL;DeepMind;Google Brain;Idiap", "aff_campus_unique_index": "1", "aff_campus_unique": ";Mountain View", "aff_country_unique_index": "0;1;2;0", "aff_country_unique": "Switzerland;United Kingdom;United States" }, { "id": "2021.acl-long.383", "title": "Personalized Transformer for Explainable Recommendation", "track": "main", "status": "Long", "award": false, "abstract": "Personalization of natural language generation plays a vital role in a large spectrum of tasks, such as explainable recommendation, review summarization and dialog systems. In these tasks, user and item IDs are important identifiers for personalization. Transformer, which is demonstrated with strong language modeling capability, however, is not personalized and fails to make use of the user and item IDs since the ID tokens are not even in the same semantic space as the words. To address this problem, we present a PErsonalized Transformer for Explainable Recommendation (PETER), on which we design a simple and effective learning objective that utilizes the IDs to predict the words in the target explanation, so as to endow the IDs with linguistic meanings and to achieve personalized Transformer. Besides generating explanations, PETER can also make recommendations, which makes it a unified model for the whole recommendation-explanation pipeline. Extensive experiments show that our small unpretrained model outperforms fine-tuned BERT on the generation task, in terms of both effectiveness and efficiency, which highlights the importance and the nice utility of our design.", "author": "Lei Li; Yongfeng Zhang; Li Chen", "authorids": "/l/lei-li/; /y/yongfeng-zhang/; /l/li-chen/", "bibtex": "@inproceedings{li-etal-2021-personalized,\n title = \"Personalized Transformer for Explainable Recommendation\",\n author = \"Li, Lei and\n Zhang, Yongfeng and\n Chen, Li\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.383/\",\n doi = \"10.18653/v1/2021.acl-long.383\",\n pages = \"4947--4957\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.383.pdf", "site": "https://aclanthology.org/2021.acl-long.383/", "pdf_size": 1090848, "gs_citation": 188, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=2358263753438500336&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 12, "aff": "Hong Kong Baptist University, Hong Kong, China; Rutgers University, New Brunswick, USA; Hong Kong Baptist University, Hong Kong, China", "aff_domain": "comp.hkbu.edu.hk;rutgers.edu;comp.hkbu.edu.hk", "email": "comp.hkbu.edu.hk;rutgers.edu;comp.hkbu.edu.hk", "github": "https://github.com/lileipisces/PETER", "project": "", "author_num": 3, "aff_unique_index": "0;1;0", "aff_unique_norm": "Hong Kong Baptist University;Rutgers University", "aff_unique_dep": ";", "aff_unique_url": "https://www.hkbu.edu.hk;https://www.rutgers.edu", "aff_unique_abbr": "HKBU;Rutgers", "aff_campus_unique_index": "0;1;0", "aff_campus_unique": "Hong Kong;New Brunswick", "aff_country_unique_index": "0;1;0", "aff_country_unique": "China;United States" }, { "id": "2021.acl-long.479", "title": "PhotoChat: A Human-Human Dialogue Dataset With Photo Sharing Behavior For Joint Image-Text Modeling", "track": "main", "status": "Long", "award": false, "abstract": "We present a new human-human dialogue dataset - PhotoChat, the first dataset that casts light on the photo sharing behavior in online messaging. PhotoChat contains 12k dialogues, each of which is paired with a user photo that is shared during the conversation. Based on this dataset, we propose two tasks to facilitate research on image-text modeling: a photo-sharing intent prediction task that predicts whether one intends to share a photo in the next conversation turn, and a photo retrieval task that retrieves the most relevant photo according to the dialogue context. In addition, for both tasks, we provide baseline models using the state-of-the-art models and report their benchmark performances. The best image retrieval model achieves 10.4% recall@1 (out of 1000 candidates) and the best photo intent prediction model achieves 58.1% F1 score, indicating that the dataset presents interesting yet challenging real-world problems. We are releasing PhotoChat to facilitate future research work among the community.", "author": "Xiaoxue Zang; Lijuan Liu; Maria Wang; Yang Song; Hao Zhang; Jindong Chen", "authorids": "/x/xiaoxue-zang/; /l/lijuan-liu/; /m/maria-wang/; /y/yang-song/; /h/hao-zhang/; /j/jindong-chen/", "bibtex": "@inproceedings{zang-etal-2021-photochat,\n title = \"{P}hoto{C}hat: A Human-Human Dialogue Dataset With Photo Sharing Behavior For Joint Image-Text Modeling\",\n author = \"Zang, Xiaoxue and\n Liu, Lijuan and\n Wang, Maria and\n Song, Yang and\n Zhang, Hao and\n Chen, Jindong\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.479/\",\n doi = \"10.18653/v1/2021.acl-long.479\",\n pages = \"6142--6152\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.479.pdf", "site": "https://aclanthology.org/2021.acl-long.479/", "pdf_size": 11448721, "gs_citation": 62, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=8151582185143247051&as_sdt=5,33&sciodt=0,33&hl=en", "gs_version_total": 6, "aff": "Google Research; Google Research; Google Research; Kuaishou Technology; Google Research; Google Research", "aff_domain": "google.com;google.com;google.com;kuaishou.com;google.com;google.com", "email": "google.com;google.com;google.com;kuaishou.com;google.com;google.com", "github": "", "project": "", "author_num": 6, "aff_unique_index": "0;0;0;1;0;0", "aff_unique_norm": "Google;Kuaishou Technology", "aff_unique_dep": "Google Research;", "aff_unique_url": "https://research.google;https://www.kuaishou.com", "aff_unique_abbr": "Google Research;Kuaishou", "aff_campus_unique_index": "0;0;0;0;0", "aff_campus_unique": "Mountain View;", "aff_country_unique_index": "0;0;0;1;0;0", "aff_country_unique": "United States;China" }, { "id": "2021.acl-long.169", "title": "PlotCoder: Hierarchical Decoding for Synthesizing Visualization Code in Programmatic Context", "track": "main", "status": "Long", "award": false, "abstract": "Creating effective visualization is an important part of data analytics. While there are many libraries for creating visualization, writing such code remains difficult given the myriad of parameters that users need to provide. In this paper, we propose the new task of synthesizing visualization programs from a combination of natural language utterances and code context. To tackle the learning problem, we introduce PlotCoder, a new hierarchical encoder-decoder architecture that models both the code context and the input utterance. We use PlotCoder to first determine the template of the visualization code, followed by predicting the data to be plotted. We use Jupyter notebooks containing visualization programs crawled from GitHub to train PlotCoder. On a comprehensive set of test samples from those notebooks, we show that PlotCoder correctly predicts the plot type of about 70% samples, and synthesizes the correct programs for 35% samples, performing 3-4.5% better than the baselines.", "author": "Xinyun Chen; Linyuan Gong; Alvin Cheung; Dawn Song", "authorids": "/x/xinyun-chen/; /l/linyuan-gong/; /a/alvin-cheung/; /d/dawn-song/", "bibtex": "@inproceedings{chen-etal-2021-plotcoder,\n title = \"{P}lot{C}oder: Hierarchical Decoding for Synthesizing Visualization Code in Programmatic Context\",\n author = \"Chen, Xinyun and\n Gong, Linyuan and\n Cheung, Alvin and\n Song, Dawn\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.169/\",\n doi = \"10.18653/v1/2021.acl-long.169\",\n pages = \"2169--2181\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.169.pdf", "site": "https://aclanthology.org/2021.acl-long.169/", "pdf_size": 1102060, "gs_citation": 23, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=6835093142744390449&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 2, "aff": "UC Berkeley; UC Berkeley; UC Berkeley; UC Berkeley", "aff_domain": "berkeley.edu;berkeley.edu;berkeley.edu;berkeley.edu", "email": "berkeley.edu;berkeley.edu;berkeley.edu;berkeley.edu", "github": "https://github.com/jungyhuk/plotcoder", "project": "", "author_num": 4, "aff_unique_index": "0;0;0;0", "aff_unique_norm": "University of California, Berkeley", "aff_unique_dep": "", "aff_unique_url": "https://www.berkeley.edu", "aff_unique_abbr": "UC Berkeley", "aff_campus_unique_index": "0;0;0;0", "aff_campus_unique": "Berkeley", "aff_country_unique_index": "0;0;0;0", "aff_country_unique": "United States" }, { "id": "2021.acl-long.307", "title": "Point, Disambiguate and Copy: Incorporating Bilingual Dictionaries for Neural Machine Translation", "track": "main", "status": "Long", "award": false, "abstract": "This paper proposes a sophisticated neural architecture to incorporate bilingual dictionaries into Neural Machine Translation (NMT) models. By introducing three novel components: Pointer, Disambiguator, and Copier, our method PDC achieves the following merits inherently compared with previous efforts: (1) Pointer leverages the semantic information from bilingual dictionaries, for the first time, to better locate source words whose translation in dictionaries can potentially be used; (2) Disambiguator synthesizes contextual information from the source view and the target view, both of which contribute to distinguishing the proper translation of a specific source word from multiple candidates in dictionaries; (3) Copier systematically connects Pointer and Disambiguator based on a hierarchical copy mechanism seamlessly integrated with Transformer, thereby building an end-to-end architecture that could avoid error propagation problems in alternative pipe-line methods. The experimental results on Chinese-English and English-Japanese benchmarks demonstrate the PDC\u2019s overall superiority and effectiveness of each component.", "author": "Tong Zhang; Long Zhang; Wei Ye; Bo Li; Jinan Sun; Xiaoyu Zhu; Wen Zhao; Shikun Zhang", "authorids": "/t/tong-zhang/; /l/long-zhang/; /w/wei-ye/; /b/bo-li/; /j/jinan-sun/; /x/xiaoyu-zhu/; /w/wen-zhao/; /s/shikun-zhang/", "bibtex": "@inproceedings{zhang-etal-2021-point,\n title = \"Point, Disambiguate and Copy: Incorporating Bilingual Dictionaries for Neural Machine Translation\",\n author = \"Zhang, Tong and\n Zhang, Long and\n Ye, Wei and\n Li, Bo and\n Sun, Jinan and\n Zhu, Xiaoyu and\n Zhao, Wen and\n Zhang, Shikun\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.307/\",\n doi = \"10.18653/v1/2021.acl-long.307\",\n pages = \"3970--3979\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.307.pdf", "site": "https://aclanthology.org/2021.acl-long.307/", "pdf_size": 555783, "gs_citation": 20, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=8398401848273075838&as_sdt=5,33&sciodt=0,33&hl=en", "gs_version_total": 3, "aff": "National Engineering Research Center for Software Engineering, Peking University + School of Software and Microelectronics, Peking University; National Engineering Research Center for Software Engineering, Peking University + School of Software and Microelectronics, Peking University; National Engineering Research Center for Software Engineering, Peking University; National Engineering Research Center for Software Engineering, Peking University + School of Software and Microelectronics, Peking University; National Engineering Research Center for Software Engineering, Peking University; BIGO; National Engineering Research Center for Software Engineering, Peking University; National Engineering Research Center for Software Engineering, Peking University + School of Software and Microelectronics, Peking University", "aff_domain": "pku.edu.cn;pku.edu.cn;pku.edu.cn; ;pku.edu.cn; ; ; ", "email": "pku.edu.cn;pku.edu.cn;pku.edu.cn; ;pku.edu.cn; ; ; ", "github": "", "project": "", "author_num": 8, "aff_unique_index": "0+0;0+0;0;0+0;0;1;0;0+0", "aff_unique_norm": "Peking University;BIGO Technology", "aff_unique_dep": "National Engineering Research Center for Software Engineering;", "aff_unique_url": "http://www.pku.edu.cn;https://www.bigo.com", "aff_unique_abbr": "PKU;BIGO", "aff_campus_unique_index": ";;;", "aff_campus_unique": "", "aff_country_unique_index": "0+0;0+0;0;0+0;0;0;0;0+0", "aff_country_unique": "China" }, { "id": "2021.acl-long.147", "title": "Poisoning Knowledge Graph Embeddings via Relation Inference Patterns", "track": "main", "status": "Long", "award": false, "abstract": "We study the problem of generating data poisoning attacks against Knowledge Graph Embedding (KGE) models for the task of link prediction in knowledge graphs. To poison KGE models, we propose to exploit their inductive abilities which are captured through the relationship patterns like symmetry, inversion and composition in the knowledge graph. Specifically, to degrade the model\u2019s prediction confidence on target facts, we propose to improve the model\u2019s prediction confidence on a set of decoy facts. Thus, we craft adversarial additions that can improve the model\u2019s prediction confidence on decoy facts through different inference patterns. Our experiments demonstrate that the proposed poisoning attacks outperform state-of-art baselines on four KGE models for two publicly available datasets. We also find that the symmetry pattern based attacks generalize across all model-dataset combinations which indicates the sensitivity of KGE models to this pattern.", "author": "Peru Bhardwaj; John Kelleher; Luca Costabello; Declan O\u2019Sullivan", "authorids": "/p/peru-bhardwaj/; /j/john-kelleher/; /l/luca-costabello/; /d/declan-osullivan/", "bibtex": "@inproceedings{bhardwaj-etal-2021-poisoning,\n title = \"Poisoning Knowledge Graph Embeddings via Relation Inference Patterns\",\n author = \"Bhardwaj, Peru and\n Kelleher, John and\n Costabello, Luca and\n O{'}Sullivan, Declan\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.147/\",\n doi = \"10.18653/v1/2021.acl-long.147\",\n pages = \"1875--1888\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.147.pdf", "site": "https://aclanthology.org/2021.acl-long.147/", "pdf_size": 1309342, "gs_citation": 22, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=9164633841652917803&as_sdt=5,31&sciodt=0,31&hl=en", "gs_version_total": 4, "aff": "ADAPT Centre, Trinity College Dublin, Ireland; ADAPT Centre, TU Dublin, Ireland; Accenture Labs, Ireland; ADAPT Centre, Trinity College Dublin, Ireland", "aff_domain": "adaptcentre.ie; ; ; ", "email": "adaptcentre.ie; ; ; ", "github": "", "project": "", "author_num": 4, "aff_unique_index": "0;1;2;0", "aff_unique_norm": "Trinity College Dublin;TU Dublin;Accenture Labs", "aff_unique_dep": "ADAPT Centre;ADAPT Centre;", "aff_unique_url": "https://www.tcd.ie;https://www.tudublin.ie/;https://labs.accenture.com", "aff_unique_abbr": "TCD;TU Dublin;Accenture Labs", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0;0;0;0", "aff_country_unique": "Ireland" }, { "id": "2021.acl-long.523", "title": "Polyjuice: Generating Counterfactuals for Explaining, Evaluating, and Improving Models", "track": "main", "status": "Long", "award": false, "abstract": "While counterfactual examples are useful for analysis and training of NLP models, current generation methods either rely on manual labor to create very few counterfactuals, or only instantiate limited types of perturbations such as paraphrases or word substitutions. We present Polyjuice, a general-purpose counterfactual generator that allows for control over perturbation types and locations, trained by finetuning GPT-2 on multiple datasets of paired sentences. We show that Polyjuice produces diverse sets of realistic counterfactuals, which in turn are useful in various distinct applications: improving training and evaluation on three different tasks (with around 70% less annotation effort than manual generation), augmenting state-of-the-art explanation techniques, and supporting systematic counterfactual error analysis by revealing behaviors easily missed by human experts.", "author": "Tongshuang Wu; Marco Tulio Ribeiro; Jeffrey Heer; Daniel Weld", "authorids": "/t/tongshuang-wu/; /m/marco-tulio-ribeiro/; /j/jeffrey-heer/; /d/daniel-s-weld/", "bibtex": "@inproceedings{wu-etal-2021-polyjuice,\n title = \"Polyjuice: Generating Counterfactuals for Explaining, Evaluating, and Improving Models\",\n author = \"Wu, Tongshuang and\n Ribeiro, Marco Tulio and\n Heer, Jeffrey and\n Weld, Daniel\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.523/\",\n doi = \"10.18653/v1/2021.acl-long.523\",\n pages = \"6707--6723\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.523.pdf", "site": "https://aclanthology.org/2021.acl-long.523/", "pdf_size": 2679535, "gs_citation": 276, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=9593898182448095757&as_sdt=5,44&sciodt=0,44&hl=en", "gs_version_total": 12, "aff": "University of Washington; Microsoft Research; University of Washington+Allen Institute for Arti\ufb01cial Intelligence; University of Washington+Allen Institute for Arti\ufb01cial Intelligence", "aff_domain": "cs.uw.edu;microsoft.com;cs.uw.edu;cs.uw.edu", "email": "cs.uw.edu;microsoft.com;cs.uw.edu;cs.uw.edu", "github": "https://github.com/tongshuangwu/polyjuice", "project": "", "author_num": 4, "aff_unique_index": "0;1;0+2;0+2", "aff_unique_norm": "University of Washington;Microsoft Corporation;Allen Institute for Artificial Intelligence", "aff_unique_dep": ";Microsoft Research;Artificial Intelligence", "aff_unique_url": "https://www.washington.edu;https://www.microsoft.com/en-us/research;https://allenai.org", "aff_unique_abbr": "UW;MSR;AI2", "aff_campus_unique_index": ";", "aff_campus_unique": "", "aff_country_unique_index": "0;0;0+0;0+0", "aff_country_unique": "United States" }, { "id": "2021.acl-long.261", "title": "Position Bias Mitigation: A Knowledge-Aware Graph Model for Emotion Cause Extraction", "track": "main", "status": "Long", "award": false, "abstract": "The Emotion Cause Extraction (ECE) task aims to identify clauses which contain emotion-evoking information for a particular emotion expressed in text. We observe that a widely-used ECE dataset exhibits a bias that the majority of annotated cause clauses are either directly before their associated emotion clauses or are the emotion clauses themselves. Existing models for ECE tend to explore such relative position information and suffer from the dataset bias. To investigate the degree of reliance of existing ECE models on clause relative positions, we propose a novel strategy to generate adversarial examples in which the relative position information is no longer the indicative feature of cause clauses. We test the performance of existing models on such adversarial examples and observe a significant performance drop. To address the dataset bias, we propose a novel graph-based method to explicitly model the emotion triggering paths by leveraging the commonsense knowledge to enhance the semantic dependencies between a candidate clause and an emotion clause. Experimental results show that our proposed approach performs on par with the existing state-of-the-art methods on the original ECE dataset, and is more robust against adversarial attacks compared to existing models.", "author": "Hanqi Yan; Lin Gui; Gabriele Pergola; Yulan He", "authorids": "/h/hanqi-yan/; /l/lin-gui/; /g/gabriele-pergola/; /y/yulan-he/", "bibtex": "@inproceedings{yan-etal-2021-position,\n title = \"Position Bias Mitigation: A Knowledge-Aware Graph Model for Emotion Cause Extraction\",\n author = \"Yan, Hanqi and\n Gui, Lin and\n Pergola, Gabriele and\n He, Yulan\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.261/\",\n doi = \"10.18653/v1/2021.acl-long.261\",\n pages = \"3364--3375\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.261.pdf", "site": "https://aclanthology.org/2021.acl-long.261/", "pdf_size": 621174, "gs_citation": 61, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=16813816589934970385&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 10, "aff": "Department of Computer Science, University of Warwick; Department of Computer Science, University of Warwick; Department of Computer Science, University of Warwick; Department of Computer Science, University of Warwick", "aff_domain": "warwick.ac.uk;warwick.ac.uk;warwick.ac.uk;warwick.ac.uk", "email": "warwick.ac.uk;warwick.ac.uk;warwick.ac.uk;warwick.ac.uk", "github": "https://github.com/hanqi-qi/Position-Bias-Mitigation-in-Emotion-Cause-Analysis", "project": "", "author_num": 4, "aff_unique_index": "0;0;0;0", "aff_unique_norm": "University of Warwick", "aff_unique_dep": "Department of Computer Science", "aff_unique_url": "https://warwick.ac.uk", "aff_unique_abbr": "Warwick", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0;0;0;0", "aff_country_unique": "United Kingdom" }, { "id": "2021.acl-long.413", "title": "Positional Artefacts Propagate Through Masked Language Model Embeddings", "track": "main", "status": "Long", "award": false, "abstract": "In this work, we demonstrate that the contextualized word vectors derived from pretrained masked language model-based encoders share a common, perhaps undesirable pattern across layers. Namely, we find cases of persistent outlier neurons within BERT and RoBERTa\u2019s hidden state vectors that consistently bear the smallest or largest values in said vectors. In an attempt to investigate the source of this information, we introduce a neuron-level analysis method, which reveals that the outliers are closely related to information captured by positional embeddings. We also pre-train the RoBERTa-base models from scratch and find that the outliers disappear without using positional embeddings. These outliers, we find, are the major cause of anisotropy of encoders\u2019 raw vector spaces, and clipping them leads to increased similarity across vectors. We demonstrate this in practice by showing that clipped vectors can more accurately distinguish word senses, as well as lead to better sentence embeddings when mean pooling. In three supervised tasks, we find that clipping does not affect the performance.", "author": "Ziyang Luo; Artur Kulmizev; Xiaoxi Mao", "authorids": "/z/ziyang-luo/; /a/artur-kulmizev/; /x/xiaoxi-mao/", "bibtex": "@inproceedings{luo-etal-2021-positional,\n title = \"Positional Artefacts Propagate Through Masked Language Model Embeddings\",\n author = \"Luo, Ziyang and\n Kulmizev, Artur and\n Mao, Xiaoxi\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.413/\",\n doi = \"10.18653/v1/2021.acl-long.413\",\n pages = \"5312--5327\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.413.pdf", "site": "https://aclanthology.org/2021.acl-long.413/", "pdf_size": 2172531, "gs_citation": 46, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=2320134493535220642&as_sdt=5,33&sciodt=0,33&hl=en", "gs_version_total": 6, "aff": "Department of Linguistics and Philology, Uppsala University, Sweden; Department of Linguistics and Philology, Uppsala University, Sweden; Fuxi AI Lab, NetEase Inc., Hangzhou, China", "aff_domain": "student.uu.se;lingfil.uu.se;corp.netease.com", "email": "student.uu.se;lingfil.uu.se;corp.netease.com", "github": "", "project": "", "author_num": 3, "aff_unique_index": "0;0;1", "aff_unique_norm": "Uppsala University;NetEase Inc.", "aff_unique_dep": "Department of Linguistics and Philology;Fuxi AI Lab", "aff_unique_url": "https://www.uu.se;https://www.163.com", "aff_unique_abbr": "UU;NetEase", "aff_campus_unique_index": "1", "aff_campus_unique": ";Hangzhou", "aff_country_unique_index": "0;0;1", "aff_country_unique": "Sweden;China" }, { "id": "2021.acl-long.398", "title": "Pre-training Universal Language Representation", "track": "main", "status": "Long", "award": false, "abstract": "Despite the well-developed cut-edge representation learning for language, most language representation models usually focus on specific levels of linguistic units. This work introduces universal language representation learning, i.e., embeddings of different levels of linguistic units or text with quite diverse lengths in a uniform vector space. We propose the training objective MiSAD that utilizes meaningful n-grams extracted from large unlabeled corpus by a simple but effective algorithm for pre-trained language models. Then we empirically verify that well designed pre-training scheme may effectively yield universal language representation, which will bring great convenience when handling multiple layers of linguistic objects in a unified way. Especially, our model achieves the highest accuracy on analogy tasks in different language levels and significantly improves the performance on downstream tasks in the GLUE benchmark and a question answering dataset.", "author": "Yian Li; Hai Zhao", "authorids": "/y/yian-li/; /h/hai-zhao/", "bibtex": "@inproceedings{li-zhao-2021-pre,\n title = \"Pre-training Universal Language Representation\",\n author = \"Li, Yian and\n Zhao, Hai\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.398/\",\n doi = \"10.18653/v1/2021.acl-long.398\",\n pages = \"5122--5133\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.398.pdf", "site": "https://aclanthology.org/2021.acl-long.398/", "pdf_size": 336658, "gs_citation": 10, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=13150070645115688259&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 5, "aff": "Department of Computer Science and Engineering, Shanghai Jiao Tong University+Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering, Shanghai Jiao Tong University+MoE Key Lab of Arti\ufb01cial Intelligence, AI Institute, Shanghai Jiao Tong University; Department of Computer Science and Engineering, Shanghai Jiao Tong University+Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering, Shanghai Jiao Tong University+MoE Key Lab of Arti\ufb01cial Intelligence, AI Institute, Shanghai Jiao Tong University", "aff_domain": "sjtu.edu.cn;cs.sjtu.edu.cn", "email": "sjtu.edu.cn;cs.sjtu.edu.cn", "github": "", "project": "", "author_num": 2, "aff_unique_index": "0+0+0;0+0+0", "aff_unique_norm": "Shanghai Jiao Tong University", "aff_unique_dep": "Department of Computer Science and Engineering", "aff_unique_url": "https://www.sjtu.edu.cn", "aff_unique_abbr": "SJTU", "aff_campus_unique_index": "1+1;1+1", "aff_campus_unique": ";Shanghai", "aff_country_unique_index": "0+0+0;0+0+0", "aff_country_unique": "China" }, { "id": "2021.acl-short.96", "title": "Pre-training is a Hot Topic: Contextualized Document Embeddings Improve Topic Coherence", "track": "main", "status": "Short", "award": false, "abstract": "Topic models extract groups of words from documents, whose interpretation as a topic hopefully allows for a better understanding of the data. However, the resulting word groups are often not coherent, making them harder to interpret. Recently, neural topic models have shown improvements in overall coherence. Concurrently, contextual embeddings have advanced the state of the art of neural models in general. In this paper, we combine contextualized representations with neural topic models. We find that our approach produces more meaningful and coherent topics than traditional bag-of-words topic models and recent neural models. Our results indicate that future improvements in language models will translate into better topic models.", "author": "Federico Bianchi; Silvia Terragni; Dirk Hovy", "authorids": "/f/federico-bianchi/; /s/silvia-terragni/; /d/dirk-hovy/", "bibtex": "@inproceedings{bianchi-etal-2021-pre,\n title = \"Pre-training is a Hot Topic: Contextualized Document Embeddings Improve Topic Coherence\",\n author = \"Bianchi, Federico and\n Terragni, Silvia and\n Hovy, Dirk\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-short.96/\",\n doi = \"10.18653/v1/2021.acl-short.96\",\n pages = \"759--766\"\n}", "pdf": "https://aclanthology.org/2021.acl-short.96.pdf", "site": "https://aclanthology.org/2021.acl-short.96/", "pdf_size": 281917, "gs_citation": 384, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=2147766156802737102&as_sdt=5,44&sciodt=0,44&hl=en", "gs_version_total": 9, "aff": "Bocconi University; University of Milan-Bicocca; Bocconi University", "aff_domain": "unibocconi.it;campus.unimib.it;unibocconi.it", "email": "unibocconi.it;campus.unimib.it;unibocconi.it", "github": "", "project": "", "author_num": 3, "aff_unique_index": "0;1;0", "aff_unique_norm": "Bocconi University;University of Milan-Bicocca", "aff_unique_dep": ";", "aff_unique_url": "https://www.bocconi.edu;https://www.unimib.it", "aff_unique_abbr": "Bocconi;UNIMIB", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0;0;0", "aff_country_unique": "Italy" }, { "id": "2021.acl-long.353", "title": "Prefix-Tuning: Optimizing Continuous Prompts for Generation", "track": "main", "status": "Long", "award": false, "abstract": "Fine-tuning is the de facto way of leveraging large pretrained language models for downstream tasks. However, fine-tuning modifies all the language model parameters and therefore necessitates storing a full copy for each task. In this paper, we propose prefix-tuning, a lightweight alternative to fine-tuning for natural language generation tasks, which keeps language model parameters frozen and instead optimizes a sequence of continuous task-specific vectors, which we call the prefix. Prefix-tuning draws inspiration from prompting for language models, allowing subsequent tokens to attend to this prefix as if it were \u201cvirtual tokens\u201d. We apply prefix-tuning to GPT-2 for table-to-text generation and to BART for summarization. We show that by learning only 0.1% of the parameters, prefix-tuning obtains comparable performance in the full data setting, outperforms fine-tuning in low-data settings, and extrapolates better to examples with topics that are unseen during training.", "author": "Xiang Lisa Li; Percy Liang", "authorids": "/x/xiang-lisa-li/; /p/percy-liang/", "bibtex": "@inproceedings{li-liang-2021-prefix,\n title = \"Prefix-Tuning: Optimizing Continuous Prompts for Generation\",\n author = \"Li, Xiang Lisa and\n Liang, Percy\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.353/\",\n doi = \"10.18653/v1/2021.acl-long.353\",\n pages = \"4582--4597\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.353.pdf", "site": "https://aclanthology.org/2021.acl-long.353/", "pdf_size": 1568936, "gs_citation": 4642, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=8278065458136258265&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 7, "aff": "Stanford University; Stanford University", "aff_domain": "stanford.edu;cs.stanford.edu", "email": "stanford.edu;cs.stanford.edu", "github": "", "project": "", "author_num": 2, "aff_unique_index": "0;0", "aff_unique_norm": "Stanford University", "aff_unique_dep": "", "aff_unique_url": "https://www.stanford.edu", "aff_unique_abbr": "Stanford", "aff_campus_unique_index": "0;0", "aff_campus_unique": "Stanford", "aff_country_unique_index": "0;0", "aff_country_unique": "United States" }, { "id": "2021.acl-long.268", "title": "Prevent the Language Model from being Overconfident in Neural Machine Translation", "track": "main", "status": "Long", "award": false, "abstract": "The Neural Machine Translation (NMT) model is essentially a joint language model conditioned on both the source sentence and partial translation. Therefore, the NMT model naturally involves the mechanism of the Language Model (LM) that predicts the next token only based on partial translation. Despite its success, NMT still suffers from the hallucination problem, generating fluent but inadequate translations. The main reason is that NMT pays excessive attention to the partial translation while neglecting the source sentence to some extent, namely overconfidence of the LM. Accordingly, we define the Margin between the NMT and the LM, calculated by subtracting the predicted probability of the LM from that of the NMT model for each token. The Margin is negatively correlated to the overconfidence degree of the LM. Based on the property, we propose a Margin-based Token-level Objective (MTO) and a Margin-based Sentence-level Objective (MSO) to maximize the Margin for preventing the LM from being overconfident. Experiments on WMT14 English-to-German, WMT19 Chinese-to-English, and WMT14 English-to-French translation tasks demonstrate the effectiveness of our approach, with 1.36, 1.50, and 0.63 BLEU improvements, respectively, compared to the Transformer baseline. The human evaluation further verifies that our approaches improve translation adequacy as well as fluency.", "author": "Mengqi Miao; Fandong Meng; Yijin Liu; Xiao-Hua Zhou; Jie Zhou", "authorids": "/m/mengqi-miao/; /f/fandong-meng/; /y/yijin-liu/; /x/xiao-hua-zhou/; /j/jie-zhou/", "bibtex": "@inproceedings{miao-etal-2021-prevent,\n title = \"Prevent the Language Model from being Overconfident in Neural Machine Translation\",\n author = \"Miao, Mengqi and\n Meng, Fandong and\n Liu, Yijin and\n Zhou, Xiao-Hua and\n Zhou, Jie\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.268/\",\n doi = \"10.18653/v1/2021.acl-long.268\",\n pages = \"3456--3468\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.268.pdf", "site": "https://aclanthology.org/2021.acl-long.268/", "pdf_size": 856460, "gs_citation": 51, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=12027778464867596642&as_sdt=400005&sciodt=0,14&hl=en", "gs_version_total": 4, "aff": "Peking University, China; Pattern Recognition Center, WeChat AI, Tencent Inc, China; Pattern Recognition Center, WeChat AI, Tencent Inc, China; Beijing International Center for Mathematical Research, National Engineering Lab for Big Data Analysis and Applications, Department of Biostatistics, Peking University, Beijing, China; Pattern Recognition Center, WeChat AI, Tencent Inc, China", "aff_domain": "pku.edu.cn;tencent.com;tencent.com;math.pku.edu.cn;tencent.com", "email": "pku.edu.cn;tencent.com;tencent.com;math.pku.edu.cn;tencent.com", "github": "https://github.com/Mlair77/nmt_adequacy", "project": "", "author_num": 5, "aff_unique_index": "0;1;1;0;1", "aff_unique_norm": "Peking University;Tencent Inc", "aff_unique_dep": ";Pattern Recognition Center, WeChat AI", "aff_unique_url": "http://www.pku.edu.cn;https://www.tencent.com", "aff_unique_abbr": "Peking U;Tencent", "aff_campus_unique_index": "1", "aff_campus_unique": ";Beijing", "aff_country_unique_index": "0;0;0;0;0", "aff_country_unique": "China" }, { "id": "2021.acl-short.111", "title": "Preview, Attend and Review: Schema-Aware Curriculum Learning for Multi-Domain Dialogue State Tracking", "track": "main", "status": "Short", "award": false, "abstract": "Existing dialog state tracking (DST) models are trained with dialog data in a random order, neglecting rich structural information in a dataset. In this paper, we propose to use curriculum learning (CL) to better leverage both the curriculum structure and schema structure for task-oriented dialogs. Specifically, we propose a model-agnostic framework called Schema-aware Curriculum Learning for Dialog State Tracking (SaCLog), which consists of a preview module that pre-trains a DST model with schema information, a curriculum module that optimizes the model with CL, and a review module that augments mispredicted data to reinforce the CL training. We show that our proposed approach improves DST performance over both a transformer-based and RNN-based DST model (TripPy and TRADE) and achieves new state-of-the-art results on WOZ2.0 and MultiWOZ2.1.", "author": "Yinpei Dai; Hangyu Li; Yongbin Li; Jian Sun; Fei Huang; Luo Si; Xiaodan Zhu", "authorids": "/y/yinpei-dai/; /h/hangyu-li/; /y/yongbin-li/; /j/jian-sun/; /f/fei-huang/; /l/luo-si/; /x/xiaodan-zhu/", "bibtex": "@inproceedings{dai-etal-2021-preview,\n title = \"Preview, Attend and Review: Schema-Aware Curriculum Learning for Multi-Domain Dialogue State Tracking\",\n author = \"Dai, Yinpei and\n Li, Hangyu and\n Li, Yongbin and\n Sun, Jian and\n Huang, Fei and\n Si, Luo and\n Zhu, Xiaodan\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-short.111/\",\n doi = \"10.18653/v1/2021.acl-short.111\",\n pages = \"879--885\"\n}", "pdf": "https://aclanthology.org/2021.acl-short.111.pdf", "site": "https://aclanthology.org/2021.acl-short.111/", "pdf_size": 667434, "gs_citation": 57, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=11256095515717661429&as_sdt=40005&sciodt=0,10&hl=en", "gs_version_total": 4, "aff": "Alibaba Group\u2020; Alibaba Group\u2020; Alibaba Group\u2020\u2217; Alibaba Group\u2020; Alibaba Group\u2020; Alibaba Group\u2020; Ingenuity Labs Research Institute & ECE, Queen\u2019s University\u2021", "aff_domain": "alibaba-inc.com;alibaba-inc.com;alibaba-inc.com;alibaba-inc.com;alibaba-inc.com;alibaba-inc.com;gmail.com", "email": "alibaba-inc.com;alibaba-inc.com;alibaba-inc.com;alibaba-inc.com;alibaba-inc.com;alibaba-inc.com;gmail.com", "github": "", "project": "", "author_num": 7, "aff_unique_index": "0;0;0;0;0;0;1", "aff_unique_norm": "Alibaba Group;Queen\u2019s University", "aff_unique_dep": ";Ingenuity Labs Research Institute & ECE", "aff_unique_url": "https://www.alibaba.com;https://www.queensu.ca", "aff_unique_abbr": "Alibaba;Queen's U", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0;0;0;0;0;0;1", "aff_country_unique": "China;Canada" }, { "id": "2021.acl-long.532", "title": "Privacy at Scale: Introducing the PrivaSeer Corpus of Web Privacy Policies", "track": "main", "status": "Long", "award": false, "abstract": "Organisations disclose their privacy practices by posting privacy policies on their websites. Even though internet users often care about their digital privacy, they usually do not read privacy policies, since understanding them requires a significant investment of time and effort. Natural language processing has been used to create experimental tools to interpret privacy policies, but there has been a lack of large privacy policy corpora to facilitate the creation of large-scale semi-supervised and unsupervised models to interpret and simplify privacy policies. Thus, we present the PrivaSeer Corpus of 1,005,380 English language website privacy policies collected from the web. The number of unique websites represented in PrivaSeer is about ten times larger than the next largest public collection of web privacy policies, and it surpasses the aggregate of unique websites represented in all other publicly available privacy policy corpora combined. We describe a corpus creation pipeline with stages that include a web crawler, language detection, document classification, duplicate and near-duplicate removal, and content extraction. We employ an unsupervised topic modelling approach to investigate the contents of policy documents in the corpus and discuss the distribution of topics in privacy policies at web scale. We further investigate the relationship between privacy policy domain PageRanks and text features of the privacy policies. Finally, we use the corpus to pretrain PrivBERT, a transformer-based privacy policy language model, and obtain state of the art results on the data practice classification and question answering tasks.", "author": "Mukund Srinath; Shomir Wilson; C Lee Giles", "authorids": "/m/mukund-srinath/; /s/shomir-wilson/; /c/c-lee-giles/", "bibtex": "@inproceedings{srinath-etal-2021-privacy,\n title = \"Privacy at Scale: Introducing the {P}riva{S}eer Corpus of Web Privacy Policies\",\n author = \"Srinath, Mukund and\n Wilson, Shomir and\n Giles, C Lee\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.532/\",\n doi = \"10.18653/v1/2021.acl-long.532\",\n pages = \"6829--6839\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.532.pdf", "site": "https://aclanthology.org/2021.acl-long.532/", "pdf_size": 323880, "gs_citation": 64, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=7454071625303185767&as_sdt=5,33&sciodt=0,33&hl=en", "gs_version_total": 8, "aff": "College of Information Sciences and Technology, Pennsylvania State University, University Park, PA, USA; College of Information Sciences and Technology, Pennsylvania State University, University Park, PA, USA; College of Information Sciences and Technology, Pennsylvania State University, University Park, PA, USA", "aff_domain": "psu.edu;psu.edu;psu.edu", "email": "psu.edu;psu.edu;psu.edu", "github": "", "project": "", "author_num": 3, "aff_unique_index": "0;0;0", "aff_unique_norm": "Pennsylvania State University", "aff_unique_dep": "College of Information Sciences and Technology", "aff_unique_url": "https://www.psu.edu", "aff_unique_abbr": "PSU", "aff_campus_unique_index": "0;0;0", "aff_campus_unique": "University Park", "aff_country_unique_index": "0;0;0", "aff_country_unique": "United States" }, { "id": "2021.acl-long.257", "title": "Probabilistic, Structure-Aware Algorithms for Improved Variety, Accuracy, and Coverage of AMR Alignments", "track": "main", "status": "Long", "award": false, "abstract": "We present algorithms for aligning components of Abstract Meaning Representation (AMR) graphs to spans in English sentences. We leverage unsupervised learning in combination with heuristics, taking the best of both worlds from previous AMR aligners. Our unsupervised models, however, are more sensitive to graph substructures, without requiring a separate syntactic parse. Our approach covers a wider variety of AMR substructures than previously considered, achieves higher coverage of nodes and edges, and does so with higher accuracy. We will release our LEAMR datasets and aligner for use in research on AMR parsing, generation, and evaluation.", "author": "Austin Blodgett; Nathan Schneider", "authorids": "/a/austin-blodgett/; /n/nathan-schneider/", "bibtex": "@inproceedings{blodgett-schneider-2021-probabilistic,\n title = \"Probabilistic, Structure-Aware Algorithms for Improved Variety, Accuracy, and Coverage of {AMR} Alignments\",\n author = \"Blodgett, Austin and\n Schneider, Nathan\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.257/\",\n doi = \"10.18653/v1/2021.acl-long.257\",\n pages = \"3310--3321\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.257.pdf", "site": "https://aclanthology.org/2021.acl-long.257/", "pdf_size": 351094, "gs_citation": 20, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=2778763205372327559&as_sdt=400005&sciodt=0,14&hl=en", "gs_version_total": 7, "aff": "Georgetown University; Georgetown University", "aff_domain": "georgetown.edu;georgetown.edu", "email": "georgetown.edu;georgetown.edu", "github": "", "project": "", "author_num": 2, "aff_unique_index": "0;0", "aff_unique_norm": "Georgetown University", "aff_unique_dep": "", "aff_unique_url": "https://www.georgetown.edu", "aff_unique_abbr": "GU", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0;0", "aff_country_unique": "United States" }, { "id": "2021.acl-long.329", "title": "Probing Toxic Content in Large Pre-Trained Language Models", "track": "main", "status": "Long", "award": false, "abstract": "Large pre-trained language models (PTLMs) have been shown to carry biases towards different social groups which leads to the reproduction of stereotypical and toxic content by major NLP systems. We propose a method based on logistic regression classifiers to probe English, French, and Arabic PTLMs and quantify the potentially harmful content that they convey with respect to a set of templates. The templates are prompted by a name of a social group followed by a cause-effect relation. We use PTLMs to predict masked tokens at the end of a sentence in order to examine how likely they enable toxicity towards specific communities. We shed the light on how such negative content can be triggered within unrelated and benign contexts based on evidence from a large-scale study, then we explain how to take advantage of our methodology to assess and mitigate the toxicity transmitted by PTLMs.", "author": "Nedjma Ousidhoum; Xinran Zhao; Tianqing Fang; Yangqiu Song; Dit-Yan Yeung", "authorids": "/n/nedjma-ousidhoum/; /x/xinran-zhao/; /t/tianqing-fang/; /y/yangqiu-song/; /d/dit-yan-yeung/", "bibtex": "@inproceedings{ousidhoum-etal-2021-probing,\n title = \"Probing Toxic Content in Large Pre-Trained Language Models\",\n author = \"Ousidhoum, Nedjma and\n Zhao, Xinran and\n Fang, Tianqing and\n Song, Yangqiu and\n Yeung, Dit-Yan\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.329/\",\n doi = \"10.18653/v1/2021.acl-long.329\",\n pages = \"4262--4274\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.329.pdf", "site": "https://aclanthology.org/2021.acl-long.329/", "pdf_size": 390474, "gs_citation": 135, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=12024134194798238115&as_sdt=5,48&sciodt=0,48&hl=en", "gs_version_total": 5, "aff": "Department of Computer Science and Engineering, The Hong Kong University of Science and Technology; Department of Computer Science and Engineering, The Hong Kong University of Science and Technology; Department of Computer Science and Engineering, The Hong Kong University of Science and Technology; Department of Computer Science and Engineering, The Hong Kong University of Science and Technology; Department of Computer Science and Engineering, The Hong Kong University of Science and Technology", "aff_domain": "cse.ust.hk;connect.ust.hk;connect.ust.hk;cse.ust.hk;cse.ust.hk", "email": "cse.ust.hk;connect.ust.hk;connect.ust.hk;cse.ust.hk;cse.ust.hk", "github": "", "project": "", "author_num": 5, "aff_unique_index": "0;0;0;0;0", "aff_unique_norm": "The Hong Kong University of Science and Technology", "aff_unique_dep": "Department of Computer Science and Engineering", "aff_unique_url": "https://www.ust.hk", "aff_unique_abbr": "HKUST", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0;0;0;0;0", "aff_country_unique": "China" }, { "id": "2021.acl-long.326", "title": "Psycholinguistic Tripartite Graph Network for Personality Detection", "track": "main", "status": "Long", "award": false, "abstract": "Most of the recent work on personality detection from online posts adopts multifarious deep neural networks to represent the posts and builds predictive models in a data-driven manner, without the exploitation of psycholinguistic knowledge that may unveil the connections between one\u2019s language use and his psychological traits. In this paper, we propose a psycholinguistic knowledge-based tripartite graph network, TrigNet, which consists of a tripartite graph network and a BERT-based graph initializer. The graph network injects structural psycholinguistic knowledge in LIWC, a computerized instrument for psycholinguistic analysis, by constructing a heterogeneous tripartite graph. The initializer is employed to provide initial embeddings for the graph nodes. To reduce the computational cost in graph learning, we further propose a novel flow graph attention network (GAT) that only transmits messages between neighboring parties in the tripartite graph. Benefiting from the tripartite graph, TrigNet can aggregate post information from a psychological perspective, which is a novel way of exploiting domain knowledge. Extensive experiments on two datasets show that TrigNet outperforms the existing state-of-art model by 3.47 and 2.10 points in average F1. Moreover, the flow GAT reduces the FLOPS and Memory measures by 38% and 32%, respectively, in comparison to the original GAT in our setting.", "author": "Tao Yang; Feifan Yang; Haolan Ouyang; Xiaojun Quan", "authorids": "/t/tao-yang/; /f/feifan-yang/; /h/haolan-ouyang/; /x/xiaojun-quan/", "bibtex": "@inproceedings{yang-etal-2021-psycholinguistic,\n title = \"Psycholinguistic Tripartite Graph Network for Personality Detection\",\n author = \"Yang, Tao and\n Yang, Feifan and\n Ouyang, Haolan and\n Quan, Xiaojun\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.326/\",\n doi = \"10.18653/v1/2021.acl-long.326\",\n pages = \"4229--4239\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.326.pdf", "site": "https://aclanthology.org/2021.acl-long.326/", "pdf_size": 698300, "gs_citation": 42, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=3812815989846159113&as_sdt=5,44&sciodt=0,44&hl=en", "gs_version_total": 5, "aff": "School of Computer Science and Engineering, Sun Yat-sen University, China; School of Computer Science and Engineering, Sun Yat-sen University, China; School of Computer Science and Engineering, Sun Yat-sen University, China; School of Computer Science and Engineering, Sun Yat-sen University, China", "aff_domain": "mail2.sysu.edu.cn;mail2.sysu.edu.cn;mail2.sysu.edu.cn;mail.sysu.edu.cn", "email": "mail2.sysu.edu.cn;mail2.sysu.edu.cn;mail2.sysu.edu.cn;mail.sysu.edu.cn", "github": "", "project": "", "author_num": 4, "aff_unique_index": "0;0;0;0", "aff_unique_norm": "Sun Yat-sen University", "aff_unique_dep": "School of Computer Science and Engineering", "aff_unique_url": "http://www.sysu.edu.cn", "aff_unique_abbr": "SYSU", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0;0;0;0", "aff_country_unique": "China" }, { "id": "2021.acl-short.83", "title": "QA-Driven Zero-shot Slot Filling with Weak Supervision Pretraining", "track": "main", "status": "Short", "award": false, "abstract": "Slot-filling is an essential component for building task-oriented dialog systems. In this work, we focus on the zero-shot slot-filling problem, where the model needs to predict slots and their values, given utterances from new domains without training on the target domain. Prior methods directly encode slot descriptions to generalize to unseen slot types. However, raw slot descriptions are often ambiguous and do not encode enough semantic information, limiting the models\u2019 zero-shot capability. To address this problem, we introduce QA-driven slot filling (QASF), which extracts slot-filler spans from utterances with a span-based QA model. We use a linguistically motivated questioning strategy to turn descriptions into questions, allowing the model to generalize to unseen slot types. Moreover, our QASF model can benefit from weak supervision signals from QA pairs synthetically generated from unlabeled conversations. Our full system substantially outperforms baselines by over 5% on the SNIPS benchmark.", "author": "Xinya Du; Luheng He; Qi Li; Dian Yu; Panupong Pasupat; Yuan Zhang", "authorids": "/x/xinya-du/; /l/luheng-he/; /q/qi-li/; /d/dian-yu/; /p/panupong-pasupat/; /y/yuan-zhang/", "bibtex": "@inproceedings{du-etal-2021-qa,\n title = \"{QA}-Driven Zero-shot Slot Filling with Weak Supervision Pretraining\",\n author = \"Du, Xinya and\n He, Luheng and\n Li, Qi and\n Yu, Dian and\n Pasupat, Panupong and\n Zhang, Yuan\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-short.83/\",\n doi = \"10.18653/v1/2021.acl-short.83\",\n pages = \"654--664\"\n}", "pdf": "https://aclanthology.org/2021.acl-short.83.pdf", "site": "https://aclanthology.org/2021.acl-short.83/", "pdf_size": 403617, "gs_citation": 30, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=15492055226704078987&as_sdt=5,33&sciodt=0,33&hl=en", "gs_version_total": 2, "aff": "Cornell University; Google Research; Google Assistant; University of California, Davis; Google Research; Google Research", "aff_domain": "cs.cornell.edu;google.com;google.com;ucdavis.edu;google.com;google.com", "email": "cs.cornell.edu;google.com;google.com;ucdavis.edu;google.com;google.com", "github": "", "project": "", "author_num": 6, "aff_unique_index": "0;1;1;2;1;1", "aff_unique_norm": "Cornell University;Google;University of California, Davis", "aff_unique_dep": ";Google Research;", "aff_unique_url": "https://www.cornell.edu;https://research.google;https://www.ucdavis.edu", "aff_unique_abbr": "Cornell;Google Research;UC Davis", "aff_campus_unique_index": "1;1;2;1;1", "aff_campus_unique": ";Mountain View;Davis", "aff_country_unique_index": "0;0;0;0;0;0", "aff_country_unique": "United States" }, { "id": "2021.acl-long.177", "title": "QASR: QCRI Aljazeera Speech Resource A Large Scale Annotated Arabic Speech Corpus", "track": "main", "status": "Long", "award": false, "abstract": "We introduce the largest transcribed Arabic speech corpus, QASR, collected from the broadcast domain. This multi-dialect speech dataset contains 2,000 hours of speech sampled at 16kHz crawled from Aljazeera news channel. The dataset is released with lightly supervised transcriptions, aligned with the audio segments. Unlike previous datasets, QASR contains linguistically motivated segmentation, punctuation, speaker information among others. QASR is suitable for training and evaluating speech recognition systems, acoustics- and/or linguistics- based Arabic dialect identification, punctuation restoration, speaker identification, speaker linking, and potentially other NLP modules for spoken data. In addition to QASR transcription, we release a dataset of 130M words to aid in designing and training a better language model. We show that end-to-end automatic speech recognition trained on QASR reports a competitive word error rate compared to the previous MGB-2 corpus. We report baseline results for downstream natural language processing tasks such as named entity recognition using speech transcript. We also report the first baseline for Arabic punctuation restoration. We make the corpus available for the research community.", "author": "Hamdy Mubarak; Amir Hussein; Shammur Absar Chowdhury; Ahmed Ali", "authorids": "/h/hamdy-mubarak/; /a/amir-hussein/; /s/shammur-absar-chowdhury/; /a/ahmed-ali/", "bibtex": "@inproceedings{mubarak-etal-2021-qasr,\n title = \"{QASR}: {QCRI} Aljazeera Speech Resource A Large Scale Annotated {A}rabic Speech Corpus\",\n author = \"Mubarak, Hamdy and\n Hussein, Amir and\n Chowdhury, Shammur Absar and\n Ali, Ahmed\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.177/\",\n doi = \"10.18653/v1/2021.acl-long.177\",\n pages = \"2274--2285\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.177.pdf", "site": "https://aclanthology.org/2021.acl-long.177/", "pdf_size": 1401216, "gs_citation": 50, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=18353696478329292035&as_sdt=5,33&sciodt=0,33&hl=en", "gs_version_total": 6, "aff": "Qatar Computing Research Institute, HBKU, Doha, Qatar; Kanari AI, California, USA; Qatar Computing Research Institute, HBKU, Doha, Qatar; Qatar Computing Research Institute, HBKU, Doha, Qatar", "aff_domain": "arabicspeech.org; ; ; ", "email": "arabicspeech.org; ; ; ", "github": "", "project": "https://arabicspeech.org/", "author_num": 4, "aff_unique_index": "0;1;0;0", "aff_unique_norm": "Qatar Computing Research Institute;Kanari AI", "aff_unique_dep": ";", "aff_unique_url": "https://www.qcri.org;", "aff_unique_abbr": "QCRI;", "aff_campus_unique_index": "0;1;0;0", "aff_campus_unique": "Doha;California", "aff_country_unique_index": "0;1;0;0", "aff_country_unique": "Qatar;United States" }, { "id": "2021.acl-short.44", "title": "Quantifying and Avoiding Unfair Qualification Labour in Crowdsourcing", "track": "main", "status": "Short", "award": false, "abstract": "Extensive work has argued in favour of paying crowd workers a wage that is at least equivalent to the U.S. federal minimum wage. Meanwhile, research on collecting high quality annotations suggests using a qualification that requires workers to have previously completed a certain number of tasks. If most requesters who pay fairly require workers to have completed a large number of tasks already then workers need to complete a substantial amount of poorly paid work before they can earn a fair wage. Through analysis of worker discussions and guidance for researchers, we estimate that workers spend approximately 2.25 months of full time effort on poorly paid tasks in order to get the qualifications needed for better paid tasks. We discuss alternatives to this qualification and conduct a study of the correlation between qualifications and work quality on two NLP tasks. We find that it is possible to reduce the burden on workers while still collecting high quality data.", "author": "Jonathan K. Kummerfeld", "authorids": "/j/jonathan-k-kummerfeld/", "bibtex": "@inproceedings{kummerfeld-2021-quantifying,\n title = \"Quantifying and Avoiding Unfair Qualification Labour in Crowdsourcing\",\n author = \"Kummerfeld, Jonathan K.\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-short.44/\",\n doi = \"10.18653/v1/2021.acl-short.44\",\n pages = \"343--349\"\n}", "pdf": "https://aclanthology.org/2021.acl-short.44.pdf", "site": "https://aclanthology.org/2021.acl-short.44/", "pdf_size": 509639, "gs_citation": 20, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=3830294739004292099&as_sdt=5,33&sciodt=0,33&hl=en", "gs_version_total": 5, "aff": "Computer Science & Engineering, University of Michigan, Ann Arbor", "aff_domain": "umich.edu", "email": "umich.edu", "github": "", "project": "", "author_num": 1, "aff_unique_index": "0", "aff_unique_norm": "University of Michigan", "aff_unique_dep": "Computer Science & Engineering", "aff_unique_url": "https://www.umich.edu", "aff_unique_abbr": "UM", "aff_campus_unique_index": "0", "aff_campus_unique": "Ann Arbor", "aff_country_unique_index": "0", "aff_country_unique": "United States" }, { "id": "2021.acl-long.520", "title": "Question Answering Over Temporal Knowledge Graphs", "track": "main", "status": "Long", "award": false, "abstract": "Temporal Knowledge Graphs (Temporal KGs) extend regular Knowledge Graphs by providing temporal scopes (start and end times) on each edge in the KG. While Question Answering over KG (KGQA) has received some attention from the research community, QA over Temporal KGs (Temporal KGQA) is a relatively unexplored area. Lack of broad coverage datasets has been another factor limiting progress in this area. We address this challenge by presenting CRONQUESTIONS, the largest known Temporal KGQA dataset, clearly stratified into buckets of structural complexity. CRONQUESTIONS expands the only known previous dataset by a factor of 340x. We find that various state-of-the-art KGQA methods fall far short of the desired performance on this new dataset. In response, we also propose CRONKGQA, a transformer-based solution that exploits recent advances in Temporal KG embeddings, and achieves performance superior to all baselines, with an increase of 120% in accuracy over the next best performing method. Through extensive experiments, we give detailed insights into the workings of CRONKGQA, as well as situations where significant further improvements appear possible. In addition to the dataset, we have released our code as well.", "author": "Apoorv Saxena; Soumen Chakrabarti; Partha Talukdar", "authorids": "/a/apoorv-saxena/; /s/soumen-chakrabarti/; /p/partha-talukdar/", "bibtex": "@inproceedings{saxena-etal-2021-question,\n title = \"Question Answering Over Temporal Knowledge Graphs\",\n author = \"Saxena, Apoorv and\n Chakrabarti, Soumen and\n Talukdar, Partha\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.520/\",\n doi = \"10.18653/v1/2021.acl-long.520\",\n pages = \"6663--6676\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.520.pdf", "site": "https://aclanthology.org/2021.acl-long.520/", "pdf_size": 499925, "gs_citation": 155, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=11335557615085231623&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 7, "aff": "Indian Institute of Science; Indian Institute of Technology Bombay; Google Research India", "aff_domain": "iisc.ac.in;cse.iitb.ac.in;google.com", "email": "iisc.ac.in;cse.iitb.ac.in;google.com", "github": "", "project": "", "author_num": 3, "aff_unique_index": "0;1;2", "aff_unique_norm": "Indian Institute of Science;Indian Institute of Technology Bombay;Google", "aff_unique_dep": ";;Google Research", "aff_unique_url": "https://www.iisc.ac.in;https://www.iitb.ac.in;https://research.google", "aff_unique_abbr": "IISc;IIT Bombay;Google Research India", "aff_campus_unique_index": "1;2", "aff_campus_unique": ";Bombay;Bangalore", "aff_country_unique_index": "0;0;0", "aff_country_unique": "India" }, { "id": "2021.acl-short.88", "title": "Question Generation for Adaptive Education", "track": "main", "status": "Short", "award": false, "abstract": "Intelligent and adaptive online education systems aim to make high-quality education available for a diverse range of students. However, existing systems usually depend on a pool of hand-made questions, limiting how fine-grained and open-ended they can be in adapting to individual students. We explore targeted question generation as a controllable sequence generation task. We first show how to fine-tune pre-trained language models for deep knowledge tracing (LM-KT). This model accurately predicts the probability of a student answering a question correctly, and generalizes to questions not seen in training. We then use LM-KT to specify the objective and data for training a model to generate questions conditioned on the student and target difficulty. Our results show we succeed at generating novel, well-calibrated language translation questions for second language learners from a real online education platform.", "author": "Megha Srivastava; Noah Goodman", "authorids": "/m/megha-srivastava/; /n/noah-goodman/", "bibtex": "@inproceedings{srivastava-goodman-2021-question,\n title = \"Question Generation for Adaptive Education\",\n author = \"Srivastava, Megha and\n Goodman, Noah\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-short.88/\",\n doi = \"10.18653/v1/2021.acl-short.88\",\n pages = \"692--701\"\n}", "pdf": "https://aclanthology.org/2021.acl-short.88.pdf", "site": "https://aclanthology.org/2021.acl-short.88/", "pdf_size": 369234, "gs_citation": 57, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=13336967710079808301&as_sdt=5,33&sciodt=0,33&hl=en", "gs_version_total": 4, "aff": "Stanford University; Stanford University", "aff_domain": "cs.stanford.edu;stanford.edu", "email": "cs.stanford.edu;stanford.edu", "github": "", "project": "", "author_num": 2, "aff_unique_index": "0;0", "aff_unique_norm": "Stanford University", "aff_unique_dep": "", "aff_unique_url": "https://www.stanford.edu", "aff_unique_abbr": "Stanford", "aff_campus_unique_index": "0;0", "aff_campus_unique": "Stanford", "aff_country_unique_index": "0;0", "aff_country_unique": "United States" }, { "id": "2021.acl-short.95", "title": "Quotation Recommendation and Interpretation Based on Transformation from Queries to Quotations", "track": "main", "status": "Short", "award": false, "abstract": "To help individuals express themselves better, quotation recommendation is receiving growing attention. Nevertheless, most prior efforts focus on modeling quotations and queries separately and ignore the relationship between the quotations and the queries. In this work, we introduce a transformation matrix that directly maps the query representations to quotation representations. To better learn the mapping relationship, we employ a mapping loss that minimizes the distance of two semantic spaces (one for quotation and another for mapped-query). Furthermore, we explore using the words in history queries to interpret the figurative language of quotations, where quotation-aware attention is applied on top of history queries to highlight the indicator words. Experiments on two datasets in English and Chinese show that our model outperforms previous state-of-the-art models.", "author": "Lingzhi Wang; Xingshan Zeng; Kam-Fai Wong", "authorids": "/l/lingzhi-wang/; /x/xingshan-zeng/; /k/kam-fai-wong/", "bibtex": "@inproceedings{wang-etal-2021-quotation,\n title = \"Quotation Recommendation and Interpretation Based on Transformation from Queries to Quotations\",\n author = \"Wang, Lingzhi and\n Zeng, Xingshan and\n Wong, Kam-Fai\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-short.95/\",\n doi = \"10.18653/v1/2021.acl-short.95\",\n pages = \"754--758\"\n}", "pdf": "https://aclanthology.org/2021.acl-short.95.pdf", "site": "https://aclanthology.org/2021.acl-short.95/", "pdf_size": 360659, "gs_citation": 9, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=8573786210458034795&as_sdt=5,31&sciodt=0,31&hl=en", "gs_version_total": 4, "aff": "The Chinese University of Hong Kong, Hong Kong, China + MoE Key Laboratory of High Con\ufb01dence Software Technologies, China; Huawei Noah\u2019s Ark Lab, China; The Chinese University of Hong Kong, Hong Kong, China + MoE Key Laboratory of High Con\ufb01dence Software Technologies, China", "aff_domain": "se.cuhk.edu.hk;huawei.com;se.cuhk.edu.hk", "email": "se.cuhk.edu.hk;huawei.com;se.cuhk.edu.hk", "github": "https://github.com/Lingzhi-WANG/Quotation-Recommendation", "project": "", "author_num": 3, "aff_unique_index": "0+1;2;0+1", "aff_unique_norm": "The Chinese University of Hong Kong;MoE Key Laboratory of High Con\ufb01dence Software Technologies;Huawei Noah\u2019s Ark Lab", "aff_unique_dep": ";High Con\ufb01dence Software Technologies;", "aff_unique_url": "https://www.cuhk.edu.hk;;https://www.huawei.com/en/ai/noahs-ark-lab", "aff_unique_abbr": "CUHK;;HNAL", "aff_campus_unique_index": "0;0", "aff_campus_unique": "Hong Kong;", "aff_country_unique_index": "0+0;0;0+0", "aff_country_unique": "China" }, { "id": "2021.acl-long.379", "title": "R2D2: Recursive Transformer based on Differentiable Tree for Interpretable Hierarchical Language Modeling", "track": "main", "status": "Long", "award": false, "abstract": "Human language understanding operates at multiple levels of granularity (e.g., words, phrases, and sentences) with increasing levels of abstraction that can be hierarchically combined. However, existing deep models with stacked layers do not explicitly model any sort of hierarchical process. In this paper, we propose a recursive Transformer model based on differentiable CKY style binary trees to emulate this composition process, and we extend the bidirectional language model pre-training objective to this architecture, attempting to predict each word given its left and right abstraction nodes. To scale up our approach, we also introduce an efficient pruning and growing algorithm to reduce the time complexity and enable encoding in linear time. Experimental results on language modeling and unsupervised parsing show the effectiveness of our approach.", "author": "Xiang Hu; Haitao Mi; Zujie Wen; Yafang Wang; Yi Su; Jing Zheng; Gerard de Melo", "authorids": "/x/xiang-hu/; /h/haitao-mi/; /z/zujie-wen/; /y/yafang-wang/; /y/yi-su/; /j/jing-zheng/; /g/gerard-de-melo/", "bibtex": "@inproceedings{hu-etal-2021-r2d2,\n title = \"{R}2{D}2: Recursive Transformer based on Differentiable Tree for Interpretable Hierarchical Language Modeling\",\n author = \"Hu, Xiang and\n Mi, Haitao and\n Wen, Zujie and\n Wang, Yafang and\n Su, Yi and\n Zheng, Jing and\n de Melo, Gerard\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.379/\",\n doi = \"10.18653/v1/2021.acl-long.379\",\n pages = \"4897--4908\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.379.pdf", "site": "https://aclanthology.org/2021.acl-long.379/", "pdf_size": 598929, "gs_citation": 27, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=5587052623902460398&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 6, "aff": "Ant Financial Services Group\u2020; Ant Financial Services Group\u2020; Ant Financial Services Group\u2020; Ant Financial Services Group\u2020; Ant Financial Services Group\u2020; Ant Financial Services Group\u2020; Hasso Plattner Institute / University of Potsdam\u2021", "aff_domain": "alibaba-inc.com;alibaba-inc.com;alibaba-inc.com;alibaba-inc.com;alibaba-inc.com;alibaba-inc.com;demelo.org", "email": "alibaba-inc.com;alibaba-inc.com;alibaba-inc.com;alibaba-inc.com;alibaba-inc.com;alibaba-inc.com;demelo.org", "github": "https://github.com/alipay/StructuredLM_RTD", "project": "", "author_num": 7, "aff_unique_index": "0;0;0;0;0;0;1", "aff_unique_norm": "Ant Financial Services Group;Hasso Plattner Institute", "aff_unique_dep": ";", "aff_unique_url": "https://www.antgroup.com;https://www.hpi.de", "aff_unique_abbr": "Ant Financial;HPI", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0;0;0;0;0;0;1", "aff_country_unique": "China;Germany" }, { "id": "2021.acl-long.341", "title": "RADDLE: An Evaluation Benchmark and Analysis Platform for Robust Task-oriented Dialog Systems", "track": "main", "status": "Long", "award": false, "abstract": "For task-oriented dialog systems to be maximally useful, it must be able to process conversations in a way that is (1) generalizable with a small number of training examples for new task domains, and (2) robust to user input in various styles, modalities, or domains. In pursuit of these goals, we introduce the RADDLE benchmark, a collection of corpora and tools for evaluating the performance of models across a diverse set of domains. By including tasks with limited training data, RADDLE is designed to favor and encourage models with a strong generalization ability. RADDLE also includes a diagnostic checklist that facilitates detailed robustness analysis in aspects such as language variations, speech errors, unseen entities, and out-of-domain utterances. We evaluate recent state-of-the-art systems based on pre-training and fine-tuning, and find that grounded pre-training on heterogeneous dialog corpora performs better than training a separate model per domain. Adversarial training is also proposed to improve model robustness against noisy inputs. Overall, existing models are less than satisfactory in robustness evaluation, which suggests opportunities for future improvement.", "author": "Baolin Peng; Chunyuan Li; Zhu Zhang; Chenguang Zhu; Jinchao Li; Jianfeng Gao", "authorids": "/b/baolin-peng/; /c/chunyuan-li/; /z/zhu-zhang/; /c/chenguang-zhu/; /j/jinchao-li/; /j/jianfeng-gao/", "bibtex": "@inproceedings{peng-etal-2021-raddle,\n title = \"{RADDLE}: An Evaluation Benchmark and Analysis Platform for Robust Task-oriented Dialog Systems\",\n author = \"Peng, Baolin and\n Li, Chunyuan and\n Zhang, Zhu and\n Zhu, Chenguang and\n Li, Jinchao and\n Gao, Jianfeng\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.341/\",\n doi = \"10.18653/v1/2021.acl-long.341\",\n pages = \"4418--4429\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.341.pdf", "site": "https://aclanthology.org/2021.acl-long.341/", "pdf_size": 4066144, "gs_citation": 49, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=1956776281119382587&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 4, "aff": "Microsoft Research, Redmond, WA; Microsoft Research, Redmond, WA; Microsoft Research, Redmond, WA + Iowa State University / Ames, IA; Microsoft Research, Redmond, WA; Microsoft Research, Redmond, WA; Microsoft Research, Redmond, WA", "aff_domain": "microsoft.com;microsoft.com;microsoft.com;microsoft.com;microsoft.com;iastate.edu", "email": "microsoft.com;microsoft.com;microsoft.com;microsoft.com;microsoft.com;iastate.edu", "github": "", "project": "http://aka.ms/raddle", "author_num": 6, "aff_unique_index": "0;0;0+1;0;0;0", "aff_unique_norm": "Microsoft Research;Iowa State University", "aff_unique_dep": ";", "aff_unique_url": "https://www.microsoft.com/en-us/research;https://www.iastate.edu", "aff_unique_abbr": "MSR;ISU", "aff_campus_unique_index": "0;0;0+1;0;0;0", "aff_campus_unique": "Redmond;Ames", "aff_country_unique_index": "0;0;0+0;0;0;0", "aff_country_unique": "United States" }, { "id": "2021.acl-long.550", "title": "RAW-C: Relatedness of Ambiguous Words in Context (A New Lexical Resource for English)", "track": "main", "status": "Long", "award": false, "abstract": "Most words are ambiguous\u2014-i.e., they convey distinct meanings in different contexts\u2014-and even the meanings of unambiguous words are context-dependent. Both phenomena present a challenge for NLP. Recently, the advent of contextualized word embeddings has led to success on tasks involving lexical ambiguity, such as Word Sense Disambiguation. However, there are few tasks that directly evaluate how well these contextualized embeddings accommodate the more continuous, dynamic nature of word meaning\u2014-particularly in a way that matches human intuitions. We introduce RAW-C, a dataset of graded, human relatedness judgments for 112 ambiguous words in context (with 672 sentence pairs total), as well as human estimates of sense dominance. The average inter-annotator agreement (assessed using a leave-one-annotator-out method) was 0.79. We then show that a measure of cosine distance, computed using contextualized embeddings from BERT and ELMo, correlates with human judgments, but that cosine distance also systematically underestimates how similar humans find uses of the same sense of a word to be, and systematically overestimates how similar humans find uses of different-sense homonyms. Finally, we propose a synthesis between psycholinguistic theories of the mental lexicon and computational models of lexical semantics.", "author": "Sean Trott; Benjamin Bergen", "authorids": "/s/sean-trott/; /b/benjamin-bergen/", "bibtex": "@inproceedings{trott-bergen-2021-raw,\n title = \"{RAW}-{C}: Relatedness of Ambiguous Words in Context (A New Lexical Resource for {E}nglish)\",\n author = \"Trott, Sean and\n Bergen, Benjamin\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.550/\",\n doi = \"10.18653/v1/2021.acl-long.550\",\n pages = \"7077--7087\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.550.pdf", "site": "https://aclanthology.org/2021.acl-long.550/", "pdf_size": 371913, "gs_citation": 28, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=15572844448484300132&as_sdt=80000005&sciodt=0,23&hl=en", "gs_version_total": 5, "aff": "University of California, San Diego; University of California, San Diego", "aff_domain": "ucsd.edu;ucsd.edu", "email": "ucsd.edu;ucsd.edu", "github": "", "project": "", "author_num": 2, "aff_unique_index": "0;0", "aff_unique_norm": "University of California, San Diego", "aff_unique_dep": "", "aff_unique_url": "https://www.ucsd.edu", "aff_unique_abbr": "UCSD", "aff_campus_unique_index": "0;0", "aff_campus_unique": "San Diego", "aff_country_unique_index": "0;0", "aff_country_unique": "United States" }, { "id": "2021.acl-short.41", "title": "ROPE: Reading Order Equivariant Positional Encoding for Graph-based Document Information Extraction", "track": "main", "status": "Short", "award": false, "abstract": "Natural reading orders of words are crucial for information extraction from form-like documents. Despite recent advances in Graph Convolutional Networks (GCNs) on modeling spatial layout patterns of documents, they have limited ability to capture reading orders of given word-level node representations in a graph. We propose Reading Order Equivariant Positional Encoding (ROPE), a new positional encoding technique designed to apprehend the sequential presentation of words in documents. ROPE generates unique reading order codes for neighboring words relative to the target word given a word-level graph connectivity. We study two fundamental document entity extraction tasks including word labeling and word grouping on the public FUNSD dataset and a large-scale payment dataset. We show that ROPE consistently improves existing GCNs with a margin up to 8.4% F1-score.", "author": "Chen-Yu Lee; Chun-Liang Li; Chu Wang; Renshen Wang; Yasuhisa Fujii; Siyang Qin; Ashok Popat; Tomas Pfister", "authorids": "/c/chen-yu-lee/; /c/chun-liang-li/; /c/chu-wang/; /r/renshen-wang/; /y/yasuhisa-fujii/; /s/siyang-qin/; /a/ashok-popat/; /t/tomas-pfister/", "bibtex": "@inproceedings{lee-etal-2021-rope,\n title = \"{ROPE}: Reading Order Equivariant Positional Encoding for Graph-based Document Information Extraction\",\n author = \"Lee, Chen-Yu and\n Li, Chun-Liang and\n Wang, Chu and\n Wang, Renshen and\n Fujii, Yasuhisa and\n Qin, Siyang and\n Popat, Ashok and\n Pfister, Tomas\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-short.41/\",\n doi = \"10.18653/v1/2021.acl-short.41\",\n pages = \"314--321\"\n}", "pdf": "https://aclanthology.org/2021.acl-short.41.pdf", "site": "https://aclanthology.org/2021.acl-short.41/", "pdf_size": 2946520, "gs_citation": 30, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=16090262808832013475&as_sdt=5,33&sciodt=0,33&hl=en", "gs_version_total": 6, "aff": "Google Cloud AI; Google Cloud AI; McGill University + Google Research; Google Research; Google Research; Google Research; Google Research; Google Cloud AI", "aff_domain": "google.com;google.com;mail.mcgill.ca;google.com;google.com;google.com;google.com;google.com", "email": "google.com;google.com;mail.mcgill.ca;google.com;google.com;google.com;google.com;google.com", "github": "", "project": "", "author_num": 8, "aff_unique_index": "0;0;1+0;0;0;0;0;0", "aff_unique_norm": "Google;McGill University", "aff_unique_dep": "Google Cloud AI;", "aff_unique_url": "https://cloud.google.com/ai;https://www.mcgill.ca", "aff_unique_abbr": "Google Cloud AI;McGill", "aff_campus_unique_index": "0;0;0;0;0;0;0;0", "aff_campus_unique": "Mountain View;", "aff_country_unique_index": "0;0;1+0;0;0;0;0;0", "aff_country_unique": "United States;Canada" }, { "id": "2021.acl-long.229", "title": "Rational LAMOL: A Rationale-based Lifelong Learning Framework", "track": "main", "status": "Long", "award": false, "abstract": "Lifelong learning (LL) aims to train a neural network on a stream of tasks while retaining knowledge from previous tasks. However, many prior attempts in NLP still suffer from the catastrophic forgetting issue, where the model completely forgets what it just learned in the previous tasks. In this paper, we introduce Rational LAMOL, a novel end-to-end LL framework for language models. In order to alleviate catastrophic forgetting, Rational LAMOL enhances LAMOL, a recent LL model, by applying critical freezing guided by human rationales. When the human rationales are not available, we propose exploiting unsupervised generated rationales as substitutions. In the experiment, we tested Rational LAMOL on permutations of three datasets from the ERASER benchmark. The results show that our proposed framework outperformed vanilla LAMOL on most permutations. Furthermore, unsupervised rationale generation was able to consistently improve the overall LL performance from the baseline without relying on human-annotated rationales.", "author": "Kasidis Kanwatchara; Thanapapas Horsuwan; Piyawat Lertvittayakumjorn; Boonserm Kijsirikul; Peerapon Vateekul", "authorids": "/k/kasidis-kanwatchara/; /t/thanapapas-horsuwan/; /p/piyawat-lertvittayakumjorn/; /b/boonserm-kijsirikul/; /p/peerapon-vateekul/", "bibtex": "@inproceedings{kanwatchara-etal-2021-rational,\n title = \"Rational {LAMOL}: A Rationale-based Lifelong Learning Framework\",\n author = \"Kanwatchara, Kasidis and\n Horsuwan, Thanapapas and\n Lertvittayakumjorn, Piyawat and\n Kijsirikul, Boonserm and\n Vateekul, Peerapon\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.229/\",\n doi = \"10.18653/v1/2021.acl-long.229\",\n pages = \"2942--2953\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.229.pdf", "site": "https://aclanthology.org/2021.acl-long.229/", "pdf_size": 850928, "gs_citation": 21, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=885936901906849786&as_sdt=5,44&sciodt=0,44&hl=en", "gs_version_total": 3, "aff": "Department of Computer Engineering, Faculty of Engineering, Chulalongkorn University, Thailand; Department of Computer Engineering, Faculty of Engineering, Chulalongkorn University, Thailand; Department of Computing, Imperial College London, UK; Department of Computer Engineering, Faculty of Engineering, Chulalongkorn University, Thailand; Department of Computer Engineering, Faculty of Engineering, Chulalongkorn University, Thailand", "aff_domain": "gmail.com;gmail.com;imperial.ac.uk;chula.ac.th;chula.ac.th", "email": "gmail.com;gmail.com;imperial.ac.uk;chula.ac.th;chula.ac.th", "github": "https://github.com/kanwatchara-k/r_lamol", "project": "", "author_num": 5, "aff_unique_index": "0;0;1;0;0", "aff_unique_norm": "Chulalongkorn University;Imperial College London", "aff_unique_dep": "Department of Computer Engineering;Department of Computing", "aff_unique_url": "http://www.chula.ac.th;https://www.imperial.ac.uk", "aff_unique_abbr": "Chula;Imperial", "aff_campus_unique_index": "1", "aff_campus_unique": ";London", "aff_country_unique_index": "0;0;1;0;0", "aff_country_unique": "Thailand;United Kingdom" }, { "id": "2021.acl-long.554", "title": "ReadOnce Transformers: Reusable Representations of Text for Transformers", "track": "main", "status": "Long", "award": false, "abstract": "We present ReadOnce Transformers, an approach to convert a transformer-based model into one that can build an information-capturing, task-independent, and compressed representation of text. The resulting representation is reusable across different examples and tasks, thereby requiring a document shared across many examples or tasks to only be read once. This leads to faster training and evaluation of models. Additionally, we extend standard text-to-text transformer models to Representation+Text-to-text models, and evaluate on multiple downstream tasks: multi-hop QA, abstractive QA, and long-document summarization. Our one-time computed representation results in a 2x-5x speedup compared to standard text-to-text models, while the compression also allows existing language models to handle longer documents without the need for designing new pre-trained models.", "author": "Shih-Ting Lin; Ashish Sabharwal; Tushar Khot", "authorids": "/s/shih-ting-lin/; /a/ashish-sabharwal/; /t/tushar-khot/", "bibtex": "@inproceedings{lin-etal-2021-readonce,\n title = \"{R}ead{O}nce Transformers: Reusable Representations of Text for Transformers\",\n author = \"Lin, Shih-Ting and\n Sabharwal, Ashish and\n Khot, Tushar\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.554/\",\n doi = \"10.18653/v1/2021.acl-long.554\",\n pages = \"7129--7141\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.554.pdf", "site": "https://aclanthology.org/2021.acl-long.554/", "pdf_size": 970139, "gs_citation": 4, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=9624659628212161968&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 4, "aff": "University of Texas, Austin, U.S.A. + Allen Institute for AI, Seattle, U.S.A.; Allen Institute for AI, Seattle, U.S.A.; Allen Institute for AI, Seattle, U.S.A.", "aff_domain": "utexas.edu;allenai.org;allenai.org", "email": "utexas.edu;allenai.org;allenai.org", "github": "", "project": "", "author_num": 3, "aff_unique_index": "0+1;1;1", "aff_unique_norm": "University of Texas at Austin;Allen Institute for AI", "aff_unique_dep": ";", "aff_unique_url": "https://www.utexas.edu;https://allenai.org", "aff_unique_abbr": "UT Austin;AI2", "aff_campus_unique_index": "0+1;1;1", "aff_campus_unique": "Austin;Seattle", "aff_country_unique_index": "0+0;0;0", "aff_country_unique": "United States" }, { "id": "2021.acl-long.396", "title": "Reasoning over Entity-Action-Location Graph for Procedural Text Understanding", "track": "main", "status": "Long", "award": false, "abstract": "Procedural text understanding aims at tracking the states (e.g., create, move, destroy) and locations of the entities mentioned in a given paragraph. To effectively track the states and locations, it is essential to capture the rich semantic relations between entities, actions, and locations in the paragraph. Although recent works have achieved substantial progress, most of them focus on leveraging the inherent constraints or incorporating external knowledge for state prediction. The rich semantic relations in the given paragraph are largely overlooked. In this paper, we propose a novel approach (REAL) to procedural text understanding, where we build a general framework to systematically model the entity-entity, entity-action, and entity-location relations using a graph neural network. We further develop algorithms for graph construction, representation learning, and state and location tracking. We evaluate the proposed approach on two benchmark datasets, ProPara, and Recipes. The experimental results show that our method outperforms strong baselines by a large margin, i.e., 5.0% on ProPara and 3.2% on Recipes, illustrating the utility of semantic relations and the effectiveness of the graph-based reasoning model.", "author": "Hao Huang; Xiubo Geng; Jian Pei; Guodong Long; Daxin Jiang", "authorids": "/h/hao-huang/; /x/xiubo-geng/; /j/jian-pei/; /g/guodong-long/; /d/daxin-jiang/", "bibtex": "@inproceedings{huang-etal-2021-reasoning,\n title = \"Reasoning over Entity-Action-Location Graph for Procedural Text Understanding\",\n author = \"Huang, Hao and\n Geng, Xiubo and\n Pei, Jian and\n Long, Guodong and\n Jiang, Daxin\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.396/\",\n doi = \"10.18653/v1/2021.acl-long.396\",\n pages = \"5100--5109\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.396.pdf", "site": "https://aclanthology.org/2021.acl-long.396/", "pdf_size": 613541, "gs_citation": 12, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=1446596109841880686&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 3, "aff": "Australian AI Institute, School of CS, FEIT, University of Technology Sydney; STCA NLP Group, Microsoft; School of Computing Science, Simon Fraser University; Australian AI Institute, School of CS, FEIT, University of Technology Sydney; STCA NLP Group, Microsoft", "aff_domain": "student.uts.edu.au;uts.edu.au;microsoft.com;microsoft.com;cs.sfu.ca", "email": "student.uts.edu.au;uts.edu.au;microsoft.com;microsoft.com;cs.sfu.ca", "github": "", "project": "", "author_num": 5, "aff_unique_index": "0;1;2;0;1", "aff_unique_norm": "University of Technology Sydney;Microsoft;Simon Fraser University", "aff_unique_dep": "School of Computer Science;STCA NLP Group;School of Computing Science", "aff_unique_url": "https://www.uts.edu.au;https://www.microsoft.com;https://www.sfu.ca", "aff_unique_abbr": "UTS;Microsoft;SFU", "aff_campus_unique_index": "0;2;0", "aff_campus_unique": "Sydney;;Burnaby", "aff_country_unique_index": "0;1;2;0;1", "aff_country_unique": "Australia;United States;Canada" }, { "id": "2021.acl-long.358", "title": "Recursive Tree-Structured Self-Attention for Answer Sentence Selection", "track": "main", "status": "Long", "award": false, "abstract": "Syntactic structure is an important component of natural language text. Recent top-performing models in Answer Sentence Selection (AS2) use self-attention and transfer learning, but not syntactic structure. Tree structures have shown strong performance in tasks with sentence pair input like semantic relatedness. We investigate whether tree structures can boost performance in AS2. We introduce the Tree Aggregation Transformer: a novel recursive, tree-structured self-attention model for AS2. The recursive nature of our model is able to represent all levels of syntactic parse trees with only one additional self-attention layer. Without transfer learning, we establish a new state of the art on the popular TrecQA and WikiQA benchmark datasets. Additionally, we evaluate our method on four Community Question Answering datasets, and find that tree-structured representations have limitations with noisy user-generated text. We conduct probing experiments to evaluate how our models leverage tree structures across datasets. Our findings show that the ability of tree-structured models to successfully absorb syntactic information is strongly correlated with a higher performance in AS2.", "author": "Khalil Mrini; Emilia Farcas; Ndapa Nakashole", "authorids": "/k/khalil-mrini/; /e/emilia-farcas/; /n/ndapandula-nakashole/", "bibtex": "@inproceedings{mrini-etal-2021-recursive,\n title = \"Recursive Tree-Structured Self-Attention for Answer Sentence Selection\",\n author = \"Mrini, Khalil and\n Farcas, Emilia and\n Nakashole, Ndapa\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.358/\",\n doi = \"10.18653/v1/2021.acl-long.358\",\n pages = \"4651--4661\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.358.pdf", "site": "https://aclanthology.org/2021.acl-long.358/", "pdf_size": 453116, "gs_citation": 12, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=6930127726098207987&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 4, "aff": "University of California, San Diego, La Jolla, CA 92093; University of California, San Diego, La Jolla, CA 92093; University of California, San Diego, La Jolla, CA 92093", "aff_domain": "ucsd.edu;ucsd.edu;ucsd.edu", "email": "ucsd.edu;ucsd.edu;ucsd.edu", "github": "", "project": "", "author_num": 3, "aff_unique_index": "0;0;0", "aff_unique_norm": "University of California, San Diego", "aff_unique_dep": "", "aff_unique_url": "https://www.ucsd.edu", "aff_unique_abbr": "UCSD", "aff_campus_unique_index": "0;0;0", "aff_campus_unique": "La Jolla", "aff_country_unique_index": "0;0;0", "aff_country_unique": "United States" }, { "id": "2021.acl-long.151", "title": "RedditBias: A Real-World Resource for Bias Evaluation and Debiasing of Conversational Language Models", "track": "main", "status": "Long", "award": false, "abstract": "Text representation models are prone to exhibit a range of societal biases, reflecting the non-controlled and biased nature of the underlying pretraining data, which consequently leads to severe ethical issues and even bias amplification. Recent work has predominantly focused on measuring and mitigating bias in pretrained language models. Surprisingly, the landscape of bias measurements and mitigation resources and methods for conversational language models is still very scarce: it is limited to only a few types of bias, artificially constructed resources, and completely ignores the impact that debiasing methods may have on the final perfor mance in dialog tasks, e.g., conversational response generation. In this work, we present REDDITBIAS, the first conversational data set grounded in the actual human conversations from Reddit, allowing for bias measurement and mitigation across four important bias dimensions: gender,race,religion, and queerness. Further, we develop an evaluation framework which simultaneously 1)measures bias on the developed REDDITBIAS resource, and 2)evaluates model capability in dialog tasks after model debiasing. We use the evaluation framework to benchmark the widely used conversational DialoGPT model along with the adaptations of four debiasing methods. Our results indicate that DialoGPT is biased with respect to religious groups and that some debiasing techniques can remove this bias while preserving downstream task performance.", "author": "Soumya Barikeri; Anne Lauscher; Ivan Vuli\u0107; Goran Glava\u0161", "authorids": "/s/soumya-barikeri/; /a/anne-lauscher/; /i/ivan-vulic/; /g/goran-glavas/", "bibtex": "@inproceedings{barikeri-etal-2021-redditbias,\n title = \"{R}eddit{B}ias: A Real-World Resource for Bias Evaluation and Debiasing of Conversational Language Models\",\n author = \"Barikeri, Soumya and\n Lauscher, Anne and\n Vuli{\\'c}, Ivan and\n Glava{\\v{s}}, Goran\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.151/\",\n doi = \"10.18653/v1/2021.acl-long.151\",\n pages = \"1941--1955\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.151.pdf", "site": "https://aclanthology.org/2021.acl-long.151/", "pdf_size": 440752, "gs_citation": 196, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=280094979663568982&as_sdt=5,33&sciodt=0,33&hl=en", "gs_version_total": 5, "aff": "Data and Web Science Research Group, University of Mannheim; Data and Web Science Research Group, University of Mannheim; Language Technology Lab, University of Cambridge; Data and Web Science Research Group, University of Mannheim", "aff_domain": "gmail.com;informatik.uni-mannheim.de;cam.ac.uk;informatik.uni-mannheim.de", "email": "gmail.com;informatik.uni-mannheim.de;cam.ac.uk;informatik.uni-mannheim.de", "github": "", "project": "", "author_num": 4, "aff_unique_index": "0;0;1;0", "aff_unique_norm": "University of Mannheim;University of Cambridge", "aff_unique_dep": "Data and Web Science Research Group;Language Technology Lab", "aff_unique_url": "https://www.uni-mannheim.de;https://www.cam.ac.uk", "aff_unique_abbr": ";Cambridge", "aff_campus_unique_index": "1", "aff_campus_unique": ";Cambridge", "aff_country_unique_index": "0;0;1;0", "aff_country_unique": "Germany;United Kingdom" }, { "id": "2021.acl-long.20", "title": "Refining Sample Embeddings with Relation Prototypes to Enhance Continual Relation Extraction", "track": "main", "status": "Long", "award": false, "abstract": "Continual learning has gained increasing attention in recent years, thanks to its biological interpretation and efficiency in many real-world applications. As a typical task of continual learning, continual relation extraction (CRE) aims to extract relations between entities from texts, where the samples of different relations are delivered into the model continuously. Some previous works have proved that storing typical samples of old relations in memory can help the model keep a stable understanding of old relations and avoid forgetting them. However, most methods heavily depend on the memory size in that they simply replay these memorized samples in subsequent tasks. To fully utilize memorized samples, in this paper, we employ relation prototype to extract useful information of each relation. Specifically, the prototype embedding for a specific relation is computed based on memorized samples of this relation, which is collected by K-means algorithm. The prototypes of all observed relations at current learning stage are used to re-initialize a memory network to refine subsequent sample embeddings, which ensures the model\u2019s stable understanding on all observed relations when learning a new task. Compared with previous CRE models, our model utilizes the memory information sufficiently and efficiently, resulting in enhanced CRE performance. Our experiments show that the proposed model outperforms the state-of-the-art CRE models and has great advantage in avoiding catastrophic forgetting. The code and datasets are released on https://github.com/fd2014cl/RP-CRE.", "author": "Li Cui; Deqing Yang; Jiaxin Yu; Chengwei Hu; Jiayang Cheng; Jingjie Yi; Yanghua Xiao", "authorids": "/l/li-cui/; /d/deqing-yang/; /j/jiaxin-yu/; /c/chengwei-hu/; /j/jiayang-cheng/; /j/jingjie-yi/; /y/yanghua-xiao/", "bibtex": "@inproceedings{cui-etal-2021-refining,\n title = \"Refining Sample Embeddings with Relation Prototypes to Enhance Continual Relation Extraction\",\n author = \"Cui, Li and\n Yang, Deqing and\n Yu, Jiaxin and\n Hu, Chengwei and\n Cheng, Jiayang and\n Yi, Jingjie and\n Xiao, Yanghua\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.20/\",\n doi = \"10.18653/v1/2021.acl-long.20\",\n pages = \"232--243\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.20.pdf", "site": "https://aclanthology.org/2021.acl-long.20/", "pdf_size": 797113, "gs_citation": 66, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=15351774273200073723&as_sdt=5,33&sciodt=0,33&hl=en", "gs_version_total": 3, "aff": "School of Data Science, Fudan University; School of Data Science, Fudan University; School of Data Science, Fudan University; School of Data Science, Fudan University; School of Data Science, Fudan University; School of Data Science, Fudan University; Shanghai Key Laboratory of Data Science, School of Computer Science, Fudan University + Fudan-Aishu Cognitive Intelligence Joint Research Center, Shanghai, China", "aff_domain": "gmail.com;fudan.edu.cn;fudan.edu.cn;fudan.edu.cn;fudan.edu.cn;fudan.edu.cn;fudan.edu.cn", "email": "gmail.com;fudan.edu.cn;fudan.edu.cn;fudan.edu.cn;fudan.edu.cn;fudan.edu.cn;fudan.edu.cn", "github": "https://github.com/fd2014cl/RP-CRE", "project": "", "author_num": 7, "aff_unique_index": "0;0;0;0;0;0;0+0", "aff_unique_norm": "Fudan University", "aff_unique_dep": "School of Data Science", "aff_unique_url": "https://www.fudan.edu.cn", "aff_unique_abbr": "Fudan", "aff_campus_unique_index": "1+1", "aff_campus_unique": ";Shanghai", "aff_country_unique_index": "0;0;0;0;0;0;0+0", "aff_country_unique": "China" }, { "id": "2021.acl-long.114", "title": "Reflective Decoding: Beyond Unidirectional Generation with Off-the-Shelf Language Models", "track": "main", "status": "Long", "award": false, "abstract": "Publicly available, large pretrained Language Models (LMs) generate text with remarkable quality, but only sequentially from left to right. As a result, they are not immediately applicable to generation tasks that break the unidirectional assumption, such as paraphrasing or text-infilling, necessitating task-specific supervision. In this paper, we present Reflective Decoding, a novel unsupervised algorithm that allows for direct application of unidirectional LMs to non-sequential tasks. Our 2-step approach requires no supervision or even parallel corpora, only two off-the-shelf pretrained LMs in opposite directions: forward and backward. First, in the contextualization step, we use LMs to generate ensembles of past and future contexts which collectively capture the input (e.g. the source sentence for paraphrasing). Second, in the reflection step, we condition on these \u201ccontext ensembles\u201d, generating outputs that are compatible with them. Comprehensive empirical results demonstrate that Reflective Decoding outperforms strong unsupervised baselines on both paraphrasing and abductive text infilling, significantly narrowing the gap between unsupervised and supervised methods. Reflective Decoding surpasses multiple supervised baselines on various metrics including human evaluation.", "author": "Peter West; Ximing Lu; Ari Holtzman; Chandra Bhagavatula; Jena D. Hwang; Yejin Choi", "authorids": "/p/peter-west/; /x/ximing-lu/; /a/ari-holtzman/; /c/chandra-bhagavatula/; /j/jena-d-hwang/; /y/yejin-choi/", "bibtex": "@inproceedings{west-etal-2021-reflective,\n title = \"Reflective Decoding: Beyond Unidirectional Generation with Off-the-Shelf Language Models\",\n author = \"West, Peter and\n Lu, Ximing and\n Holtzman, Ari and\n Bhagavatula, Chandra and\n Hwang, Jena D. and\n Choi, Yejin\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.114/\",\n doi = \"10.18653/v1/2021.acl-long.114\",\n pages = \"1435--1450\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.114.pdf", "site": "https://aclanthology.org/2021.acl-long.114/", "pdf_size": 723839, "gs_citation": 19, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=3758689450410847222&as_sdt=5,44&sciodt=0,44&hl=en", "gs_version_total": 5, "aff": "Paul G. Allen School of Computer Science & Engineering, University of Washington+Allen Institute for Arti\ufb01cial Intelligence; Paul G. Allen School of Computer Science & Engineering, University of Washington+Allen Institute for Arti\ufb01cial Intelligence; Paul G. Allen School of Computer Science & Engineering, University of Washington; Allen Institute for Arti\ufb01cial Intelligence; Allen Institute for Arti\ufb01cial Intelligence; Paul G. Allen School of Computer Science & Engineering, University of Washington+Allen Institute for Arti\ufb01cial Intelligence", "aff_domain": "cs.washington.edu;allenai.org;cs.washington.edu;allenai.org;allenai.org;cs.washington.edu", "email": "cs.washington.edu;allenai.org;cs.washington.edu;allenai.org;allenai.org;cs.washington.edu", "github": "", "project": "https://homes.cs.washington.edu/~pawest/ReflectiveDecoding.html", "author_num": 6, "aff_unique_index": "0+1;0+1;0;1;1;0+1", "aff_unique_norm": "University of Washington;Allen Institute for Artificial Intelligence", "aff_unique_dep": "Paul G. Allen School of Computer Science & Engineering;Artificial Intelligence", "aff_unique_url": "https://www.washington.edu;https://allenai.org", "aff_unique_abbr": "UW;AI2", "aff_campus_unique_index": "0;0;0;0", "aff_campus_unique": "Seattle;", "aff_country_unique_index": "0+0;0+0;0;0;0;0+0", "aff_country_unique": "United States" }, { "id": "2021.acl-long.515", "title": "Regression Bugs Are In Your Model! Measuring, Reducing and Analyzing Regressions In NLP Model Updates", "track": "main", "status": "Long", "award": false, "abstract": "Behavior of deep neural networks can be inconsistent between different versions. Regressions during model update are a common cause of concern that often over-weigh the benefits in accuracy or efficiency gain. This work focuses on quantifying, reducing and analyzing regression errors in the NLP model updates. Using negative flip rate as regression measure, we show that regression has a prevalent presence across tasks in the GLUE benchmark. We formulate the regression-free model updates into a constrained optimization problem, and further reduce it into a relaxed form which can be approximately optimized through knowledge distillation training method. We empirically analyze how model ensemble reduces regression. Finally, we conduct CheckList behavioral testing to understand the distribution of regressions across linguistic phenomena, and the efficacy of ensemble and distillation methods.", "author": "Yuqing Xie; Yi-An Lai; Yuanjun Xiong; Yi Zhang; Stefano Soatto", "authorids": "/y/yuqing-xie/; /y/yi-an-lai/; /y/yuanjun-xiong/; /y/yi-zhang/; /s/stefano-soatto/", "bibtex": "@inproceedings{xie-etal-2021-regression,\n title = \"Regression Bugs Are In Your Model! Measuring, Reducing and Analyzing Regressions In {NLP} Model Updates\",\n author = \"Xie, Yuqing and\n Lai, Yi-An and\n Xiong, Yuanjun and\n Zhang, Yi and\n Soatto, Stefano\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.515/\",\n doi = \"10.18653/v1/2021.acl-long.515\",\n pages = \"6589--6602\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.515.pdf", "site": "https://aclanthology.org/2021.acl-long.515/", "pdf_size": 473901, "gs_citation": 15, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=18257125510758917018&as_sdt=80005&sciodt=0,11&hl=en", "gs_version_total": 5, "aff": "University of Waterloo; Amazon AWS AI; Amazon AWS AI; Amazon AWS AI; Amazon AWS AI", "aff_domain": "uwaterloo.ca;amazon.com;amazon.com;amazon.com;amazon.com", "email": "uwaterloo.ca;amazon.com;amazon.com;amazon.com;amazon.com", "github": "", "project": "", "author_num": 5, "aff_unique_index": "0;1;1;1;1", "aff_unique_norm": "University of Waterloo;Amazon", "aff_unique_dep": ";Amazon Web Services AI", "aff_unique_url": "https://uwaterloo.ca;https://aws.amazon.com", "aff_unique_abbr": "UW;AWS", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0;1;1;1;1", "aff_country_unique": "Canada;United States" }, { "id": "2021.acl-short.33", "title": "Reinforcement Learning for Abstractive Question Summarization with Question-aware Semantic Rewards", "track": "main", "status": "Short", "award": false, "abstract": "The growth of online consumer health questions has led to the necessity for reliable and accurate question answering systems. A recent study showed that manual summarization of consumer health questions brings significant improvement in retrieving relevant answers. However, the automatic summarization of long questions is a challenging task due to the lack of training data and the complexity of the related subtasks, such as the question focus and type recognition. In this paper, we introduce a reinforcement learning-based framework for abstractive question summarization. We propose two novel rewards obtained from the downstream tasks of (i) question-type identification and (ii) question-focus recognition to regularize the question generation model. These rewards ensure the generation of semantically valid questions and encourage the inclusion of key medical entities/foci in the question summary. We evaluated our proposed method on two benchmark datasets and achieved higher performance over state-of-the-art models. The manual evaluation of the summaries reveals that the generated questions are more diverse and have fewer factual inconsistencies than the baseline summaries. The source code is available here: https://github.com/shwetanlp/CHQ-Summ.", "author": "Shweta Yadav; Deepak Gupta; Asma Ben Abacha; Dina Demner-Fushman", "authorids": "/s/shweta-yadav/; /d/deepak-gupta/; /a/asma-ben-abacha/; /d/dina-demner-fushman/", "bibtex": "@inproceedings{yadav-etal-2021-reinforcement,\n title = \"Reinforcement Learning for Abstractive Question Summarization with Question-aware Semantic Rewards\",\n author = \"Yadav, Shweta and\n Gupta, Deepak and\n Ben Abacha, Asma and\n Demner-Fushman, Dina\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-short.33/\",\n doi = \"10.18653/v1/2021.acl-short.33\",\n pages = \"249--255\"\n}", "pdf": "https://aclanthology.org/2021.acl-short.33.pdf", "site": "https://aclanthology.org/2021.acl-short.33/", "pdf_size": 311539, "gs_citation": 36, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=136949971164202334&as_sdt=5,44&sciodt=0,44&hl=en", "gs_version_total": 5, "aff": "LHNCBC, U.S. National Library of Medicine, MD, USA; LHNCBC, U.S. National Library of Medicine, MD, USA; LHNCBC, U.S. National Library of Medicine, MD, USA; LHNCBC, U.S. National Library of Medicine, MD, USA", "aff_domain": "nih.gov;nih.gov;nih.gov;mail.nih.gov", "email": "nih.gov;nih.gov;nih.gov;mail.nih.gov", "github": "https://github.com/shwetanlp/CHQ-Summ", "project": "", "author_num": 4, "aff_unique_index": "0;0;0;0", "aff_unique_norm": "U.S. National Library of Medicine", "aff_unique_dep": "LHNCBC", "aff_unique_url": "https://www.nlm.nih.gov", "aff_unique_abbr": "NLM", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0;0;0;0", "aff_country_unique": "United States" }, { "id": "2021.acl-long.266", "title": "Rejuvenating Low-Frequency Words: Making the Most of Parallel Data in Non-Autoregressive Translation", "track": "main", "status": "Long", "award": false, "abstract": "Knowledge distillation (KD) is commonly used to construct synthetic data for training non-autoregressive translation (NAT) models. However, there exists a discrepancy on low-frequency words between the distilled and the original data, leading to more errors on predicting low-frequency words. To alleviate the problem, we directly expose the raw data into NAT by leveraging pretraining. By analyzing directed alignments, we found that KD makes low-frequency source words aligned with targets more deterministically but fails to align sufficient low-frequency words from target to source. Accordingly, we propose reverse KD to rejuvenate more alignments for low-frequency target words. To make the most of authentic and synthetic data, we combine these complementary approaches as a new training strategy for further boosting NAT performance. We conduct experiments on five translation benchmarks over two advanced architectures. Results demonstrate that the proposed approach can significantly and universally improve translation quality by reducing translation errors on low-frequency words. Encouragingly, our approach achieves 28.2 and 33.9 BLEU points on the WMT14 English-German and WMT16 Romanian-English datasets, respectively. Our code, data, and trained models are available at https://github.com/longyuewangdcu/RLFW-NAT.", "author": "Liang Ding; Longyue Wang; Xuebo Liu; Derek F. Wong; Dacheng Tao; Zhaopeng Tu", "authorids": "/l/liang-ding/; /l/longyue-wang/; /x/xuebo-liu/; /d/derek-f-wong/; /d/dacheng-tao/; /z/zhaopeng-tu/", "bibtex": "@inproceedings{ding-etal-2021-rejuvenating,\n title = \"Rejuvenating Low-Frequency Words: Making the Most of Parallel Data in Non-Autoregressive Translation\",\n author = \"Ding, Liang and\n Wang, Longyue and\n Liu, Xuebo and\n Wong, Derek F. and\n Tao, Dacheng and\n Tu, Zhaopeng\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.266/\",\n doi = \"10.18653/v1/2021.acl-long.266\",\n pages = \"3431--3441\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.266.pdf", "site": "https://aclanthology.org/2021.acl-long.266/", "pdf_size": 481381, "gs_citation": 43, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=13285892753309163819&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 6, "aff": "The University of Sydney; Tencent AI Lab; University of Macau; University of Macau; JD Explore Academy, JD.com; Tencent AI Lab", "aff_domain": "sydnye.edu.au;tencent.com;gmail.com;um.edu.com;gmail.com;tencent.com", "email": "sydnye.edu.au;tencent.com;gmail.com;um.edu.com;gmail.com;tencent.com", "github": "https://github.com/longyuewangdcu/RLFW-NAT", "project": "", "author_num": 6, "aff_unique_index": "0;1;2;2;3;1", "aff_unique_norm": "University of Sydney;Tencent;University of Macau;JD.com", "aff_unique_dep": ";Tencent AI Lab;;JD Explore Academy", "aff_unique_url": "https://www.sydney.edu.au;https://ai.tencent.com;https://www.um.edu.mo;https://www.jd.com", "aff_unique_abbr": "USYD;Tencent AI Lab;UM;JD", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0;1;2;2;1;1", "aff_country_unique": "Australia;China;Macau" }, { "id": "2021.acl-short.19", "title": "Relative Importance in Sentence Processing", "track": "main", "status": "Short", "award": false, "abstract": "Determining the relative importance of the elements in a sentence is a key factor for effortless natural language understanding. For human language processing, we can approximate patterns of relative importance by measuring reading fixations using eye-tracking technology. In neural language models, gradient-based saliency methods indicate the relative importance of a token for the target objective. In this work, we compare patterns of relative importance in English language processing by humans and models and analyze the underlying linguistic patterns. We find that human processing patterns in English correlate strongly with saliency-based importance in language models and not with attention-based importance. Our results indicate that saliency could be a cognitively more plausible metric for interpreting neural language models. The code is available on github: https://github.com/beinborn/relative_importance.", "author": "Nora Hollenstein; Lisa Beinborn", "authorids": "/n/nora-hollenstein/; /l/lisa-beinborn/", "bibtex": "@inproceedings{hollenstein-beinborn-2021-relative,\n title = \"Relative Importance in Sentence Processing\",\n author = \"Hollenstein, Nora and\n Beinborn, Lisa\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-short.19/\",\n doi = \"10.18653/v1/2021.acl-short.19\",\n pages = \"141--150\"\n}", "pdf": "https://aclanthology.org/2021.acl-short.19.pdf", "site": "https://aclanthology.org/2021.acl-short.19/", "pdf_size": 738183, "gs_citation": 37, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=9205584926143865659&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 6, "aff": "Center for Language Technology, University of Copenhagen; Computational Linguistics & Text Mining Lab, Vrije Universiteit Amsterdam", "aff_domain": "hum.ku.dk;vu.nl", "email": "hum.ku.dk;vu.nl", "github": "https://github.com/beinborn/relative_importance", "project": "", "author_num": 2, "aff_unique_index": "0;1", "aff_unique_norm": "University of Copenhagen;Vrije Universiteit Amsterdam", "aff_unique_dep": "Center for Language Technology;Computational Linguistics & Text Mining Lab", "aff_unique_url": "https://www.ku.dk;https://www.vu.nl", "aff_unique_abbr": "UCPH;VU Amsterdam", "aff_campus_unique_index": "1", "aff_campus_unique": ";Amsterdam", "aff_country_unique_index": "0;1", "aff_country_unique": "Denmark;Netherlands" }, { "id": "2021.acl-long.321", "title": "Reliability Testing for Natural Language Processing Systems", "track": "main", "status": "Long", "award": false, "abstract": "Questions of fairness, robustness, and transparency are paramount to address before deploying NLP systems. Central to these concerns is the question of reliability: Can NLP systems reliably treat different demographics fairly and function correctly in diverse and noisy environments? To address this, we argue for the need for reliability testing and contextualize it among existing work on improving accountability. We show how adversarial attacks can be reframed for this goal, via a framework for developing reliability tests. We argue that reliability testing \u2014 with an emphasis on interdisciplinary collaboration \u2014 will enable rigorous and targeted testing, and aid in the enactment and enforcement of industry standards.", "author": "Samson Tan; Shafiq Joty; Kathy Baxter; Araz Taeihagh; Gregory A. Bennett; Min-Yen Kan", "authorids": "/s/samson-tan/; /s/shafiq-joty/; /k/kathy-baxter/; /a/araz-taeihagh/; /g/gregory-a-bennett/; /m/min-yen-kan/", "bibtex": "@inproceedings{tan-etal-2021-reliability,\n title = \"Reliability Testing for Natural Language Processing Systems\",\n author = \"Tan, Samson and\n Joty, Shafiq and\n Baxter, Kathy and\n Taeihagh, Araz and\n Bennett, Gregory A. and\n Kan, Min-Yen\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.321/\",\n doi = \"10.18653/v1/2021.acl-long.321\",\n pages = \"4153--4169\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.321.pdf", "site": "https://aclanthology.org/2021.acl-long.321/", "pdf_size": 628859, "gs_citation": 42, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=13044054427924260681&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 13, "aff": "Salesforce Research; Salesforce Research + Nanyang Technological University; Salesforce Research; Lee Kuan Yew School of Public Policy, National University of Singapore + Centre for Trusted Internet and Community, National University of Singapore; Salesforce Research; School of Computing, National University of Singapore", "aff_domain": "salesforce.com; ; ; ; ; ", "email": "salesforce.com; ; ; ; ; ", "github": "", "project": "", "author_num": 6, "aff_unique_index": "0;0+1;0;2+2;0;2", "aff_unique_norm": "Salesforce;Nanyang Technological University;National University of Singapore", "aff_unique_dep": "Salesforce Research;;Lee Kuan Yew School of Public Policy", "aff_unique_url": "https://research.salesforce.com;https://www.ntu.edu.sg;https://www.nus.edu.sg", "aff_unique_abbr": "Salesforce;NTU;NUS", "aff_campus_unique_index": ";1", "aff_campus_unique": ";Singapore", "aff_country_unique_index": "0;0+1;0;1+1;0;1", "aff_country_unique": "United States;Singapore" }, { "id": "2021.acl-long.471", "title": "RepSum: Unsupervised Dialogue Summarization based on Replacement Strategy", "track": "main", "status": "Long", "award": false, "abstract": "In the field of dialogue summarization, due to the lack of training data, it is often difficult for supervised summary generation methods to learn vital information from dialogue context with limited data. Several attempts on unsupervised summarization for text by leveraging semantic information solely or auto-encoder strategy (i.e., sentence compression), it however cannot be adapted to the dialogue scene due to the limited words in utterances and huge gap between the dialogue and its summary. In this study, we propose a novel unsupervised strategy to address this challenge, which roots from the hypothetical foundation that a superior summary approximates a replacement of the original dialogue, and they are roughly equivalent for auxiliary (self-supervised) tasks, e.g., dialogue generation. The proposed strategy RepSum is applied to generate both extractive and abstractive summary with the guidance of the followed n\u02c6th utterance generation and classification tasks. Extensive experiments on various datasets demonstrate the superiority of the proposed model compared with the state-of-the-art methods.", "author": "Xiyan Fu; Yating Zhang; Tianyi Wang; Xiaozhong Liu; Changlong Sun; Zhenglu Yang", "authorids": "/x/xiyan-fu/; /y/yating-zhang/; /t/tianyi-wang/; /x/xiaozhong-liu/; /c/changlong-sun/; /z/zhenglu-yang/", "bibtex": "@inproceedings{fu-etal-2021-repsum,\n title = \"{R}ep{S}um: Unsupervised Dialogue Summarization based on Replacement Strategy\",\n author = \"Fu, Xiyan and\n Zhang, Yating and\n Wang, Tianyi and\n Liu, Xiaozhong and\n Sun, Changlong and\n Yang, Zhenglu\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.471/\",\n doi = \"10.18653/v1/2021.acl-long.471\",\n pages = \"6042--6051\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.471.pdf", "site": "https://aclanthology.org/2021.acl-long.471/", "pdf_size": 758706, "gs_citation": 15, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=9016508196731731157&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 2, "aff": "Nankai University, Tianjin, China; Alibaba Group, Hangzhou, China; Alibaba Group, Hangzhou, China; Indiana University, Bloomington, USA; Alibaba Group, Hangzhou, China; Nankai University, Tianjin, China", "aff_domain": "mail.nankai.edu.cn;alibaba-inc.com;alibaba-inc.com;indiana.edu;taobao.com;nankai.edu.cn", "email": "mail.nankai.edu.cn;alibaba-inc.com;alibaba-inc.com;indiana.edu;taobao.com;nankai.edu.cn", "github": "", "project": "", "author_num": 6, "aff_unique_index": "0;1;1;2;1;0", "aff_unique_norm": "Nankai University;Alibaba Group;Indiana University", "aff_unique_dep": ";;", "aff_unique_url": "http://www.nankai.edu.cn;https://www.alibaba.com;https://www.indiana.edu", "aff_unique_abbr": "NKU;Alibaba;IU", "aff_campus_unique_index": "0;1;1;2;1;0", "aff_campus_unique": "Tianjin;Hangzhou;Bloomington", "aff_country_unique_index": "0;0;0;1;0;0", "aff_country_unique": "China;United States" }, { "id": "2021.acl-short.138", "title": "Replicating and Extending \u201cBecause Their Treebanks Leak\u201d: Graph Isomorphism, Covariants, and Parser Performance", "track": "main", "status": "Short", "award": false, "abstract": "S\u00f8gaard (2020) obtained results suggesting the fraction of trees occurring in the test data isomorphic to trees in the training set accounts for a non-trivial variation in parser performance. Similar to other statistical analyses in NLP, the results were based on evaluating linear regressions. However, the study had methodological issues and was undertaken using a small sample size leading to unreliable results. We present a replication study in which we also bin sentences by length and find that only a small subset of sentences vary in performance with respect to graph isomorphism. Further, the correlation observed between parser performance and graph isomorphism in the wild disappears when controlling for covariants. However, in a controlled experiment, where covariants are kept fixed, we do observe a correlation. We suggest that conclusions drawn from statistical analyses like this need to be tempered and that controlled experiments can complement them by more readily teasing factors apart.", "author": "Mark Anderson; Anders S\u00f8gaard; Carlos G\u00f3mez-Rodr\u00edguez", "authorids": "/m/mark-anderson/; /a/anders-sogaard/; /c/carlos-gomez-rodriguez/", "bibtex": "@inproceedings{anderson-etal-2021-replicating,\n title = \"{R}eplicating and Extending {\\textquotedblleft}{B}ecause Their Treebanks Leak{\\textquotedblright}: {G}raph Isomorphism, Covariants, and Parser Performance\",\n author = \"Anderson, Mark and\n S{\\o}gaard, Anders and\n G{\\'o}mez-Rodr{\\'i}guez, Carlos\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-short.138/\",\n doi = \"10.18653/v1/2021.acl-short.138\",\n pages = \"1090--1098\"\n}", "pdf": "https://aclanthology.org/2021.acl-short.138.pdf", "site": "https://aclanthology.org/2021.acl-short.138/", "pdf_size": 937788, "gs_citation": 6, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=15609246081057616990&as_sdt=5,44&sciodt=0,44&hl=en", "gs_version_total": 6, "aff": "Universidade da Coru\u00f1a, CITIC, Department of CS & IT; Dpt. of Computer Science, University of Copenhagen; Universidade da Coru\u00f1a, CITIC, Department of CS & IT", "aff_domain": "udc;di.ku.dk;udc.es", "email": "udc;di.ku.dk;udc.es", "github": "", "project": "", "author_num": 3, "aff_unique_index": "0;1;0", "aff_unique_norm": "Universidade da Coru\u00f1a;University of Copenhagen", "aff_unique_dep": "Department of CS & IT;Department of Computer Science", "aff_unique_url": "https://www.udc.es;https://www.ku.dk", "aff_unique_abbr": "UDC;UCPH", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0;1;0", "aff_country_unique": "Spain;Denmark" }, { "id": "2021.acl-long.331", "title": "Reservoir Transformers", "track": "main", "status": "Long", "award": false, "abstract": "We demonstrate that transformers obtain impressive performance even when some of the layers are randomly initialized and never updated. Inspired by old and well-established ideas in machine learning, we explore a variety of non-linear \u201creservoir\u201d layers interspersed with regular transformer layers, and show improvements in wall-clock compute time until convergence, as well as overall performance, on various machine translation and (masked) language modelling tasks.", "author": "Sheng Shen; Alexei Baevski; Ari Morcos; Kurt Keutzer; Michael Auli; Douwe Kiela", "authorids": "/s/sheng-shen/; /a/alexei-baevski/; /a/ari-morcos/; /k/kurt-keutzer/; /m/michael-auli/; /d/douwe-kiela/", "bibtex": "@inproceedings{shen-etal-2021-reservoir,\n title = \"Reservoir Transformers\",\n author = \"Shen, Sheng and\n Baevski, Alexei and\n Morcos, Ari and\n Keutzer, Kurt and\n Auli, Michael and\n Kiela, Douwe\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.331/\",\n doi = \"10.18653/v1/2021.acl-long.331\",\n pages = \"4294--4309\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.331.pdf", "site": "https://aclanthology.org/2021.acl-long.331/", "pdf_size": 981211, "gs_citation": 30, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=7233417758951236944&as_sdt=5,33&sciodt=0,33&hl=en", "gs_version_total": 9, "aff": "UC Berkeley; Facebook AI Research; Facebook AI Research; UC Berkeley; Facebook AI Research; Facebook AI Research", "aff_domain": "berkeley.edu; ; ; ;fb.com; ", "email": "berkeley.edu; ; ; ;fb.com; ", "github": "", "project": "", "author_num": 6, "aff_unique_index": "0;1;1;0;1;1", "aff_unique_norm": "University of California, Berkeley;Facebook", "aff_unique_dep": ";Facebook AI Research", "aff_unique_url": "https://www.berkeley.edu;https://research.facebook.com", "aff_unique_abbr": "UC Berkeley;FAIR", "aff_campus_unique_index": "0;0", "aff_campus_unique": "Berkeley;", "aff_country_unique_index": "0;0;0;0;0;0", "aff_country_unique": "United States" }, { "id": "2021.acl-long.431", "title": "Rethinking Stealthiness of Backdoor Attack against NLP Models", "track": "main", "status": "Long", "award": false, "abstract": "Recent researches have shown that large natural language processing (NLP) models are vulnerable to a kind of security threat called the Backdoor Attack. Backdoor attacked models can achieve good performance on clean test sets but perform badly on those input sentences injected with designed trigger words. In this work, we point out a potential problem of current backdoor attacking research: its evaluation ignores the stealthiness of backdoor attacks, and most of existing backdoor attacking methods are not stealthy either to system deployers or to system users. To address this issue, we first propose two additional stealthiness-based metrics to make the backdoor attacking evaluation more credible. We further propose a novel word-based backdoor attacking method based on negative data augmentation and modifying word embeddings, making an important step towards achieving stealthy backdoor attacking. Experiments on sentiment analysis and toxic detection tasks show that our method is much stealthier while maintaining pretty good attacking performance. Our code is available at https://github.com/lancopku/SOS.", "author": "Wenkai Yang; Yankai Lin; Peng Li; Jie Zhou; Xu Sun", "authorids": "/w/wenkai-yang/; /y/yankai-lin/; /p/peng-li/; /j/jie-zhou/; /x/xu-sun/", "bibtex": "@inproceedings{yang-etal-2021-rethinking,\n title = \"Rethinking Stealthiness of Backdoor Attack against {NLP} Models\",\n author = \"Yang, Wenkai and\n Lin, Yankai and\n Li, Peng and\n Zhou, Jie and\n Sun, Xu\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.431/\",\n doi = \"10.18653/v1/2021.acl-long.431\",\n pages = \"5543--5557\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.431.pdf", "site": "https://aclanthology.org/2021.acl-long.431/", "pdf_size": 1495136, "gs_citation": 125, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=9417665123543627196&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 2, "aff": "Center for Data Science, Peking University; Pattern Recognition Center, WeChat AI, Tencent Inc., China; Pattern Recognition Center, WeChat AI, Tencent Inc., China; Pattern Recognition Center, WeChat AI, Tencent Inc., China; Center for Data Science, Peking University + MOE Key Laboratory of Computational Linguistics, School of EECS, Peking University", "aff_domain": "stu.pku.edu.cn;tencent.com;tencent.com;tencent.com;pku.edu.cn", "email": "stu.pku.edu.cn;tencent.com;tencent.com;tencent.com;pku.edu.cn", "github": "https://github.com/lancopku/SOS", "project": "", "author_num": 5, "aff_unique_index": "0;1;1;1;0+0", "aff_unique_norm": "Peking University;Tencent Inc.", "aff_unique_dep": "Center for Data Science;Pattern Recognition Center, WeChat AI", "aff_unique_url": "http://www.pku.edu.cn;https://www.tencent.com", "aff_unique_abbr": "PKU;Tencent", "aff_campus_unique_index": "0;0", "aff_campus_unique": "Beijing;", "aff_country_unique_index": "0;0;0;0;0+0", "aff_country_unique": "China" }, { "id": "2021.acl-long.277", "title": "Revisiting the Negative Data of Distantly Supervised Relation Extraction", "track": "main", "status": "Long", "award": false, "abstract": "Distantly supervision automatically generates plenty of training samples for relation extraction. However, it also incurs two major problems: noisy labels and imbalanced training data. Previous works focus more on reducing wrongly labeled relations (false positives) while few explore the missing relations that are caused by incompleteness of knowledge base (false negatives). Furthermore, the quantity of negative labels overwhelmingly surpasses the positive ones in previous problem formulations. In this paper, we first provide a thorough analysis of the above challenges caused by negative data. Next, we formulate the problem of relation extraction into as a positive unlabeled learning task to alleviate false negative problem. Thirdly, we propose a pipeline approach, dubbed ReRe, that first performs sentence classification with relational labels and then extracts the subjects/objects. Experimental results show that the proposed method consistently outperforms existing approaches and remains excellent performance even learned with a large quantity of false positive samples. Source code is available online at https://github.com/redreamality/RERE-relation-extraction.", "author": "Chenhao Xie; Jiaqing Liang; Jingping Liu; Chengsong Huang; Wenhao Huang; Yanghua Xiao", "authorids": "/c/chenhao-xie/; /j/jiaqing-liang/; /j/jingping-liu/; /c/chengsong-huang/; /w/wenhao-huang/; /y/yanghua-xiao/", "bibtex": "@inproceedings{xie-etal-2021-revisiting,\n title = \"Revisiting the Negative Data of Distantly Supervised Relation Extraction\",\n author = \"Xie, Chenhao and\n Liang, Jiaqing and\n Liu, Jingping and\n Huang, Chengsong and\n Huang, Wenhao and\n Xiao, Yanghua\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.277/\",\n doi = \"10.18653/v1/2021.acl-long.277\",\n pages = \"3572--3581\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.277.pdf", "site": "https://aclanthology.org/2021.acl-long.277/", "pdf_size": 649092, "gs_citation": 42, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=11514381557920822699&as_sdt=80000005&sciodt=0,23&hl=en", "gs_version_total": 5, "aff": "Shanghai Key Laboratory of Data Science, School of Computer Science, Fudan University+Shuyan Technology Inc., Shanghai, China; Shanghai Key Laboratory of Data Science, School of Computer Science, Fudan University+Shuyan Technology Inc., Shanghai, China; Shanghai Key Laboratory of Data Science, School of Computer Science, Fudan University; Shanghai Key Laboratory of Data Science, School of Computer Science, Fudan University; Shanghai Key Laboratory of Data Science, School of Computer Science, Fudan University; Shanghai Key Laboratory of Data Science, School of Computer Science, Fudan University+Fudan-Aishu Cognitive Intelligence Joint Research Center, Shanghai, China", "aff_domain": "gmail.com;gmail.com;fudan.edu.cn;fudan.edu.cn;fudan.edu.cn;fudan.edu.cn", "email": "gmail.com;gmail.com;fudan.edu.cn;fudan.edu.cn;fudan.edu.cn;fudan.edu.cn", "github": "https://github.com/redreamality/RERE-relation-extraction", "project": "", "author_num": 6, "aff_unique_index": "0+1;0+1;0;0;0;0+0", "aff_unique_norm": "Fudan University;Shuyan Technology Inc.", "aff_unique_dep": "School of Computer Science;", "aff_unique_url": "https://www.fudan.edu.cn;", "aff_unique_abbr": "Fudan;", "aff_campus_unique_index": "0;0;0;0;0;0+0", "aff_campus_unique": "Shanghai;", "aff_country_unique_index": "0+0;0+0;0;0;0;0+0", "aff_country_unique": "China" }, { "id": "2021.acl-long.443", "title": "Rewriter-Evaluator Architecture for Neural Machine Translation", "track": "main", "status": "Long", "award": false, "abstract": "A few approaches have been developed to improve neural machine translation (NMT) models with multiple passes of decoding. However, their performance gains are limited because of lacking proper policies to terminate the multi-pass process. To address this issue, we introduce a novel architecture of Rewriter-Evaluator. Translating a source sentence involves multiple rewriting passes. In every pass, a rewriter generates a new translation to improve the past translation. Termination of this multi-pass process is determined by a score of translation quality estimated by an evaluator. We also propose prioritized gradient descent (PGD) to jointly and efficiently train the rewriter and the evaluator. Extensive experiments on three machine translation tasks show that our architecture notably improves the performances of NMT models and significantly outperforms prior methods. An oracle experiment reveals that it can largely reduce performance gaps to the oracle policy. Experiments confirm that the evaluator trained with PGD is more accurate than prior methods in determining proper numbers of rewriting.", "author": "Yangming Li; Kaisheng Yao", "authorids": "/y/yangming-li/; /k/kaisheng-yao/", "bibtex": "@inproceedings{li-yao-2021-rewriter,\n title = \"Rewriter-Evaluator Architecture for Neural Machine Translation\",\n author = \"Li, Yangming and\n Yao, Kaisheng\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.443/\",\n doi = \"10.18653/v1/2021.acl-long.443\",\n pages = \"5701--5710\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.443.pdf", "site": "https://aclanthology.org/2021.acl-long.443/", "pdf_size": 441203, "gs_citation": 2, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=7777294992884392396&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 4, "aff": "Tencent AI Lab; Ant Group", "aff_domain": "tencent.com;antgroup.com", "email": "tencent.com;antgroup.com", "github": "", "project": "", "author_num": 2, "aff_unique_index": "0;1", "aff_unique_norm": "Tencent;Ant Group", "aff_unique_dep": "Tencent AI Lab;", "aff_unique_url": "https://ai.tencent.com;https://www.antgroup.com", "aff_unique_abbr": "Tencent AI Lab;Ant Group", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0;0", "aff_country_unique": "China" }, { "id": "2021.acl-long.380", "title": "Risk Minimization for Zero-shot Sequence Labeling", "track": "main", "status": "Long", "award": false, "abstract": "Zero-shot sequence labeling aims to build a sequence labeler without human-annotated datasets. One straightforward approach is utilizing existing systems (source models) to generate pseudo-labeled datasets and train a target sequence labeler accordingly. However, due to the gap between the source and the target languages/domains, this approach may fail to recover the true labels. In this paper, we propose a novel unified framework for zero-shot sequence labeling with minimum risk training and design a new decomposable risk function that models the relations between the predicted labels from the source models and the true labels. By making the risk function trainable, we draw a connection between minimum risk training and latent variable model learning. We propose a unified learning algorithm based on the expectation maximization (EM) algorithm. We extensively evaluate our proposed approaches on cross-lingual/domain sequence labeling tasks over twenty-one datasets. The results show that our approaches outperform state-of-the-art baseline systems.", "author": "Zechuan Hu; Yong Jiang; Nguyen Bach; Tao Wang; Zhongqiang Huang; Fei Huang; Kewei Tu", "authorids": "/z/zechuan-hu/; /y/yong-jiang/; /n/nguyen-bach/; /t/tao-wang/; /z/zhongqiang-huang/; /f/fei-huang/; /k/kewei-tu/", "bibtex": "@inproceedings{hu-etal-2021-risk,\n title = \"Risk Minimization for Zero-shot Sequence Labeling\",\n author = \"Hu, Zechuan and\n Jiang, Yong and\n Bach, Nguyen and\n Wang, Tao and\n Huang, Zhongqiang and\n Huang, Fei and\n Tu, Kewei\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.380/\",\n doi = \"10.18653/v1/2021.acl-long.380\",\n pages = \"4909--4920\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.380.pdf", "site": "https://aclanthology.org/2021.acl-long.380/", "pdf_size": 1481834, "gs_citation": 3, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=12955072730695452955&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 6, "aff": "School of Information Science and Technology, ShanghaiTech University + Shanghai Engineering Research Center of Intelligent Vision and Imaging + Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences + University of Chinese Academy of Sciences; DAMO Academy, Alibaba Group; DAMO Academy, Alibaba Group; DAMO Academy, Alibaba Group; DAMO Academy, Alibaba Group; DAMO Academy, Alibaba Group; School of Information Science and Technology, ShanghaiTech University + Shanghai Engineering Research Center of Intelligent Vision and Imaging + Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences + University of Chinese Academy of Sciences", "aff_domain": "shanghaitech.edu.cn;alibaba-inc.com;alibaba-inc.com;alibaba-inc.com;alibaba-inc.com;alibaba-inc.com;shanghaitech.edu.cn", "email": "shanghaitech.edu.cn;alibaba-inc.com;alibaba-inc.com;alibaba-inc.com;alibaba-inc.com;alibaba-inc.com;shanghaitech.edu.cn", "github": "", "project": "", "author_num": 7, "aff_unique_index": "0+1+2+3;4;4;4;4;4;0+1+2+3", "aff_unique_norm": "ShanghaiTech University;Shanghai Engineering Research Center of Intelligent Vision and Imaging;Shanghai Institute of Microsystem and Information Technology;University of Chinese Academy of Sciences;Alibaba Group", "aff_unique_dep": "School of Information Science and Technology;;;;DAMO Academy", "aff_unique_url": "https://www.shanghaitech.edu.cn;;http://www.sim.cas.cn;http://www.ucas.ac.cn;https://www.alibaba-group.com", "aff_unique_abbr": "ShanghaiTech;;SIM;UCAS;Alibaba", "aff_campus_unique_index": "0+0;0+0", "aff_campus_unique": "Shanghai;", "aff_country_unique_index": "0+0+0+0;0;0;0;0;0;0+0+0+0", "aff_country_unique": "China" }, { "id": "2021.acl-long.82", "title": "Robust Knowledge Graph Completion with Stacked Convolutions and a Student Re-Ranking Network", "track": "main", "status": "Long", "award": false, "abstract": "Knowledge Graph (KG) completion research usually focuses on densely connected benchmark datasets that are not representative of real KGs. We curate two KG datasets that include biomedical and encyclopedic knowledge and use an existing commonsense KG dataset to explore KG completion in the more realistic setting where dense connectivity is not guaranteed. We develop a deep convolutional network that utilizes textual entity representations and demonstrate that our model outperforms recent KG completion methods in this challenging setting. We find that our model\u2019s performance improvements stem primarily from its robustness to sparsity. We then distill the knowledge from the convolutional network into a student network that re-ranks promising candidate entities. This re-ranking stage leads to further improvements in performance and demonstrates the effectiveness of entity re-ranking for KG completion.", "author": "Justin Lovelace; Denis Newman-Griffis; Shikhar Vashishth; Jill Fain Lehman; Carolyn Ros\u00e9", "authorids": "/j/justin-lovelace/; /d/denis-newman-griffis/; /s/shikhar-vashishth/; /j/jill-fain-lehman/; /c/carolyn-rose/", "bibtex": "@inproceedings{lovelace-etal-2021-robust,\n title = \"Robust Knowledge Graph Completion with Stacked Convolutions and a Student Re-Ranking Network\",\n author = \"Lovelace, Justin and\n Newman-Griffis, Denis and\n Vashishth, Shikhar and\n Lehman, Jill Fain and\n Ros{\\'e}, Carolyn\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.82/\",\n doi = \"10.18653/v1/2021.acl-long.82\",\n pages = \"1016--1029\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.82.pdf", "site": "https://aclanthology.org/2021.acl-long.82/", "pdf_size": 1059474, "gs_citation": 30, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=16165130173874685894&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 10, "aff": "Language Technologies Institute, Carnegie Mellon University; Department of Biomedical Informatics, University of Pittsburgh; Microsoft Research; Human-Computer Interaction Institute, Carnegie Mellon University; Language Technologies Institute, Carnegie Mellon University", "aff_domain": "cs.cmu.edu;pitt.edu;microsoft.com;cs.cmu.edu;cs.cmu.edu", "email": "cs.cmu.edu;pitt.edu;microsoft.com;cs.cmu.edu;cs.cmu.edu", "github": "https://github.com/justinlovelace/robust-kg-completion", "project": "", "author_num": 5, "aff_unique_index": "0;1;2;0;0", "aff_unique_norm": "Carnegie Mellon University;University of Pittsburgh;Microsoft Corporation", "aff_unique_dep": "Language Technologies Institute;Department of Biomedical Informatics;Microsoft Research", "aff_unique_url": "https://www.cmu.edu;https://www.pitt.edu;https://www.microsoft.com/en-us/research", "aff_unique_abbr": "CMU;Pitt;MSR", "aff_campus_unique_index": "0;0;0", "aff_campus_unique": "Pittsburgh;", "aff_country_unique_index": "0;0;0;0;0", "aff_country_unique": "United States" }, { "id": "2021.acl-short.108", "title": "Robust Transfer Learning with Pretrained Language Models through Adapters", "track": "main", "status": "Short", "award": false, "abstract": "Transfer learning with large pretrained transformer-based language models like BERT has become a dominating approach for most NLP tasks. Simply fine-tuning those large language models on downstream tasks or combining it with task-specific pretraining is often not robust. In particular, the performance considerably varies as the random seed changes or the number of pretraining and/or fine-tuning iterations varies, and the fine-tuned model is vulnerable to adversarial attack. We propose a simple yet effective adapter-based approach to mitigate these issues. Specifically, we insert small bottleneck layers (i.e., adapter) within each layer of a pretrained model, then fix the pretrained layers and train the adapter layers on the downstream task data, with (1) task-specific unsupervised pretraining and then (2) task-specific supervised training (e.g., classification, sequence labeling). Our experiments demonstrate that such a training scheme leads to improved stability and adversarial robustness in transfer learning to various downstream tasks.", "author": "Wenjuan Han; Bo Pang; Ying Nian Wu", "authorids": "/w/wenjuan-han/; /b/bo-pang/; /y/ying-nian-wu/", "bibtex": "@inproceedings{han-etal-2021-robust,\n title = \"Robust Transfer Learning with Pretrained Language Models through Adapters\",\n author = \"Han, Wenjuan and\n Pang, Bo and\n Wu, Ying Nian\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-short.108/\",\n doi = \"10.18653/v1/2021.acl-short.108\",\n pages = \"854--861\"\n}", "pdf": "https://aclanthology.org/2021.acl-short.108.pdf", "site": "https://aclanthology.org/2021.acl-short.108/", "pdf_size": 1661588, "gs_citation": 75, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=2341528989207927911&as_sdt=5,48&sciodt=0,48&hl=en", "gs_version_total": 4, "aff": "Beijing Institute for General Artificial Intelligence, Beijing, China; Department of Statistics, University of California, Los Angeles; Department of Statistics, University of California, Los Angeles", "aff_domain": "bigai.ai;ucla.edu;ucla.edu", "email": "bigai.ai;ucla.edu;ucla.edu", "github": "https://github.com/WinnieHAN/Adapter-Robustness.git", "project": "", "author_num": 3, "aff_unique_index": "0;1;1", "aff_unique_norm": "Beijing Institute for General Artificial Intelligence;University of California, Los Angeles", "aff_unique_dep": ";Department of Statistics", "aff_unique_url": ";https://www.ucla.edu", "aff_unique_abbr": ";UCLA", "aff_campus_unique_index": "0;1;1", "aff_campus_unique": "Beijing;Los Angeles", "aff_country_unique_index": "0;1;1", "aff_country_unique": "China;United States" }, { "id": "2021.acl-long.476", "title": "Robustifying Multi-hop QA through Pseudo-Evidentiality Training", "track": "main", "status": "Long", "award": false, "abstract": "This paper studies the bias problem of multi-hop question answering models, of answering correctly without correct reasoning. One way to robustify these models is by supervising to not only answer right, but also with right reasoning chains. An existing direction is to annotate reasoning chains to train models, requiring expensive additional annotations. In contrast, we propose a new approach to learn evidentiality, deciding whether the answer prediction is supported by correct evidences, without such annotations. Instead, we compare counterfactual changes in answer confidence with and without evidence sentences, to generate \u201cpseudo-evidentiality\u201d annotations. We validate our proposed model on an original set and challenge set in HotpotQA, showing that our method is accurate and robust in multi-hop reasoning.", "author": "Kyungjae Lee; Seung-won Hwang; Sang-eun Han; Dohyeon Lee", "authorids": "/k/kyungjae-lee/; /s/seung-won-hwang/; /s/sang-eun-han/; /d/dohyeon-lee/", "bibtex": "@inproceedings{lee-etal-2021-robustifying,\n title = \"Robustifying Multi-hop {QA} through Pseudo-Evidentiality Training\",\n author = \"Lee, Kyungjae and\n Hwang, Seung-won and\n Han, Sang-eun and\n Lee, Dohyeon\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.476/\",\n doi = \"10.18653/v1/2021.acl-long.476\",\n pages = \"6110--6119\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.476.pdf", "site": "https://aclanthology.org/2021.acl-long.476/", "pdf_size": 1435123, "gs_citation": 12, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=15886053101955593753&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 7, "aff": "Yonsei University; Seoul National University; Yonsei University; Yonsei University", "aff_domain": "yonsei.ac.kr;snu.ac.kr;yonsei.ac.kr;yonsei.ac.kr", "email": "yonsei.ac.kr;snu.ac.kr;yonsei.ac.kr;yonsei.ac.kr", "github": "", "project": "", "author_num": 4, "aff_unique_index": "0;1;0;0", "aff_unique_norm": "Yonsei University;Seoul National University", "aff_unique_dep": ";", "aff_unique_url": "https://www.yonsei.ac.kr;https://www.snu.ac.kr", "aff_unique_abbr": "Yonsei;SNU", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0;0;0;0", "aff_country_unique": "South Korea" }, { "id": "2021.acl-long.192", "title": "Robustness Testing of Language Understanding in Task-Oriented Dialog", "track": "main", "status": "Long", "award": false, "abstract": "Most language understanding models in task-oriented dialog systems are trained on a small amount of annotated training data, and evaluated in a small set from the same distribution. However, these models can lead to system failure or undesirable output when being exposed to natural language perturbation or variation in practice. In this paper, we conduct comprehensive evaluation and analysis with respect to the robustness of natural language understanding models, and introduce three important aspects related to language understanding in real-world dialog systems, namely, language variety, speech characteristics, and noise perturbation. We propose a model-agnostic toolkit LAUG to approximate natural language perturbations for testing the robustness issues in task-oriented dialog. Four data augmentation approaches covering the three aspects are assembled in LAUG, which reveals critical robustness issues in state-of-the-art models. The augmented dataset through LAUG can be used to facilitate future research on the robustness testing of language understanding in task-oriented dialog.", "author": "Jiexi Liu; Ryuichi Takanobu; Jiaxin Wen; Dazhen Wan; Hongguang Li; Weiran Nie; Cheng Li; Wei Peng; Minlie Huang", "authorids": "/j/jiexi-liu/; /r/ryuichi-takanobu/; /j/jiaxin-wen/; /d/dazhen-wan/; /h/hongguang-li/; /w/weiran-nie/; /c/cheng-li/; /w/wei-peng/; /m/minlie-huang/", "bibtex": "@inproceedings{liu-etal-2021-robustness,\n title = \"Robustness Testing of Language Understanding in Task-Oriented Dialog\",\n author = \"Liu, Jiexi and\n Takanobu, Ryuichi and\n Wen, Jiaxin and\n Wan, Dazhen and\n Li, Hongguang and\n Nie, Weiran and\n Li, Cheng and\n Peng, Wei and\n Huang, Minlie\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.192/\",\n doi = \"10.18653/v1/2021.acl-long.192\",\n pages = \"2467--2480\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.192.pdf", "site": "https://aclanthology.org/2021.acl-long.192/", "pdf_size": 1382696, "gs_citation": 55, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=13310134398008418380&as_sdt=5,33&sciodt=0,33&hl=en", "gs_version_total": 7, "aff": "CoAI Group, DCST, IAI, BNRIST, Tsinghua University, Beijing, China; CoAI Group, DCST, IAI, BNRIST, Tsinghua University, Beijing, China; CoAI Group, DCST, IAI, BNRIST, Tsinghua University, Beijing, China; CoAI Group, DCST, IAI, BNRIST, Tsinghua University, Beijing, China; Huawei Technologies, Shenzhen, China; Huawei Technologies, Shenzhen, China; Huawei Technologies, Shenzhen, China; Huawei Technologies, Shenzhen, China; CoAI Group, DCST, IAI, BNRIST, Tsinghua University, Beijing, China", "aff_domain": "mails.tsinghua.edu.cn;mails.tsinghua.edu.cn; ; ; ; ; ; ;tsinghua.edu.cn", "email": "mails.tsinghua.edu.cn;mails.tsinghua.edu.cn; ; ; ; ; ; ;tsinghua.edu.cn", "github": "", "project": "", "author_num": 9, "aff_unique_index": "0;0;0;0;1;1;1;1;0", "aff_unique_norm": "Tsinghua University;Huawei Technologies", "aff_unique_dep": "CoAI Group, DCST, IAI, BNRIST;", "aff_unique_url": "https://www.tsinghua.edu.cn;https://www.huawei.com", "aff_unique_abbr": "THU;Huawei", "aff_campus_unique_index": "0;0;0;0;1;1;1;1;0", "aff_campus_unique": "Beijing;Shenzhen", "aff_country_unique_index": "0;0;0;0;0;0;0;0;0", "aff_country_unique": "China" }, { "id": "2021.acl-long.210", "title": "Ruddit: Norms of Offensiveness for English Reddit Comments", "track": "main", "status": "Long", "award": false, "abstract": "On social media platforms, hateful and offensive language negatively impact the mental well-being of users and the participation of people from diverse backgrounds. Automatic methods to detect offensive language have largely relied on datasets with categorical labels. However, comments can vary in their degree of offensiveness. We create the first dataset of English language Reddit comments that has fine-grained, real-valued scores between -1 (maximally supportive) and 1 (maximally offensive). The dataset was annotated using Best\u2013Worst Scaling, a form of comparative annotation that has been shown to alleviate known biases of using rating scales. We show that the method produces highly reliable offensiveness scores. Finally, we evaluate the ability of widely-used neural models to predict offensiveness scores on this new dataset.", "author": "Rishav Hada; Sohi Sudhir; Pushkar Mishra; Helen Yannakoudakis; Saif M. Mohammad; Ekaterina Shutova", "authorids": "/r/rishav-hada/; /s/sohi-sudhir/; /p/pushkar-mishra/; /h/helen-yannakoudakis/; /s/saif-mohammad/; /e/ekaterina-shutova/", "bibtex": "@inproceedings{hada-etal-2021-ruddit,\n title = \"Ruddit: {N}orms of Offensiveness for {E}nglish {R}eddit Comments\",\n author = \"Hada, Rishav and\n Sudhir, Sohi and\n Mishra, Pushkar and\n Yannakoudakis, Helen and\n Mohammad, Saif M. and\n Shutova, Ekaterina\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.210/\",\n doi = \"10.18653/v1/2021.acl-long.210\",\n pages = \"2700--2717\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.210.pdf", "site": "https://aclanthology.org/2021.acl-long.210/", "pdf_size": 993372, "gs_citation": 54, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=815787507685949312&as_sdt=40005&sciodt=0,10&hl=en", "gs_version_total": 10, "aff": "ILLC, University of Amsterdam; ILLC, University of Amsterdam; Facebook AI, London; Dept. of Informatics, King\u2019s College London; National Research Council Canada; ILLC, University of Amsterdam", "aff_domain": "gmail.com;gmail.com;fb.com;kcl.ac.uk;nrc-cnrc.gc.ca;uva.nl", "email": "gmail.com;gmail.com;fb.com;kcl.ac.uk;nrc-cnrc.gc.ca;uva.nl", "github": "", "project": "", "author_num": 6, "aff_unique_index": "0;0;1;2;3;0", "aff_unique_norm": "University of Amsterdam;Facebook;King\u2019s College London;National Research Council Canada", "aff_unique_dep": "ILLC;Facebook AI;Dept. of Informatics;", "aff_unique_url": "https://www.uva.nl;https://www.facebook.com;https://www.kcl.ac.uk;https://www.nrc-cnrc.gc.ca", "aff_unique_abbr": "UvA;Facebook;KCL;NRC-CNRC", "aff_campus_unique_index": "0;0;1;1;0", "aff_campus_unique": "Amsterdam;London;", "aff_country_unique_index": "0;0;1;1;2;0", "aff_country_unique": "Netherlands;United Kingdom;Canada" }, { "id": "2021.acl-long.484", "title": "SENT: Sentence-level Distant Relation Extraction via Negative Training", "track": "main", "status": "Long", "award": false, "abstract": "Distant supervision for relation extraction provides uniform bag labels for each sentence inside the bag, while accurate sentence labels are important for downstream applications that need the exact relation type. Directly using bag labels for sentence-level training will introduce much noise, thus severely degrading performance. In this work, we propose the use of negative training (NT), in which a model is trained using complementary labels regarding that \u201cthe instance does not belong to these complementary labels\u201d. Since the probability of selecting a true label as a complementary label is low, NT provides less noisy information. Furthermore, the model trained with NT is able to separate the noisy data from the training data. Based on NT, we propose a sentence-level framework, SENT, for distant relation extraction. SENT not only filters the noisy data to construct a cleaner dataset, but also performs a re-labeling process to transform the noisy data into useful training data, thus further benefiting the model\u2019s performance. Experimental results show the significant improvement of the proposed method over previous methods on sentence-level evaluation and de-noise effect.", "author": "Ruotian Ma; Tao Gui; Linyang Li; Qi Zhang; Xuanjing Huang; Yaqian Zhou", "authorids": "/r/ruotian-ma/; /t/tao-gui/; /l/linyang-li/; /q/qi-zhang/; /x/xuan-jing-huang/; /y/yaqian-zhou/", "bibtex": "@inproceedings{ma-etal-2021-sent,\n title = \"{SENT}: {S}entence-level Distant Relation Extraction via Negative Training\",\n author = \"Ma, Ruotian and\n Gui, Tao and\n Li, Linyang and\n Zhang, Qi and\n Huang, Xuanjing and\n Zhou, Yaqian\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.484/\",\n doi = \"10.18653/v1/2021.acl-long.484\",\n pages = \"6201--6213\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.484.pdf", "site": "https://aclanthology.org/2021.acl-long.484/", "pdf_size": 1176645, "gs_citation": 42, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=8143000768205212874&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 6, "aff": "School of Computer Science, Fudan University; Institute of Modern Languages and Linguistics, Fudan University; School of Computer Science, Fudan University; School of Computer Science, Fudan University; School of Computer Science, Fudan University; School of Computer Science, Fudan University", "aff_domain": "fudan.edu.cn;fudan.edu.cn;fudan.edu.cn;fudan.edu.cn;fudan.edu.cn;fudan.edu.cn", "email": "fudan.edu.cn;fudan.edu.cn;fudan.edu.cn;fudan.edu.cn;fudan.edu.cn;fudan.edu.cn", "github": "", "project": "", "author_num": 6, "aff_unique_index": "0;0;0;0;0;0", "aff_unique_norm": "Fudan University", "aff_unique_dep": "School of Computer Science", "aff_unique_url": "https://www.fudan.edu.cn", "aff_unique_abbr": "Fudan", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0;0;0;0;0;0", "aff_country_unique": "China" }, { "id": "2021.acl-long.175", "title": "SMURF: SeMantic and linguistic UndeRstanding Fusion for Caption Evaluation via Typicality Analysis", "track": "main", "status": "Long", "award": false, "abstract": "The open-ended nature of visual captioning makes it a challenging area for evaluation. The majority of proposed models rely on specialized training to improve human-correlation, resulting in limited adoption, generalizability, and explainabilty. We introduce \u201ctypicality\u201d, a new formulation of evaluation rooted in information theory, which is uniquely suited for problems lacking a definite ground truth. Typicality serves as our framework to develop a novel semantic comparison, SPARCS, as well as referenceless fluency evaluation metrics. Over the course of our analysis, two separate dimensions of fluency naturally emerge: style, captured by metric SPURTS, and grammar, captured in the form of grammatical outlier penalties. Through extensive experiments and ablation studies on benchmark datasets, we show how these decomposed dimensions of semantics and fluency provide greater system-level insight into captioner differences. Our proposed metrics along with their combination, SMURF, achieve state-of-the-art correlation with human judgment when compared with other rule-based evaluation metrics.", "author": "Joshua Feinglass; Yezhou Yang", "authorids": "/j/joshua-feinglass/; /y/yezhou-yang/", "bibtex": "@inproceedings{feinglass-yang-2021-smurf,\n title = \"{SMURF}: {S}e{M}antic and linguistic {U}nde{R}standing Fusion for Caption Evaluation via Typicality Analysis\",\n author = \"Feinglass, Joshua and\n Yang, Yezhou\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.175/\",\n doi = \"10.18653/v1/2021.acl-long.175\",\n pages = \"2250--2260\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.175.pdf", "site": "https://aclanthology.org/2021.acl-long.175/", "pdf_size": 1115368, "gs_citation": 30, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=15401420579808146569&as_sdt=5,33&sciodt=0,33&hl=en", "gs_version_total": 9, "aff": "Arizona State University; Arizona State University", "aff_domain": "asu.edu;asu.edu", "email": "asu.edu;asu.edu", "github": "https://github.com/JoshuaFeinglass/SMURF", "project": "", "author_num": 2, "aff_unique_index": "0;0", "aff_unique_norm": "Arizona State University", "aff_unique_dep": "", "aff_unique_url": "https://www.asu.edu", "aff_unique_abbr": "ASU", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0;0", "aff_country_unique": "United States" }, { "id": "2021.acl-long.457", "title": "SMedBERT: A Knowledge-Enhanced Pre-trained Language Model with Structured Semantics for Medical Text Mining", "track": "main", "status": "Long", "award": false, "abstract": "Recently, the performance of Pre-trained Language Models (PLMs) has been significantly improved by injecting knowledge facts to enhance their abilities of language understanding. For medical domains, the background knowledge sources are especially useful, due to the massive medical terms and their complicated relations are difficult to understand in text. In this work, we introduce SMedBERT, a medical PLM trained on large-scale medical corpora, incorporating deep structured semantic knowledge from neighbours of linked-entity. In SMedBERT, the mention-neighbour hybrid attention is proposed to learn heterogeneous-entity information, which infuses the semantic representations of entity types into the homogeneous neighbouring entity structure. Apart from knowledge integration as external features, we propose to employ the neighbors of linked-entities in the knowledge graph as additional global contexts of text mentions, allowing them to communicate via shared neighbors, thus enrich their semantic representations. Experiments demonstrate that SMedBERT significantly outperforms strong baselines in various knowledge-intensive Chinese medical tasks. It also improves the performance of other tasks such as question answering, question matching and natural language inference.", "author": "Taolin Zhang; Zerui Cai; Chengyu Wang; Minghui Qiu; Bite Yang; Xiaofeng He", "authorids": "/t/taolin-zhang/; /z/zerui-cai/; /c/chengyu-wang/; /m/minghui-qiu/; /b/bite-yang/; /x/xiaofeng-he/", "bibtex": "@inproceedings{zhang-etal-2021-smedbert,\n title = \"{SM}ed{BERT}: A Knowledge-Enhanced Pre-trained Language Model with Structured Semantics for Medical Text Mining\",\n author = \"Zhang, Taolin and\n Cai, Zerui and\n Wang, Chengyu and\n Qiu, Minghui and\n Yang, Bite and\n He, Xiaofeng\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.457/\",\n doi = \"10.18653/v1/2021.acl-long.457\",\n pages = \"5882--5893\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.457.pdf", "site": "https://aclanthology.org/2021.acl-long.457/", "pdf_size": 873729, "gs_citation": 70, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=3295634559981687676&as_sdt=5,33&sciodt=0,33&hl=en", "gs_version_total": 5, "aff": "School of Software Engineering, East China Normal University+Alibaba Group+Shanghai Key Laboratory of Trsustworthy Computing; School of Computer Science and Technology, East China Normal University; Alibaba Group; Alibaba Group; DXY; School of Computer Science and Technology, East China Normal University+Shanghai Research Institute for Intelligent Autonomous Systems", "aff_domain": "gmail.com;126.com;alibaba-inc.com;alibaba-inc.com;dxy.cn;cs.ecnu.edu.cn", "email": "gmail.com;126.com;alibaba-inc.com;alibaba-inc.com;dxy.cn;cs.ecnu.edu.cn", "github": "https://github.com/MatNLP/SMedBERT", "project": "", "author_num": 6, "aff_unique_index": "0+1+2;0;1;1;3;0+4", "aff_unique_norm": "East China Normal University;Alibaba Group;Shanghai Key Laboratory of Trustworthy Computing;DXY;Shanghai Research Institute for Intelligent Autonomous Systems", "aff_unique_dep": "School of Software Engineering;;Key Laboratory of Trustworthy Computing;;", "aff_unique_url": "http://www.ecnu.edu.cn;https://www.alibaba.com;;;", "aff_unique_abbr": "ECNU;Alibaba;;;", "aff_campus_unique_index": ";", "aff_campus_unique": "", "aff_country_unique_index": "0+0+0;0;0;0;0+0", "aff_country_unique": "China;" }, { "id": "2021.acl-short.136", "title": "SaRoCo: Detecting Satire in a Novel Romanian Corpus of News Articles", "track": "main", "status": "Short", "award": false, "abstract": "In this work, we introduce a corpus for satire detection in Romanian news. We gathered 55,608 public news articles from multiple real and satirical news sources, composing one of the largest corpora for satire detection regardless of language and the only one for the Romanian language. We provide an official split of the text samples, such that training news articles belong to different sources than test news articles, thus ensuring that models do not achieve high performance simply due to overfitting. We conduct experiments with two state-of-the-art deep neural models, resulting in a set of strong baselines for our novel corpus. Our results show that the machine-level accuracy for satire detection in Romanian is quite low (under 73% on the test set) compared to the human-level accuracy (87%), leaving enough room for improvement in future research.", "author": "Ana-Cristina Rogoz; Gaman Mihaela; Radu Tudor Ionescu", "authorids": "/a/ana-cristina-rogoz/; /g/gaman-mihaela/; /r/radu-tudor-ionescu/", "bibtex": "@inproceedings{rogoz-etal-2021-saroco,\n title = \"{S}a{R}o{C}o: Detecting Satire in a Novel {R}omanian Corpus of News Articles\",\n author = \"Rogoz, Ana-Cristina and\n Mihaela, Gaman and\n Ionescu, Radu Tudor\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-short.136/\",\n doi = \"10.18653/v1/2021.acl-short.136\",\n pages = \"1073--1079\"\n}", "pdf": "https://aclanthology.org/2021.acl-short.136.pdf", "site": "https://aclanthology.org/2021.acl-short.136/", "pdf_size": 256783, "gs_citation": 10, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=8856284042119464003&as_sdt=400005&sciodt=0,14&hl=en", "gs_version_total": 6, "aff": "University of Bucharest; University of Bucharest; University of Bucharest", "aff_domain": "unibuc.ro;unibuc.ro;gmail.com", "email": "unibuc.ro;unibuc.ro;gmail.com", "github": "", "project": "", "author_num": 3, "aff_unique_index": "0;0;0", "aff_unique_norm": "University of Bucharest", "aff_unique_dep": "", "aff_unique_url": "https://www.unibuc.ro", "aff_unique_abbr": "Unibuc", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0;0;0", "aff_country_unique": "Romania" }, { "id": "2021.acl-short.13", "title": "Saying No is An Art: Contextualized Fallback Responses for Unanswerable Dialogue Queries", "track": "main", "status": "Short", "award": false, "abstract": "Despite end-to-end neural systems making significant progress in the last decade for task-oriented as well as chit-chat based dialogue systems, most dialogue systems rely on hybrid approaches which use a combination of rule-based, retrieval and generative approaches for generating a set of ranked responses. Such dialogue systems need to rely on a fallback mechanism to respond to out-of-domain or novel user queries which are not answerable within the scope of the dialogue system. While, dialogue systems today rely on static and unnatural responses like \u201cI don\u2019t know the answer to that question\u201d or \u201cI\u2019m not sure about that\u201d, we design a neural approach which generates responses which are contextually aware with the user query as well as say no to the user. Such customized responses provide paraphrasing ability and contextualization as well as improve the interaction with the user and reduce dialogue monotonicity. Our simple approach makes use of rules over dependency parses and a text-to-text transformer fine-tuned on synthetic data of question-response pairs generating highly relevant, grammatical as well as diverse questions. We perform automatic and manual evaluations to demonstrate the efficacy of the system.", "author": "Ashish Shrivastava; Kaustubh Dhole; Abhinav Bhatt; Sharvani Raghunath", "authorids": "/a/ashish-shrivastava/; /k/kaustubh-dhole/; /a/abhinav-bhatt/; /s/sharvani-raghunath/", "bibtex": "@inproceedings{shrivastava-etal-2021-saying,\n title = \"{S}aying {N}o is {A}n {A}rt: {C}ontextualized {F}allback {R}esponses for {U}nanswerable {D}ialogue {Q}ueries\",\n author = \"Shrivastava, Ashish and\n Dhole, Kaustubh and\n Bhatt, Abhinav and\n Raghunath, Sharvani\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-short.13/\",\n doi = \"10.18653/v1/2021.acl-short.13\",\n pages = \"87--92\"\n}", "pdf": "https://aclanthology.org/2021.acl-short.13.pdf", "site": "https://aclanthology.org/2021.acl-short.13/", "pdf_size": 339685, "gs_citation": 6, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=14991488205401536594&as_sdt=800005&sciodt=0,15&hl=en", "gs_version_total": 8, "aff": "Amelia Science, IPsoft R&D; Amelia Science, IPsoft R&D; Universit\u00e4t des Saarlandes; Amelia Science, IPsoft R&D", "aff_domain": "ipsoft.com;ipsoft.com;stud.uni-saarland.de;ipsoft.com", "email": "ipsoft.com;ipsoft.com;stud.uni-saarland.de;ipsoft.com", "github": "", "project": "", "author_num": 4, "aff_unique_index": "0;0;1;0", "aff_unique_norm": "IPsoft;Universit\u00e4t des Saarlandes", "aff_unique_dep": "R&D;", "aff_unique_url": "https://www.ipsoft.com;https://www.uni-saarland.de", "aff_unique_abbr": "IPsoft;UDS", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0;0;1;0", "aff_country_unique": "United States;Germany" }, { "id": "2021.acl-long.566", "title": "Scientific Credibility of Machine Translation Research: A Meta-Evaluation of 769 Papers", "track": "main", "status": "Long", "award": true, "abstract": "This paper presents the first large-scale meta-evaluation of machine translation (MT). We annotated MT evaluations conducted in 769 research papers published from 2010 to 2020. Our study shows that practices for automatic MT evaluation have dramatically changed during the past decade and follow concerning trends. An increasing number of MT evaluations exclusively rely on differences between BLEU scores to draw conclusions, without performing any kind of statistical significance testing nor human evaluation, while at least 108 metrics claiming to be better than BLEU have been proposed. MT evaluations in recent papers tend to copy and compare automatic metric scores from previous work to claim the superiority of a method or an algorithm without confirming neither exactly the same training, validating, and testing data have been used nor the metric scores are comparable. Furthermore, tools for reporting standardized metric scores are still far from being widely adopted by the MT community. After showing how the accumulation of these pitfalls leads to dubious evaluation, we propose a guideline to encourage better automatic MT evaluation along with a simple meta-evaluation scoring method to assess its credibility.", "author": "Benjamin Marie; Atsushi Fujita; Raphael Rubino", "authorids": "/b/benjamin-marie/; /a/atsushi-fujita/; /r/raphael-rubino/", "bibtex": "@inproceedings{marie-etal-2021-scientific,\n title = \"Scientific Credibility of Machine Translation Research: A Meta-Evaluation of 769 Papers\",\n author = \"Marie, Benjamin and\n Fujita, Atsushi and\n Rubino, Raphael\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.566/\",\n doi = \"10.18653/v1/2021.acl-long.566\",\n pages = \"7297--7306\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.566.pdf", "site": "https://aclanthology.org/2021.acl-long.566/", "pdf_size": 346356, "gs_citation": 131, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=1753239617268030744&as_sdt=5,48&sciodt=0,48&hl=en", "gs_version_total": 7, "aff": "National Institute of Information and Communications Technology; National Institute of Information and Communications Technology; National Institute of Information and Communications Technology", "aff_domain": "nict.go.jp;nict.go.jp;nict.go.jp", "email": "nict.go.jp;nict.go.jp;nict.go.jp", "github": "", "project": "", "author_num": 3, "aff_unique_index": "0;0;0", "aff_unique_norm": "National Institute of Information and Communications Technology", "aff_unique_dep": "", "aff_unique_url": "https://www.nict.go.jp/", "aff_unique_abbr": "NICT", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0;0;0", "aff_country_unique": "Japan" }, { "id": "2021.acl-long.365", "title": "Search from History and Reason for Future: Two-stage Reasoning on Temporal Knowledge Graphs", "track": "main", "status": "Long", "award": false, "abstract": "Temporal Knowledge Graphs (TKGs) have been developed and used in many different areas. Reasoning on TKGs that predicts potential facts (events) in the future brings great challenges to existing models. When facing a prediction task, human beings usually search useful historical information (i.e., clues) in their memories and then reason for future meticulously. Inspired by this mechanism, we propose CluSTeR to predict future facts in a two-stage manner, Clue Searching and Temporal Reasoning, accordingly. Specifically, at the clue searching stage, CluSTeR learns a beam search policy via reinforcement learning (RL) to induce multiple clues from historical facts. At the temporal reasoning stage, it adopts a graph convolution network based sequence method to deduce answers from clues. Experiments on four datasets demonstrate the substantial advantages of CluSTeR compared with the state-of-the-art methods. Moreover, the clues found by CluSTeR further provide interpretability for the results.", "author": "Zixuan Li; Xiaolong Jin; Saiping Guan; Wei Li; Jiafeng Guo; Yuanzhuo Wang; Xueqi Cheng", "authorids": "/z/zixuan-li/; /x/xiaolong-jin/; /s/saiping-guan/; /w/wei-li/; /j/jiafeng-guo/; /y/yuanzhuo-wang/; /x/xueqi-cheng/", "bibtex": "@inproceedings{li-etal-2021-search,\n title = \"Search from History and Reason for Future: Two-stage Reasoning on Temporal Knowledge Graphs\",\n author = \"Li, Zixuan and\n Jin, Xiaolong and\n Guan, Saiping and\n Li, Wei and\n Guo, Jiafeng and\n Wang, Yuanzhuo and\n Cheng, Xueqi\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.365/\",\n doi = \"10.18653/v1/2021.acl-long.365\",\n pages = \"4732--4743\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.365.pdf", "site": "https://aclanthology.org/2021.acl-long.365/", "pdf_size": 1569314, "gs_citation": 132, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=7211126597667190656&as_sdt=5,33&sciodt=0,33&hl=en", "gs_version_total": 7, "aff": "School of Computer Science and Technology, University of Chinese Academy of Sciences + CAS Key Laboratory of Network Data Science and Technology, Institute of Computing Technology, Chinese Academy of Sciences; School of Computer Science and Technology, University of Chinese Academy of Sciences + CAS Key Laboratory of Network Data Science and Technology, Institute of Computing Technology, Chinese Academy of Sciences; School of Computer Science and Technology, University of Chinese Academy of Sciences + CAS Key Laboratory of Network Data Science and Technology, Institute of Computing Technology, Chinese Academy of Sciences; Baidu Inc.; School of Computer Science and Technology, University of Chinese Academy of Sciences + CAS Key Laboratory of Network Data Science and Technology, Institute of Computing Technology, Chinese Academy of Sciences; School of Computer Science and Technology, University of Chinese Academy of Sciences + CAS Key Laboratory of Network Data Science and Technology, Institute of Computing Technology, Chinese Academy of Sciences; School of Computer Science and Technology, University of Chinese Academy of Sciences + CAS Key Laboratory of Network Data Science and Technology, Institute of Computing Technology, Chinese Academy of Sciences", "aff_domain": "ict.ac.cn;ict.ac.cn;ict.ac.cn;baidu.com; ; ; ", "email": "ict.ac.cn;ict.ac.cn;ict.ac.cn;baidu.com; ; ; ", "github": "", "project": "", "author_num": 7, "aff_unique_index": "0+1;0+1;0+1;2;0+1;0+1;0+1", "aff_unique_norm": "University of Chinese Academy of Sciences;Chinese Academy of Sciences;Baidu Inc.", "aff_unique_dep": "School of Computer Science and Technology;Institute of Computing Technology;", "aff_unique_url": "http://www.ucas.ac.cn;http://www.cas.cn;https://www.baidu.com", "aff_unique_abbr": "UCAS;CAS;Baidu", "aff_campus_unique_index": ";;;;;", "aff_campus_unique": "", "aff_country_unique_index": "0+0;0+0;0+0;0;0+0;0+0;0+0", "aff_country_unique": "China" }, { "id": "2021.acl-long.111", "title": "Select, Extract and Generate: Neural Keyphrase Generation with Layer-wise Coverage Attention", "track": "main", "status": "Long", "award": false, "abstract": "Natural language processing techniques have demonstrated promising results in keyphrase generation. However, one of the major challenges in neural keyphrase generation is processing long documents using deep neural networks. Generally, documents are truncated before given as inputs to neural networks. Consequently, the models may miss essential points conveyed in the target document. To overcome this limitation, we propose SEG-Net, a neural keyphrase generation model that is composed of two major components, (1) a selector that selects the salient sentences in a document and (2) an extractor-generator that jointly extracts and generates keyphrases from the selected sentences. SEG-Net uses Transformer, a self-attentive architecture, as the basic building block with a novel layer-wise coverage attention to summarize most of the points discussed in the document. The experimental results on seven keyphrase generation benchmarks from scientific and web documents demonstrate that SEG-Net outperforms the state-of-the-art neural generative methods by a large margin.", "author": "Wasi Ahmad; Xiao Bai; Soomin Lee; Kai-Wei Chang", "authorids": "/w/wasi-ahmad/; /x/xiao-bai/; /s/soomin-lee/; /k/kai-wei-chang/", "bibtex": "@inproceedings{ahmad-etal-2021-select,\n title = \"Select, Extract and Generate: Neural Keyphrase Generation with Layer-wise Coverage Attention\",\n author = \"Ahmad, Wasi and\n Bai, Xiao and\n Lee, Soomin and\n Chang, Kai-Wei\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.111/\",\n doi = \"10.18653/v1/2021.acl-long.111\",\n pages = \"1389--1404\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.111.pdf", "site": "https://aclanthology.org/2021.acl-long.111/", "pdf_size": 621754, "gs_citation": 47, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=11746872272643890113&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 3, "aff": "University of California, Los Angeles; Yahoo Research; Yahoo Research; University of California, Los Angeles", "aff_domain": "cs.ucla.edu;verizonmedia.com;verizonmedia.com;cs.ucla.edu", "email": "cs.ucla.edu;verizonmedia.com;verizonmedia.com;cs.ucla.edu", "github": "", "project": "", "author_num": 4, "aff_unique_index": "0;1;1;0", "aff_unique_norm": "University of California, Los Angeles;Yahoo", "aff_unique_dep": ";Yahoo Research", "aff_unique_url": "https://www.ucla.edu;https://research.yahoo.com", "aff_unique_abbr": "UCLA;Yahoo Research", "aff_campus_unique_index": "0;0", "aff_campus_unique": "Los Angeles;", "aff_country_unique_index": "0;0;0;0", "aff_country_unique": "United States" }, { "id": "2021.acl-long.87", "title": "Selecting Informative Contexts Improves Language Model Fine-tuning", "track": "main", "status": "Long", "award": false, "abstract": "Language model fine-tuning is essential for modern natural language processing, but is computationally expensive and time-consuming. Further, the effectiveness of fine-tuning is limited by the inclusion of training examples that negatively affect performance. Here we present a general fine-tuning method that we call information gain filtration for improving the overall training efficiency and final performance of language model fine-tuning. We define the information gain of an example as the improvement on a validation metric after training on that example. A secondary learner is then trained to approximate this quantity. During fine-tuning, this learner selects informative examples and skips uninformative ones. We show that our method has consistent improvement across datasets, fine-tuning tasks, and language model architectures. For example, we achieve a median perplexity of 54.0 on a books dataset compared to 57.3 for standard fine-tuning. We present statistical evidence that offers insight into the improvements of our method over standard fine-tuning. The generality of our method leads us to propose a new paradigm for language model fine-tuning \u2014 we encourage researchers to release pretrained secondary learners on common corpora to promote efficient and effective fine-tuning, thereby improving the performance and reducing the overall energy footprint of language model fine-tuning.", "author": "Richard Antonello; Nicole Beckage; Javier Turek; Alexander Huth", "authorids": "/r/richard-antonello/; /n/nicole-beckage/; /j/javier-turek/; /a/alexander-huth/", "bibtex": "@inproceedings{antonello-etal-2021-selecting,\n title = \"Selecting Informative Contexts Improves Language Model Fine-tuning\",\n author = \"Antonello, Richard and\n Beckage, Nicole and\n Turek, Javier and\n Huth, Alexander\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.87/\",\n doi = \"10.18653/v1/2021.acl-long.87\",\n pages = \"1072--1085\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.87.pdf", "site": "https://aclanthology.org/2021.acl-long.87/", "pdf_size": 596471, "gs_citation": 15, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=7230318612763139275&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 8, "aff": "UT Austin; Intel Labs; Intel Labs; UT Austin", "aff_domain": "utexas.edu;intel.com;intel.com;cs.utexas.edu", "email": "utexas.edu;intel.com;intel.com;cs.utexas.edu", "github": "", "project": "", "author_num": 4, "aff_unique_index": "0;1;1;0", "aff_unique_norm": "University of Texas at Austin;Intel Corporation", "aff_unique_dep": ";Intel Labs", "aff_unique_url": "https://www.utexas.edu;https://www.intel.com", "aff_unique_abbr": "UT Austin;Intel", "aff_campus_unique_index": "0;0", "aff_campus_unique": "Austin;", "aff_country_unique_index": "0;0;0;0", "aff_country_unique": "United States" }, { "id": "2021.acl-long.504", "title": "Selective Knowledge Distillation for Neural Machine Translation", "track": "main", "status": "Long", "award": false, "abstract": "Neural Machine Translation (NMT) models achieve state-of-the-art performance on many translation benchmarks. As an active research field in NMT, knowledge distillation is widely applied to enhance the model\u2019s performance by transferring teacher model\u2019s knowledge on each training sample. However, previous work rarely discusses the different impacts and connections among these samples, which serve as the medium for transferring teacher knowledge. In this paper, we design a novel protocol that can effectively analyze the different impacts of samples by comparing various samples\u2019 partitions. Based on above protocol, we conduct extensive experiments and find that the teacher\u2019s knowledge is not the more, the better. Knowledge over specific samples may even hurt the whole performance of knowledge distillation. Finally, to address these issues, we propose two simple yet effective strategies, i.e., batch-level and global-level selections, to pick suitable samples for distillation. We evaluate our approaches on two large-scale machine translation tasks, WMT\u201914 English-German and WMT\u201919 Chinese-English. Experimental results show that our approaches yield up to +1.28 and +0.89 BLEU points improvements over the Transformer baseline, respectively.", "author": "Fusheng Wang; Jianhao Yan; Fandong Meng; Jie Zhou", "authorids": "/f/fusheng-wang/; /j/jianhao-yan/; /f/fandong-meng/; /j/jie-zhou/", "bibtex": "@inproceedings{wang-etal-2021-selective,\n title = \"Selective Knowledge Distillation for Neural Machine Translation\",\n author = \"Wang, Fusheng and\n Yan, Jianhao and\n Meng, Fandong and\n Zhou, Jie\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.504/\",\n doi = \"10.18653/v1/2021.acl-long.504\",\n pages = \"6456--6466\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.504.pdf", "site": "https://aclanthology.org/2021.acl-long.504/", "pdf_size": 502708, "gs_citation": 63, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=16194316247246201137&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 7, "aff": "Peking University, China+Pattern Recognition Center, WeChat AI, Tencent, China; Pattern Recognition Center, WeChat AI, Tencent, China; Pattern Recognition Center, WeChat AI, Tencent, China; Pattern Recognition Center, WeChat AI, Tencent, China", "aff_domain": "pku.edu.com;tencent.com;tencent.com; ", "email": "pku.edu.com;tencent.com;tencent.com; ", "github": "https://github.com/LeslieOverfitting/selective_distillation", "project": "", "author_num": 4, "aff_unique_index": "0+1;1;1;1", "aff_unique_norm": "Peking University;Tencent", "aff_unique_dep": ";Pattern Recognition Center", "aff_unique_url": "http://www.pku.edu.cn;https://www.tencent.com", "aff_unique_abbr": "Peking U;Tencent", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0+0;0;0;0", "aff_country_unique": "China" }, { "id": "2021.acl-long.292", "title": "Self-Attention Networks Can Process Bounded Hierarchical Languages", "track": "main", "status": "Long", "award": false, "abstract": "Despite their impressive performance in NLP, self-attention networks were recently proved to be limited for processing formal languages with hierarchical structure, such as Dyck-k, the language consisting of well-nested parentheses of k types. This suggested that natural language can be approximated well with models that are too weak for formal languages, or that the role of hierarchy and recursion in natural language might be limited. We qualify this implication by proving that self-attention networks can process Dyck-(k, D), the subset of Dyck-k with depth bounded by D, which arguably better captures the bounded hierarchical structure of natural language. Specifically, we construct a hard-attention network with D+1 layers and O(log k) memory size (per token per layer) that recognizes Dyck-(k, D), and a soft-attention network with two layers and O(log k) memory size that generates Dyck-(k, D). Experiments show that self-attention networks trained on Dyck-(k, D) generalize to longer inputs with near-perfect accuracy, and also verify the theoretical memory advantage of self-attention networks over recurrent networks.", "author": "Shunyu Yao; Binghui Peng; Christos Papadimitriou; Karthik Narasimhan", "authorids": "/s/shunyu-yao/; /b/binghui-peng/; /c/christos-papadimitriou/; /k/karthik-narasimhan/", "bibtex": "@inproceedings{yao-etal-2021-self,\n title = \"Self-Attention Networks Can Process Bounded Hierarchical Languages\",\n author = \"Yao, Shunyu and\n Peng, Binghui and\n Papadimitriou, Christos and\n Narasimhan, Karthik\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.292/\",\n doi = \"10.18653/v1/2021.acl-long.292\",\n pages = \"3770--3785\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.292.pdf", "site": "https://aclanthology.org/2021.acl-long.292/", "pdf_size": 1774854, "gs_citation": 101, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=1100360434921952882&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 7, "aff": "Princeton University; Columbia University; Columbia University; Princeton University", "aff_domain": "princeton.edu;columbia.edu;columbia.edu;princeton.edu", "email": "princeton.edu;columbia.edu;columbia.edu;princeton.edu", "github": "https://github.com/princeton-nlp/dyck-transformer", "project": "", "author_num": 4, "aff_unique_index": "0;1;1;0", "aff_unique_norm": "Princeton University;Columbia University", "aff_unique_dep": ";", "aff_unique_url": "https://www.princeton.edu;https://www.columbia.edu", "aff_unique_abbr": "Princeton;Columbia", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0;0;0;0", "aff_country_unique": "United States" }, { "id": "2021.acl-long.197", "title": "Self-Guided Contrastive Learning for BERT Sentence Representations", "track": "main", "status": "Long", "award": false, "abstract": "Although BERT and its variants have reshaped the NLP landscape, it still remains unclear how best to derive sentence embeddings from such pre-trained Transformers. In this work, we propose a contrastive learning method that utilizes self-guidance for improving the quality of BERT sentence representations. Our method fine-tunes BERT in a self-supervised fashion, does not rely on data augmentation, and enables the usual [CLS] token embeddings to function as sentence vectors. Moreover, we redesign the contrastive learning objective (NT-Xent) and apply it to sentence representation learning. We demonstrate with extensive experiments that our approach is more effective than competitive baselines on diverse sentence-related tasks. We also show it is efficient at inference and robust to domain shifts.", "author": "Taeuk Kim; Kang Min Yoo; Sang-goo Lee", "authorids": "/t/taeuk-kim/; /k/kang-min-yoo/; /s/sang-goo-lee/", "bibtex": "@inproceedings{kim-etal-2021-self,\n title = \"Self-Guided Contrastive Learning for {BERT} Sentence Representations\",\n author = \"Kim, Taeuk and\n Yoo, Kang Min and\n Lee, Sang-goo\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.197/\",\n doi = \"10.18653/v1/2021.acl-long.197\",\n pages = \"2528--2540\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.197.pdf", "site": "https://aclanthology.org/2021.acl-long.197/", "pdf_size": 1017220, "gs_citation": 227, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=11828830862916620500&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 6, "aff": "Dept. of Computer Science and Engineering, Seoul National University, Seoul, Korea; NA VER AI Lab, Seongnam, Korea; Dept. of Computer Science and Engineering, Seoul National University, Seoul, Korea", "aff_domain": "europa.snu.ac.kr;navercorp.com;europa.snu.ac.kr", "email": "europa.snu.ac.kr;navercorp.com;europa.snu.ac.kr", "github": "", "project": "", "author_num": 3, "aff_unique_index": "0;1;0", "aff_unique_norm": "Seoul National University;NA VER AI Lab", "aff_unique_dep": "Dept. of Computer Science and Engineering;", "aff_unique_url": "https://www.snu.ac.kr;", "aff_unique_abbr": "SNU;", "aff_campus_unique_index": "0;1;0", "aff_campus_unique": "Seoul;Seongnam", "aff_country_unique_index": "0;0;0", "aff_country_unique": "Korea" }, { "id": "2021.acl-long.33", "title": "Self-Supervised Multimodal Opinion Summarization", "track": "main", "status": "Long", "award": false, "abstract": "Recently, opinion summarization, which is the generation of a summary from multiple reviews, has been conducted in a self-supervised manner by considering a sampled review as a pseudo summary. However, non-text data such as image and metadata related to reviews have been considered less often. To use the abundant information contained in non-text data, we propose a self-supervised multimodal opinion summarization framework called MultimodalSum. Our framework obtains a representation of each modality using a separate encoder for each modality, and the text decoder generates a summary. To resolve the inherent heterogeneity of multimodal data, we propose a multimodal training pipeline. We first pretrain the text encoder\u2013decoder based solely on text modality data. Subsequently, we pretrain the non-text modality encoders by considering the pretrained text decoder as a pivot for the homogeneous representation of multimodal data. Finally, to fuse multimodal representations, we train the entire framework in an end-to-end manner. We demonstrate the superiority of MultimodalSum by conducting experiments on Yelp and Amazon datasets.", "author": "Jinbae Im; Moonki Kim; Hoyeop Lee; Hyunsouk Cho; Sehee Chung", "authorids": "/j/jinbae-im/; /m/moonki-kim/; /h/hoyeop-lee/; /h/hyunsouk-cho/; /s/sehee-chung/", "bibtex": "@inproceedings{im-etal-2021-self,\n title = \"Self-Supervised Multimodal Opinion Summarization\",\n author = \"Im, Jinbae and\n Kim, Moonki and\n Lee, Hoyeop and\n Cho, Hyunsouk and\n Chung, Sehee\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.33/\",\n doi = \"10.18653/v1/2021.acl-long.33\",\n pages = \"388--403\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.33.pdf", "site": "https://aclanthology.org/2021.acl-long.33/", "pdf_size": 1170821, "gs_citation": 39, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=16666871202850694535&as_sdt=80000005&sciodt=0,23&hl=en", "gs_version_total": 6, "aff": "Knowledge AI Lab., NCSOFT Co., South Korea; Knowledge AI Lab., NCSOFT Co., South Korea; Knowledge AI Lab., NCSOFT Co., South Korea; Knowledge AI Lab., NCSOFT Co., South Korea; Knowledge AI Lab., NCSOFT Co., South Korea", "aff_domain": "ncsoft.com;ncsoft.com;ncsoft.com;ncsoft.com;ncsoft.com", "email": "ncsoft.com;ncsoft.com;ncsoft.com;ncsoft.com;ncsoft.com", "github": "", "project": "", "author_num": 5, "aff_unique_index": "0;0;0;0;0", "aff_unique_norm": "NCSOFT Co.", "aff_unique_dep": "Knowledge AI Lab.", "aff_unique_url": "https://www.ncsoft.com", "aff_unique_abbr": "NCSOFT", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0;0;0;0;0", "aff_country_unique": "South Korea" }, { "id": "2021.acl-long.221", "title": "Self-Training Sampling with Monolingual Data Uncertainty for Neural Machine Translation", "track": "main", "status": "Long", "award": false, "abstract": "Self-training has proven effective for improving NMT performance by augmenting model training with synthetic parallel data. The common practice is to construct synthetic data based on a randomly sampled subset of large-scale monolingual data, which we empirically show is sub-optimal. In this work, we propose to improve the sampling procedure by selecting the most informative monolingual sentences to complement the parallel data. To this end, we compute the uncertainty of monolingual sentences using the bilingual dictionary extracted from the parallel data. Intuitively, monolingual sentences with lower uncertainty generally correspond to easy-to-translate patterns which may not provide additional gains. Accordingly, we design an uncertainty-based sampling strategy to efficiently exploit the monolingual data for self-training, in which monolingual sentences with higher uncertainty would be sampled with higher probability. Experimental results on large-scale WMT English\u21d2German and English\u21d2Chinese datasets demonstrate the effectiveness of the proposed approach. Extensive analyses suggest that emphasizing the learning on uncertain monolingual sentences by our approach does improve the translation quality of high-uncertainty sentences and also benefits the prediction of low-frequency words at the target side.", "author": "Wenxiang Jiao; Xing Wang; Zhaopeng Tu; Shuming Shi; Michael Lyu; Irwin King", "authorids": "/w/wenxiang-jiao/; /x/xing-wang/; /z/zhaopeng-tu/; /s/shuming-shi/; /m/michael-lyu/; /i/irwin-king/", "bibtex": "@inproceedings{jiao-etal-2021-self,\n title = \"Self-Training Sampling with Monolingual Data Uncertainty for Neural Machine Translation\",\n author = \"Jiao, Wenxiang and\n Wang, Xing and\n Tu, Zhaopeng and\n Shi, Shuming and\n Lyu, Michael and\n King, Irwin\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.221/\",\n doi = \"10.18653/v1/2021.acl-long.221\",\n pages = \"2840--2850\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.221.pdf", "site": "https://aclanthology.org/2021.acl-long.221/", "pdf_size": 1444925, "gs_citation": 44, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=17680322795084509688&as_sdt=5,31&sciodt=0,31&hl=en", "gs_version_total": 6, "aff": "Department of Computer Science and Engineering, The Chinese University of Hong Kong, HKSAR, China+Tencent AI Lab; Tencent AI Lab; Tencent AI Lab; Tencent AI Lab; Department of Computer Science and Engineering, The Chinese University of Hong Kong, HKSAR, China; Department of Computer Science and Engineering, The Chinese University of Hong Kong, HKSAR, China", "aff_domain": "cse.cuhk.edu.hk;tencent.com;tencent.com;tencent.com;cse.cuhk.edu.hk;cse.cuhk.edu.hk", "email": "cse.cuhk.edu.hk;tencent.com;tencent.com;tencent.com;cse.cuhk.edu.hk;cse.cuhk.edu.hk", "github": "https://github.com/wxjiao/UncSamp", "project": "", "author_num": 6, "aff_unique_index": "0+1;1;1;1;0;0", "aff_unique_norm": "The Chinese University of Hong Kong;Tencent", "aff_unique_dep": "Department of Computer Science and Engineering;Tencent AI Lab", "aff_unique_url": "https://www.cuhk.edu.hk;https://ai.tencent.com", "aff_unique_abbr": "CUHK;Tencent AI Lab", "aff_campus_unique_index": "0;0;0", "aff_campus_unique": "Hong Kong;", "aff_country_unique_index": "0+0;0;0;0;0;0", "aff_country_unique": "China" }, { "id": "2021.acl-long.348", "title": "SemFace: Pre-training Encoder and Decoder with a Semantic Interface for Neural Machine Translation", "track": "main", "status": "Long", "award": false, "abstract": "While pre-training techniques are working very well in natural language processing, how to pre-train a decoder and effectively use it for neural machine translation (NMT) still remains a tricky issue. The main reason is that the cross-attention module between the encoder and decoder cannot be pre-trained, and the combined encoder-decoder model cannot work well in the fine-tuning stage because the inputs of the decoder cross-attention come from unknown encoder outputs. In this paper, we propose a better pre-training method for NMT by defining a semantic interface (SemFace) between the pre-trained encoder and the pre-trained decoder. Specifically, we propose two types of semantic interfaces, including CL-SemFace which regards cross-lingual embeddings as an interface, and VQ-SemFace which employs vector quantized embeddings to constrain the encoder outputs and decoder inputs in the same language-independent space. We conduct massive experiments on six supervised translation pairs and three unsupervised pairs. Experimental results demonstrate that our proposed SemFace can effectively connect the pre-trained encoder and decoder, and achieves significant improvement by 3.7 and 1.5 BLEU points on the two tasks respectively compared with previous pre-training-based NMT models.", "author": "Shuo Ren; Long Zhou; Shujie Liu; Furu Wei; Ming Zhou; Shuai Ma", "authorids": "/s/shuo-ren/; /l/long-zhou/; /s/shujie-liu/; /f/furu-wei/; /m/ming-zhou/; /s/shuai-ma/", "bibtex": "@inproceedings{ren-etal-2021-semface,\n title = \"{S}em{F}ace: Pre-training Encoder and Decoder with a Semantic Interface for Neural Machine Translation\",\n author = \"Ren, Shuo and\n Zhou, Long and\n Liu, Shujie and\n Wei, Furu and\n Zhou, Ming and\n Ma, Shuai\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.348/\",\n doi = \"10.18653/v1/2021.acl-long.348\",\n pages = \"4518--4527\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.348.pdf", "site": "https://aclanthology.org/2021.acl-long.348/", "pdf_size": 647626, "gs_citation": 18, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=8945837455864735822&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 3, "aff": "SKLSDE Lab, Beihang University, Beijing, China+Microsoft Research Asia, Beijing, China; Microsoft Research Asia, Beijing, China; Microsoft Research Asia, Beijing, China; Microsoft Research Asia, Beijing, China; Microsoft Research Asia, Beijing, China; SKLSDE Lab, Beihang University, Beijing, China", "aff_domain": "buaa.edu.cn;microsoft.com;microsoft.com;microsoft.com;microsoft.com;buaa.edu.cn", "email": "buaa.edu.cn;microsoft.com;microsoft.com;microsoft.com;microsoft.com;buaa.edu.cn", "github": "", "project": "", "author_num": 6, "aff_unique_index": "0+1;1;1;1;1;0", "aff_unique_norm": "Beihang University;Microsoft Research Asia", "aff_unique_dep": "SKLSDE Lab;Research", "aff_unique_url": "http://www.buaa.edu.cn;https://www.microsoft.com/en-us/research/group/asia", "aff_unique_abbr": ";MSRA", "aff_campus_unique_index": "0+0;0;0;0;0;0", "aff_campus_unique": "Beijing", "aff_country_unique_index": "0+0;0;0;0;0;0", "aff_country_unique": "China" }, { "id": "2021.acl-short.102", "title": "Semantic Frame Induction using Masked Word Embeddings and Two-Step Clustering", "track": "main", "status": "Short", "award": false, "abstract": "Recent studies on semantic frame induction show that relatively high performance has been achieved by using clustering-based methods with contextualized word embeddings. However, there are two potential drawbacks to these methods: one is that they focus too much on the superficial information of the frame-evoking verb and the other is that they tend to divide the instances of the same verb into too many different frame clusters. To overcome these drawbacks, we propose a semantic frame induction method using masked word embeddings and two-step clustering. Through experiments on the English FrameNet data, we demonstrate that using the masked word embeddings is effective for avoiding too much reliance on the surface information of frame-evoking verbs and that two-step clustering can improve the number of resulting frame clusters for the instances of the same verb.", "author": "Kosuke Yamada; Ryohei Sasano; Koichi Takeda", "authorids": "/k/kosuke-yamada/; /r/ryohei-sasano/; /k/koichi-takeda/", "bibtex": "@inproceedings{yamada-etal-2021-semantic,\n title = \"Semantic Frame Induction using Masked Word Embeddings and Two-Step Clustering\",\n author = \"Yamada, Kosuke and\n Sasano, Ryohei and\n Takeda, Koichi\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-short.102/\",\n doi = \"10.18653/v1/2021.acl-short.102\",\n pages = \"811--816\"\n}", "pdf": "https://aclanthology.org/2021.acl-short.102.pdf", "site": "https://aclanthology.org/2021.acl-short.102/", "pdf_size": 458314, "gs_citation": 16, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=7218913880849131708&as_sdt=5,33&sciodt=0,33&hl=en", "gs_version_total": 5, "aff": "Graduate School of Informatics, Nagoya University, Japan; Graduate School of Informatics, Nagoya University, Japan + RIKEN Center for Advanced Intelligence Project, Japan; Graduate School of Informatics, Nagoya University, Japan", "aff_domain": "c.mbox.nagoya-u.ac.jp;i.nagoya-u.ac.jp;i.nagoya-u.ac.jp", "email": "c.mbox.nagoya-u.ac.jp;i.nagoya-u.ac.jp;i.nagoya-u.ac.jp", "github": "", "project": "", "author_num": 3, "aff_unique_index": "0;0+1;0", "aff_unique_norm": "Nagoya University;RIKEN Center for Advanced Intelligence Project", "aff_unique_dep": "Graduate School of Informatics;Center for Advanced Intelligence Project", "aff_unique_url": "https://www.nagoya-u.ac.jp;https://www.riken.jp/en/c-aip/", "aff_unique_abbr": "Nagoya U;RIKEN C-AIP", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0;0+0;0", "aff_country_unique": "Japan" }, { "id": "2021.acl-long.342", "title": "Semantic Representation for Dialogue Modeling", "track": "main", "status": "Long", "award": false, "abstract": "Although neural models have achieved competitive results in dialogue systems, they have shown limited ability in representing core semantics, such as ignoring important entities. To this end, we exploit Abstract Meaning Representation (AMR) to help dialogue modeling. Compared with the textual input, AMR explicitly provides core semantic knowledge and reduces data sparsity. We develop an algorithm to construct dialogue-level AMR graphs from sentence-level AMRs and explore two ways to incorporate AMRs into dialogue systems. Experimental results on both dialogue understanding and response generation tasks show the superiority of our model. To our knowledge, we are the first to leverage a formal semantic representation into neural dialogue modeling.", "author": "Xuefeng Bai; Yulong Chen; Linfeng Song; Yue Zhang", "authorids": "/x/xuefeng-bai/; /y/yulong-chen/; /l/linfeng-song/; /y/yue-zhang/", "bibtex": "@inproceedings{bai-etal-2021-semantic,\n title = \"Semantic Representation for Dialogue Modeling\",\n author = \"Bai, Xuefeng and\n Chen, Yulong and\n Song, Linfeng and\n Zhang, Yue\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.342/\",\n doi = \"10.18653/v1/2021.acl-long.342\",\n pages = \"4430--4445\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.342.pdf", "site": "https://aclanthology.org/2021.acl-long.342/", "pdf_size": 965091, "gs_citation": 65, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=1575272920191778907&as_sdt=5,44&sciodt=0,44&hl=en", "gs_version_total": 6, "aff": "Zhejiang University+School of Engineering, Westlake University; Zhejiang University+School of Engineering, Westlake University; Tencent AI Lab; School of Engineering, Westlake University+Institute of Advanced Technology, Westlake Institute for Advanced Study", "aff_domain": ";;;", "email": ";;;", "github": "", "project": "", "author_num": 4, "aff_unique_index": "0+1;0+1;2;1+3", "aff_unique_norm": "Zhejiang University;Westlake University;Tencent;Westlake Institute for Advanced Study", "aff_unique_dep": ";School of Engineering;Tencent AI Lab;Institute of Advanced Technology", "aff_unique_url": "https://www.zju.edu.cn;https://www.westlake.edu.cn;https://ai.tencent.com;http://www.wias.org.cn/", "aff_unique_abbr": "ZJU;;Tencent AI Lab;WIAS", "aff_campus_unique_index": ";;", "aff_campus_unique": "", "aff_country_unique_index": "0+0;0+0;0;0+0", "aff_country_unique": "China" }, { "id": "2021.acl-long.391", "title": "Semi-Supervised Text Classification with Balanced Deep Representation Distributions", "track": "main", "status": "Long", "award": false, "abstract": "Semi-Supervised Text Classification (SSTC) mainly works under the spirit of self-training. They initialize the deep classifier by training over labeled texts; and then alternatively predict unlabeled texts as their pseudo-labels and train the deep classifier over the mixture of labeled and pseudo-labeled texts. Naturally, their performance is largely affected by the accuracy of pseudo-labels for unlabeled texts. Unfortunately, they often suffer from low accuracy because of the margin bias problem caused by the large difference between representation distributions of labels in SSTC. To alleviate this problem, we apply the angular margin loss, and perform Gaussian linear transformation to achieve balanced label angle variances, i.e., the variance of label angles of texts within the same label. More accuracy of predicted pseudo-labels can be achieved by constraining all label angle variances balanced, where they are estimated over both labeled and pseudo-labeled texts during self-training loops. With this insight, we propose a novel SSTC method, namely Semi-Supervised Text Classification with Balanced Deep representation Distributions (S2TC-BDD). To evaluate S2TC-BDD, we compare it against the state-of-the-art SSTC methods. Empirical results demonstrate the effectiveness of S2TC-BDD, especially when the labeled texts are scarce.", "author": "Changchun Li; Ximing Li; Jihong Ouyang", "authorids": "/c/changchun-li/; /x/ximing-li/; /j/jihong-ouyang/", "bibtex": "@inproceedings{li-etal-2021-semi-supervised,\n title = \"Semi-Supervised Text Classification with Balanced Deep Representation Distributions\",\n author = \"Li, Changchun and\n Li, Ximing and\n Ouyang, Jihong\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.391/\",\n doi = \"10.18653/v1/2021.acl-long.391\",\n pages = \"5044--5053\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.391.pdf", "site": "https://aclanthology.org/2021.acl-long.391/", "pdf_size": 995691, "gs_citation": 57, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=16946864907736660605&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 2, "aff": "College of Computer Science and Technology, Jilin University, China+Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, China; College of Computer Science and Technology, Jilin University, China+Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, China; College of Computer Science and Technology, Jilin University, China+Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, China", "aff_domain": "gmail.com;gmail.com;jlu.edu.cn", "email": "gmail.com;gmail.com;jlu.edu.cn", "github": "", "project": "", "author_num": 3, "aff_unique_index": "0+1;0+1;0+1", "aff_unique_norm": "Jilin University;Key Laboratory of Symbolic Computation and Knowledge Engineering", "aff_unique_dep": "College of Computer Science and Technology;Ministry of Education", "aff_unique_url": "http://www.jlu.edu.cn;", "aff_unique_abbr": "JLU;", "aff_campus_unique_index": ";;", "aff_campus_unique": "", "aff_country_unique_index": "0+0;0+0;0+0", "aff_country_unique": "China" }, { "id": "2021.acl-short.121", "title": "Sequence to General Tree: Knowledge-Guided Geometry Word Problem Solving", "track": "main", "status": "Short", "award": false, "abstract": "With the recent advancements in deep learning, neural solvers have gained promising results in solving math word problems. However, these SOTA solvers only generate binary expression trees that contain basic arithmetic operators and do not explicitly use the math formulas. As a result, the expression trees they produce are lengthy and uninterpretable because they need to use multiple operators and constants to represent one single formula. In this paper, we propose sequence-to-general tree (S2G) that learns to generate interpretable and executable operation trees where the nodes can be formulas with an arbitrary number of arguments. With nodes now allowed to be formulas, S2G can learn to incorporate mathematical domain knowledge into problem-solving, making the results more interpretable. Experiments show that S2G can achieve a better performance against strong baselines on problems that require domain knowledge.", "author": "Shih-hung Tsai; Chao-Chun Liang; Hsin-Min Wang; Keh-Yih Su", "authorids": "/s/shih-hung-tsai/; /c/chao-chun-liang/; /h/hsin-min-wang/; /k/keh-yih-su/", "bibtex": "@inproceedings{tsai-etal-2021-sequence,\n title = \"Sequence to General Tree: Knowledge-Guided Geometry Word Problem Solving\",\n author = \"Tsai, Shih-hung and\n Liang, Chao-Chun and\n Wang, Hsin-Min and\n Su, Keh-Yih\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-short.121/\",\n doi = \"10.18653/v1/2021.acl-short.121\",\n pages = \"964--972\"\n}", "pdf": "https://aclanthology.org/2021.acl-short.121.pdf", "site": "https://aclanthology.org/2021.acl-short.121/", "pdf_size": 398935, "gs_citation": 24, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=3199383185408510632&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 9, "aff": "Institute of Information Science, Academia Sinica, Taiwan; Institute of Information Science, Academia Sinica, Taiwan; Institute of Information Science, Academia Sinica, Taiwan; Institute of Information Science, Academia Sinica, Taiwan", "aff_domain": "iis.sinica.edu.tw;iis.sinica.edu.tw;iis.sinica.edu.tw;iis.sinica.edu.tw", "email": "iis.sinica.edu.tw;iis.sinica.edu.tw;iis.sinica.edu.tw;iis.sinica.edu.tw", "github": "https://github.com/doublebite/Sequence-to-General-tree/", "project": "", "author_num": 4, "aff_unique_index": "0;0;0;0", "aff_unique_norm": "Academia Sinica", "aff_unique_dep": "Institute of Information Science", "aff_unique_url": "https://www.sinica.edu.tw", "aff_unique_abbr": "AS", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0;0;0;0", "aff_country_unique": "Taiwan, China" }, { "id": "2021.acl-long.427", "title": "Shortformer: Better Language Modeling using Shorter Inputs", "track": "main", "status": "Long", "award": false, "abstract": "Increasing the input length has been a driver of progress in language modeling with transformers. We identify conditions where shorter inputs are not harmful, and achieve perplexity and efficiency improvements through two new methods that decrease input length. First, we show that initially training a model on short subsequences before moving on to longer ones both reduces overall training time and, surprisingly, substantially improves perplexity. Second, we show how to improve the efficiency of recurrence methods in transformers, which let models condition on previously processed tokens when generating sequences that exceed the maximal length the transformer can handle at once. Existing methods require computationally expensive relative position embeddings; we introduce a simple alternative of adding absolute position embeddings to queries and keys instead of to word embeddings, which efficiently produces superior results. We show that these recurrent models also benefit from short input lengths. Combining these techniques speeds up training by a factor of 1.65, reduces memory usage, and substantially improves perplexity on WikiText-103, without adding any parameters.", "author": "Ofir Press; Noah A. Smith; Mike Lewis", "authorids": "/o/ofir-press/; /n/noah-a-smith/; /m/mike-lewis/", "bibtex": "@inproceedings{press-etal-2021-shortformer,\n title = \"Shortformer: Better Language Modeling using Shorter Inputs\",\n author = \"Press, Ofir and\n Smith, Noah A. and\n Lewis, Mike\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.427/\",\n doi = \"10.18653/v1/2021.acl-long.427\",\n pages = \"5493--5505\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.427.pdf", "site": "https://aclanthology.org/2021.acl-long.427/", "pdf_size": 468411, "gs_citation": 85, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=9761256362533485299&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 7, "aff": "Paul G. Allen School of Computer Science & Engineering, University of Washington + Facebook AI Research; Paul G. Allen School of Computer Science & Engineering, University of Washington + Allen Institute for AI; Facebook AI Research", "aff_domain": "cs.washington.edu; ; ", "email": "cs.washington.edu; ; ", "github": "https://github.com/ofirpress/shortformer", "project": "", "author_num": 3, "aff_unique_index": "0+1;0+2;1", "aff_unique_norm": "University of Washington;Facebook;Allen Institute for AI", "aff_unique_dep": "Paul G. Allen School of Computer Science & Engineering;Facebook AI Research;", "aff_unique_url": "https://www.washington.edu;https://research.facebook.com;https://allenai.org", "aff_unique_abbr": "UW;FAIR;AI2", "aff_campus_unique_index": "0;0", "aff_campus_unique": "Seattle;", "aff_country_unique_index": "0+0;0+0;0", "aff_country_unique": "United States" }, { "id": "2021.acl-short.135", "title": "SimCLS: A Simple Framework for Contrastive Learning of Abstractive Summarization", "track": "main", "status": "Short", "award": false, "abstract": "In this paper, we present a conceptually simple while empirically powerful framework for abstractive summarization, SimCLS, which can bridge the gap between the learning objective and evaluation metrics resulting from the currently dominated sequence-to-sequence learning framework by formulating text generation as a reference-free evaluation problem (i.e., quality estimation) assisted by contrastive learning. Experimental results show that, with minor modification over existing top-scoring systems, SimCLS can improve the performance of existing top-performing models by a large margin. Particularly, 2.51 absolute improvement against BART and 2.50 over PEGASUS w.r.t ROUGE-1 on the CNN/DailyMail dataset, driving the state-of-the-art performance to a new level. We have open-sourced our codes and results: https://github.com/yixinL7/SimCLS. Results of our proposed models have been deployed into ExplainaBoard platform, which allows researchers to understand our systems in a more fine-grained way.", "author": "Yixin Liu; Pengfei Liu", "authorids": "/y/yixin-liu/; /p/pengfei-liu/", "bibtex": "@inproceedings{liu-liu-2021-simcls,\n title = \"{S}im{CLS}: A Simple Framework for Contrastive Learning of Abstractive Summarization\",\n author = \"Liu, Yixin and\n Liu, Pengfei\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-short.135/\",\n doi = \"10.18653/v1/2021.acl-short.135\",\n pages = \"1065--1072\"\n}", "pdf": "https://aclanthology.org/2021.acl-short.135.pdf", "site": "https://aclanthology.org/2021.acl-short.135/", "pdf_size": 470594, "gs_citation": 299, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=11579730523115775369&as_sdt=40005&sciodt=0,10&hl=en", "gs_version_total": 6, "aff": "Carnegie Mellon University; Carnegie Mellon University", "aff_domain": "cs.cmu.edu;cs.cmu.edu", "email": "cs.cmu.edu;cs.cmu.edu", "github": "https://github.com/yixinL7/SimCLS", "project": "https://github.com/yixinL7/SimCLS", "author_num": 2, "aff_unique_index": "0;0", "aff_unique_norm": "Carnegie Mellon University", "aff_unique_dep": "", "aff_unique_url": "https://www.cmu.edu", "aff_unique_abbr": "CMU", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0;0", "aff_country_unique": "United States" }, { "id": "2021.acl-long.54", "title": "SocAoG: Incremental Graph Parsing for Social Relation Inference in Dialogues", "track": "main", "status": "Long", "award": false, "abstract": "Inferring social relations from dialogues is vital for building emotionally intelligent robots to interpret human language better and act accordingly. We model the social network as an And-or Graph, named SocAoG, for the consistency of relations among a group and leveraging attributes as inference cues. Moreover, we formulate a sequential structure prediction task, and propose an \ud835\udefc-\ud835\udefd-\ud835\udefe strategy to incrementally parse SocAoG for the dynamic inference upon any incoming utterance: (i) an \ud835\udefc process predicting attributes and relations conditioned on the semantics of dialogues, (ii) a \ud835\udefd process updating the social relations based on related attributes, and (iii) a \ud835\udefe process updating individual\u2019s attributes based on interpersonal social relations. Empirical results on DialogRE and MovieGraph show that our model infers social relations more accurately than the state-of-the-art methods. Moreover, the ablation study shows the three processes complement each other, and the case study demonstrates the dynamic relational inference.", "author": "Liang Qiu; Yuan Liang; Yizhou Zhao; Pan Lu; Baolin Peng; Zhou Yu; Ying Nian Wu; Song-Chun Zhu", "authorids": "/l/liang-qiu/; /y/yuan-liang/; /y/yizhou-zhao/; /p/pan-lu/; /b/baolin-peng/; /z/zhou-yu/; /y/ying-nian-wu/; /s/song-chun-zhu/", "bibtex": "@inproceedings{qiu-etal-2021-socaog,\n title = \"{S}oc{A}o{G}: Incremental Graph Parsing for Social Relation Inference in Dialogues\",\n author = \"Qiu, Liang and\n Liang, Yuan and\n Zhao, Yizhou and\n Lu, Pan and\n Peng, Baolin and\n Yu, Zhou and\n Wu, Ying Nian and\n Zhu, Song-Chun\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.54/\",\n doi = \"10.18653/v1/2021.acl-long.54\",\n pages = \"658--670\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.54.pdf", "site": "https://aclanthology.org/2021.acl-long.54/", "pdf_size": 1038894, "gs_citation": 20, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=12726770769540888950&as_sdt=5,33&sciodt=0,33&hl=en", "gs_version_total": 7, "aff": "UCLA Center for Vision, Cognition, Learning, and Autonomy; University of California, Los Angeles; UCLA Center for Vision, Cognition, Learning, and Autonomy; UCLA Center for Vision, Cognition, Learning, and Autonomy; Microsoft Research, Redmond; University of California, Davis; UCLA Center for Vision, Cognition, Learning, and Autonomy; UCLA Center for Vision, Cognition, Learning, and Autonomy", "aff_domain": "ucla.edu; ; ; ; ; ; ; ", "email": "ucla.edu; ; ; ; ; ; ; ", "github": "https://github.com/Liang-Qiu/SocAoG-dialogues", "project": "", "author_num": 8, "aff_unique_index": "0;0;0;0;1;2;0;0", "aff_unique_norm": "University of California, Los Angeles;Microsoft Research;University of California, Davis", "aff_unique_dep": "Center for Vision, Cognition, Learning, and Autonomy;;", "aff_unique_url": "https://www.ucla.edu;https://www.microsoft.com/en-us/research;https://www.ucdavis.edu", "aff_unique_abbr": "UCLA;MSR;UC Davis", "aff_campus_unique_index": "0;0;0;0;1;2;0;0", "aff_campus_unique": "Los Angeles;Redmond;Davis", "aff_country_unique_index": "0;0;0;0;0;0;0;0", "aff_country_unique": "United States" }, { "id": "2021.acl-long.330", "title": "Societal Biases in Language Generation: Progress and Challenges", "track": "main", "status": "Long", "award": false, "abstract": "Technology for language generation has advanced rapidly, spurred by advancements in pre-training large models on massive amounts of data and the need for intelligent agents to communicate in a natural manner. While techniques can effectively generate fluent text, they can also produce undesirable societal biases that can have a disproportionately negative impact on marginalized populations. Language generation presents unique challenges for biases in terms of direct user interaction and the structure of decoding techniques. To better understand these challenges, we present a survey on societal biases in language generation, focusing on how data and techniques contribute to biases and progress towards reducing biases. Motivated by a lack of studies on biases from decoding techniques, we also conduct experiments to quantify the effects of these techniques. By further discussing general trends and open challenges, we call to attention promising directions for research and the importance of fairness and inclusivity considerations for language generation applications.", "author": "Emily Sheng; Kai-Wei Chang; Prem Natarajan; Nanyun Peng", "authorids": "/e/emily-sheng/; /k/kai-wei-chang/; /p/prem-natarajan/; /n/nanyun-peng/", "bibtex": "@inproceedings{sheng-etal-2021-societal,\n title = \"Societal Biases in Language Generation: Progress and Challenges\",\n author = \"Sheng, Emily and\n Chang, Kai-Wei and\n Natarajan, Prem and\n Peng, Nanyun\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.330/\",\n doi = \"10.18653/v1/2021.acl-long.330\",\n pages = \"4275--4293\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.330.pdf", "site": "https://aclanthology.org/2021.acl-long.330/", "pdf_size": 454590, "gs_citation": 237, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=104484208570032566&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 4, "aff": "Information Sciences Institute, University of Southern California; Computer Science Department, University of California, Los Angeles; Information Sciences Institute, University of Southern California; Information Sciences Institute, University of Southern California + Computer Science Department, University of California, Los Angeles", "aff_domain": "isi.edu;cs.ucla.edu;isi.edu;cs.ucla.edu", "email": "isi.edu;cs.ucla.edu;isi.edu;cs.ucla.edu", "github": "", "project": "", "author_num": 4, "aff_unique_index": "0;1;0;0+1", "aff_unique_norm": "University of Southern California;University of California, Los Angeles", "aff_unique_dep": "Information Sciences Institute;Computer Science Department", "aff_unique_url": "https://www.usc.edu;https://www.ucla.edu", "aff_unique_abbr": "USC;UCLA", "aff_campus_unique_index": "0;0;0;0+0", "aff_campus_unique": "Los Angeles", "aff_country_unique_index": "0;0;0;0+0", "aff_country_unique": "United States" }, { "id": "2021.acl-long.546", "title": "Space Efficient Context Encoding for Non-Task-Oriented Dialogue Generation with Graph Attention Transformer", "track": "main", "status": "Long", "award": false, "abstract": "To improve the coherence and knowledge retrieval capabilities of non-task-oriented dialogue systems, recent Transformer-based models aim to integrate fixed background context. This often comes in the form of knowledge graphs, and the integration is done by creating pseudo utterances through paraphrasing knowledge triples, added into the accumulated dialogue context. However, the context length is fixed in these architectures, which restricts how much background or dialogue context can be kept. In this work, we propose a more concise encoding for background context structured in the form of knowledge graphs, by expressing the graph connections through restrictions on the attention weights. The results of our human evaluation show that this encoding reduces space requirements without negative effects on the precision of reproduction of knowledge and perceived consistency. Further, models trained with our proposed context encoding generate dialogues that are judged to be more comprehensive and interesting.", "author": "Fabian Galetzka; Jewgeni Rose; David Schlangen; Jens Lehmann", "authorids": "/f/fabian-galetzka/; /j/jewgeni-rose/; /d/david-schlangen/; /j/jens-lehmann/", "bibtex": "@inproceedings{galetzka-etal-2021-space,\n title = \"Space Efficient Context Encoding for Non-Task-Oriented Dialogue Generation with Graph Attention Transformer\",\n author = \"Galetzka, Fabian and\n Rose, Jewgeni and\n Schlangen, David and\n Lehmann, Jens\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.546/\",\n doi = \"10.18653/v1/2021.acl-long.546\",\n pages = \"7028--7041\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.546.pdf", "site": "https://aclanthology.org/2021.acl-long.546/", "pdf_size": 938129, "gs_citation": 22, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=686408682615130168&as_sdt=5,33&sciodt=0,33&hl=en", "gs_version_total": 7, "aff": "Volkswagen Innovation Center, Wolfsburg, Germany+Computational Linguistics, University of Potsdam, Germany; Volkswagen Innovation Center, Wolfsburg, Germany+University of Bonn, Germany; Computational Linguistics, University of Potsdam, Germany; University of Bonn, Germany+Fraunhofer IAIS, Dresden, Germany", "aff_domain": "volkswagen.de;volkswagen.de;uni-potsdam.de;iais.fraunhofer.de", "email": "volkswagen.de;volkswagen.de;uni-potsdam.de;iais.fraunhofer.de", "github": "", "project": "", "author_num": 4, "aff_unique_index": "0+1;0+2;1;2+3", "aff_unique_norm": "Volkswagen Group;University of Potsdam;University of Bonn;Fraunhofer Institute for Intelligent Analysis and Information Systems", "aff_unique_dep": "Innovation Center;Computational Linguistics;;", "aff_unique_url": "https://www.volkswagenag.com;https://www.uni-potsdam.de;https://www.uni-bonn.de;https://www.iais.fraunhofer.de/", "aff_unique_abbr": "VW;;UBonn;Fraunhofer IAIS", "aff_campus_unique_index": "0;0;2", "aff_campus_unique": "Wolfsburg;;Dresden", "aff_country_unique_index": "0+0;0+0;0;0+0", "aff_country_unique": "Germany" }, { "id": "2021.acl-long.74", "title": "Span-based Semantic Parsing for Compositional Generalization", "track": "main", "status": "Long", "award": false, "abstract": "Despite the success of sequence-to-sequence (seq2seq) models in semantic parsing, recent work has shown that they fail in compositional generalization, i.e., the ability to generalize to new structures built of components observed during training. In this work, we posit that a span-based parser should lead to better compositional generalization. we propose SpanBasedSP, a parser that predicts a span tree over an input utterance, explicitly encoding how partial programs compose over spans in the input. SpanBasedSP extends Pasupat et al. (2019) to be comparable to seq2seq models by (i) training from programs, without access to gold trees, treating trees as latent variables, (ii) parsing a class of non-projective trees through an extension to standard CKY. On GeoQuery, SCAN and CLOSURE datasets, SpanBasedSP performs similarly to strong seq2seq baselines on random splits, but dramatically improves performance compared to baselines on splits that require compositional generalization: from 61.0 \u2192 88.9 average accuracy.", "author": "Jonathan Herzig; Jonathan Berant", "authorids": "/j/jonathan-herzig/; /j/jonathan-berant/", "bibtex": "@inproceedings{herzig-berant-2021-span,\n title = \"Span-based Semantic Parsing for Compositional Generalization\",\n author = \"Herzig, Jonathan and\n Berant, Jonathan\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.74/\",\n doi = \"10.18653/v1/2021.acl-long.74\",\n pages = \"908--921\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.74.pdf", "site": "https://aclanthology.org/2021.acl-long.74/", "pdf_size": 463325, "gs_citation": 114, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=1870887887523616081&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 5, "aff": "Blavatnik School of Computer Science, Tel-Aviv University + Allen Institute for Arti\ufb01cial Intelligence; Blavatnik School of Computer Science, Tel-Aviv University + Allen Institute for Arti\ufb01cial Intelligence", "aff_domain": "cs.tau.ac.il;cs.tau.ac.il", "email": "cs.tau.ac.il;cs.tau.ac.il", "github": "", "project": "", "author_num": 2, "aff_unique_index": "0+1;0+1", "aff_unique_norm": "Tel-Aviv University;Allen Institute for Artificial Intelligence", "aff_unique_dep": "Blavatnik School of Computer Science;Artificial Intelligence", "aff_unique_url": "https://www.tau.ac.il;https://allenai.org", "aff_unique_abbr": "TAU;AI2", "aff_campus_unique_index": "0;0", "aff_campus_unique": "Tel-Aviv;", "aff_country_unique_index": "0+1;0+1", "aff_country_unique": "Israel;United States" }, { "id": "2021.acl-long.558", "title": "SpanNER: Named Entity Re-/Recognition as Span Prediction", "track": "main", "status": "Long", "award": false, "abstract": "Recent years have seen the paradigm shift of Named Entity Recognition (NER) systems from sequence labeling to span prediction. Despite its preliminary effectiveness, the span prediction model\u2019s architectural bias has not been fully understood. In this paper, we first investigate the strengths and weaknesses when the span prediction model is used for named entity recognition compared with the sequence labeling framework and how to further improve it, which motivates us to make complementary advantages of systems based on different paradigms. We then reveal that span prediction, simultaneously, can serve as a system combiner to re-recognize named entities from different systems\u2019 outputs. We experimentally implement 154 systems on 11 datasets, covering three languages, comprehensive results show the effectiveness of span prediction models that both serve as base NER systems and system combiners. We make all codes and datasets available: https://github.com/neulab/spanner, as well as an online system demo: http://spanner.sh. Our model also has been deployed into the ExplainaBoard platform, which allows users to flexibly perform a system combination of top-scoring systems in an interactive way: http://explainaboard.nlpedia.ai/leaderboard/task-ner/.", "author": "Jinlan Fu; Xuanjing Huang; Pengfei Liu", "authorids": "/j/jinlan-fu/; /x/xuan-jing-huang/; /p/pengfei-liu/", "bibtex": "@inproceedings{fu-etal-2021-spanner,\n title = \"{S}pan{NER}: Named Entity Re-/Recognition as Span Prediction\",\n author = \"Fu, Jinlan and\n Huang, Xuanjing and\n Liu, Pengfei\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.558/\",\n doi = \"10.18653/v1/2021.acl-long.558\",\n pages = \"7183--7195\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.558.pdf", "site": "https://aclanthology.org/2021.acl-long.558/", "pdf_size": 4181082, "gs_citation": 116, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=12116579677300176525&as_sdt=5,31&sciodt=0,31&hl=en", "gs_version_total": 5, "aff": "Fudan University; Fudan University; Carnegie Mellon University", "aff_domain": "fudan.edu.cn;fudan.edu.cn;cs.cmu.edu", "email": "fudan.edu.cn;fudan.edu.cn;cs.cmu.edu", "github": "https://github.com/neulab/spanner", "project": "http://spanner.sh", "author_num": 3, "aff_unique_index": "0;0;1", "aff_unique_norm": "Fudan University;Carnegie Mellon University", "aff_unique_dep": ";", "aff_unique_url": "https://www.fudan.edu.cn;https://www.cmu.edu", "aff_unique_abbr": "Fudan;CMU", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0;0;1", "aff_country_unique": "China;United States" }, { "id": "2021.acl-long.204", "title": "Stacked Acoustic-and-Textual Encoding: Integrating the Pre-trained Models into Speech Translation Encoders", "track": "main", "status": "Long", "award": false, "abstract": "Encoder pre-training is promising in end-to-end Speech Translation (ST), given the fact that speech-to-translation data is scarce. But ST encoders are not simple instances of Automatic Speech Recognition (ASR) or Machine Translation (MT) encoders. For example, we find that ASR encoders lack the global context representation, which is necessary for translation, whereas MT encoders are not designed to deal with long but locally attentive acoustic sequences. In this work, we propose a Stacked Acoustic-and-Textual Encoding (SATE) method for speech translation. Our encoder begins with processing the acoustic sequence as usual, but later behaves more like an MT encoder for a global representation of the input sequence. In this way, it is straightforward to incorporate the pre-trained models into the system. Also, we develop an adaptor module to alleviate the representation inconsistency between the pre-trained ASR encoder and MT encoder, and develop a multi-teacher knowledge distillation method to preserve the pre-training knowledge. Experimental results on the LibriSpeech En-Fr and MuST-C En-De ST tasks show that our method achieves state-of-the-art BLEU scores of 18.3 and 25.2. To our knowledge, we are the first to develop an end-to-end ST system that achieves comparable or even better BLEU performance than the cascaded ST counterpart when large-scale ASR and MT data is available.", "author": "Chen Xu; Bojie Hu; Yanyang Li; Yuhao Zhang; Shen Huang; Qi Ju; Tong Xiao; Jingbo Zhu", "authorids": "/c/chen-xu/; /b/bojie-hu/; /y/yanyang-li/; /y/yuhao-zhang/; /s/shen-huang/; /q/qi-ju/; /t/tong-xiao/; /j/jingbo-zhu/", "bibtex": "@inproceedings{xu-etal-2021-stacked,\n title = \"Stacked Acoustic-and-Textual Encoding: Integrating the Pre-trained Models into Speech Translation Encoders\",\n author = \"Xu, Chen and\n Hu, Bojie and\n Li, Yanyang and\n Zhang, Yuhao and\n Huang, Shen and\n Ju, Qi and\n Xiao, Tong and\n Zhu, Jingbo\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.204/\",\n doi = \"10.18653/v1/2021.acl-long.204\",\n pages = \"2619--2630\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.204.pdf", "site": "https://aclanthology.org/2021.acl-long.204/", "pdf_size": 349992, "gs_citation": 74, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=15239681455977124254&as_sdt=5,33&sciodt=0,33&hl=en", "gs_version_total": 5, "aff": "NLP Lab, School of Computer Science and Engineering, Northeastern University, Shenyang, China; Tencent Minority-Mandarin Translation, Beijing, China; The Chinese University of Hong Kong, Hong Kong, China; NLP Lab, School of Computer Science and Engineering, Northeastern University, Shenyang, China; Tencent Minority-Mandarin Translation, Beijing, China; Tencent Minority-Mandarin Translation, Beijing, China; NLP Lab, School of Computer Science and Engineering, Northeastern University, Shenyang, China+NiuTrans Research, Shenyang, China; NLP Lab, School of Computer Science and Engineering, Northeastern University, Shenyang, China+NiuTrans Research, Shenyang, China", "aff_domain": "outlook.com;tencent.com;outlook.com;outlook.com;tencent.com;tencent.com;mail.neu.edu.cn;mail.neu.edu.cn", "email": "outlook.com;tencent.com;outlook.com;outlook.com;tencent.com;tencent.com;mail.neu.edu.cn;mail.neu.edu.cn", "github": "https://github.com/xuchenneu/SATE", "project": "", "author_num": 8, "aff_unique_index": "0;1;2;0;1;1;0+3;0+3", "aff_unique_norm": "Northeastern University;Tencent;The Chinese University of Hong Kong;NiuTrans Research", "aff_unique_dep": "School of Computer Science and Engineering;Minority-Mandarin Translation;;", "aff_unique_url": "http://www.neu.edu.cn/;https://www.tencent.com;https://www.cuhk.edu.hk;", "aff_unique_abbr": "NEU;Tencent;CUHK;", "aff_campus_unique_index": "0;1;2;0;1;1;0;0", "aff_campus_unique": "Shenyang;Beijing;Hong Kong;", "aff_country_unique_index": "0;0;0;0;0;0;0+0;0+0", "aff_country_unique": "China" }, { "id": "2021.acl-long.127", "title": "Stance Detection in COVID-19 Tweets", "track": "main", "status": "Long", "award": false, "abstract": "The prevalence of the COVID-19 pandemic in day-to-day life has yielded large amounts of stance detection data on social media sites, as users turn to social media to share their views regarding various issues related to the pandemic, e.g. stay at home mandates and wearing face masks when out in public. We set out to make use of this data by collecting the stance expressed by Twitter users, with respect to topics revolving around the pandemic. We annotate a new stance detection dataset, called COVID-19-Stance. Using this newly annotated dataset, we train several established stance detection models to ascertain a baseline performance for this specific task. To further improve the performance, we employ self-training and domain adaptation approaches to take advantage of large amounts of unlabeled data and existing stance detection datasets. The dataset, code, and other resources are available on GitHub.", "author": "Kyle Glandt; Sarthak Khanal; Yingjie Li; Doina Caragea; Cornelia Caragea", "authorids": "/k/kyle-glandt/; /s/sarthak-khanal/; /y/yingjie-li/; /d/doina-caragea/; /c/cornelia-caragea/", "bibtex": "@inproceedings{glandt-etal-2021-stance,\n title = \"Stance Detection in {COVID}-19 Tweets\",\n author = \"Glandt, Kyle and\n Khanal, Sarthak and\n Li, Yingjie and\n Caragea, Doina and\n Caragea, Cornelia\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.127/\",\n doi = \"10.18653/v1/2021.acl-long.127\",\n pages = \"1596--1611\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.127.pdf", "site": "https://aclanthology.org/2021.acl-long.127/", "pdf_size": 310652, "gs_citation": 151, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=14222325664941955470&as_sdt=40005&sciodt=0,10&hl=en", "gs_version_total": 5, "aff": "Computer Science, Kansas State University; Computer Science, Kansas State University; Computer Science, University of Illinois at Chicago; Computer Science, Kansas State University; Computer Science, University of Illinois at Chicago", "aff_domain": "ksu.edu;ksu.edu;uic.edu;ksu.edu;uic.edu", "email": "ksu.edu;ksu.edu;uic.edu;ksu.edu;uic.edu", "github": "https://github.com/kglandt/stance-detection-in-covid-19-tweets", "project": "", "author_num": 5, "aff_unique_index": "0;0;1;0;1", "aff_unique_norm": "Kansas State University;University of Illinois at Chicago", "aff_unique_dep": "Computer Science;Computer Science", "aff_unique_url": "https://www.k-state.edu;https://www.uic.edu", "aff_unique_abbr": "K-State;UIC", "aff_campus_unique_index": "1;1", "aff_campus_unique": ";Chicago", "aff_country_unique_index": "0;0;0;0;0", "aff_country_unique": "United States" }, { "id": "2021.acl-long.375", "title": "StereoRel: Relational Triple Extraction from a Stereoscopic Perspective", "track": "main", "status": "Long", "award": false, "abstract": "Relational triple extraction is critical to understanding massive text corpora and constructing large-scale knowledge graph, which has attracted increasing research interest. However, existing studies still face some challenging issues, including information loss, error propagation and ignoring the interaction between entity and relation. To intuitively explore the above issues and address them, in this paper, we provide a revealing insight into relational triple extraction from a stereoscopic perspective, which rationalizes the occurrence of these issues and exposes the shortcomings of existing methods. Further, a novel model is proposed for relational triple extraction, which maps relational triples to a three-dimension (3-D) space and leverages three decoders to extract them, aimed at simultaneously handling the above issues. A series of experiments are conducted on five public datasets, demonstrating that the proposed model outperforms the recent advanced baselines.", "author": "Xuetao Tian; Liping Jing; Lu He; Feng Liu", "authorids": "/x/xuetao-tian/; /l/liping-jing/; /l/lu-he/; /f/feng-liu/", "bibtex": "@inproceedings{tian-etal-2021-stereorel,\n title = \"{S}tereo{R}el: Relational Triple Extraction from a Stereoscopic Perspective\",\n author = \"Tian, Xuetao and\n Jing, Liping and\n He, Lu and\n Liu, Feng\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.375/\",\n doi = \"10.18653/v1/2021.acl-long.375\",\n pages = \"4851--4861\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.375.pdf", "site": "https://aclanthology.org/2021.acl-long.375/", "pdf_size": 1156163, "gs_citation": 21, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=5702345898294649115&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 2, "aff": "Technology Beijing Key Lab of Traffic Data Analysis and Mining; Technology Beijing Key Lab of Traffic Data Analysis and Mining; Engineering Research Center of Network Management Technology for High Speed Railway; Engineering Research Center of Network Management Technology for High Speed Railway", "aff_domain": "bjtu.edu.cn;bjtu.edu.cn;bjtu.edu.cn;bjtu.edu.cn", "email": "bjtu.edu.cn;bjtu.edu.cn;bjtu.edu.cn;bjtu.edu.cn", "github": "", "project": "", "author_num": 4, "aff_unique_index": "0;0;1;1", "aff_unique_norm": "Technology Beijing;Engineering Research Center of Network Management Technology for High Speed Railway", "aff_unique_dep": "Key Lab of Traffic Data Analysis and Mining;", "aff_unique_url": ";", "aff_unique_abbr": ";", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0;0;0;0", "aff_country_unique": "China" }, { "id": "2021.acl-long.416", "title": "StereoSet: Measuring stereotypical bias in pretrained language models", "track": "main", "status": "Long", "award": false, "abstract": "A stereotype is an over-generalized belief about a particular group of people, e.g., Asians are good at math or African Americans are athletic. Such beliefs (biases) are known to hurt target groups. Since pretrained language models are trained on large real-world data, they are known to capture stereotypical biases. It is important to quantify to what extent these biases are present in them. Although this is a rapidly growing area of research, existing literature lacks in two important aspects: 1) they mainly evaluate bias of pretrained language models on a small set of artificial sentences, even though these models are trained on natural data 2) current evaluations focus on measuring bias without considering the language modeling ability of a model, which could lead to misleading trust on a model even if it is a poor language model. We address both these problems. We present StereoSet, a large-scale natural English dataset to measure stereotypical biases in four domains: gender, profession, race, and religion. We contrast both stereotypical bias and language modeling ability of popular models like BERT, GPT-2, RoBERTa, and XLnet. We show that these models exhibit strong stereotypical biases. Our data and code are available at https://stereoset.mit.edu.", "author": "Moin Nadeem; Anna Bethke; Siva Reddy", "authorids": "/m/moin-nadeem/; /a/anna-bethke/; /s/siva-reddy/", "bibtex": "@inproceedings{nadeem-etal-2021-stereoset,\n title = \"{S}tereo{S}et: Measuring stereotypical bias in pretrained language models\",\n author = \"Nadeem, Moin and\n Bethke, Anna and\n Reddy, Siva\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.416/\",\n doi = \"10.18653/v1/2021.acl-long.416\",\n pages = \"5356--5371\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.416.pdf", "site": "https://aclanthology.org/2021.acl-long.416/", "pdf_size": 733424, "gs_citation": 1108, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=18122742874816753810&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 7, "aff": "Massachusetts Institute of Technology, Cambridge MA, USA; Facebook, Menlo Park CA, USA; Facebook CIFAR AI Chair, Mila + McGill University, Montreal, QC, Canada", "aff_domain": "mit.edu;intel.com;mila.quebec", "email": "mit.edu;intel.com;mila.quebec", "github": "", "project": "https://stereoset.mit.edu", "author_num": 3, "aff_unique_index": "0;1;2+3", "aff_unique_norm": "Massachusetts Institute of Technology;Facebook;Mila;McGill University", "aff_unique_dep": ";;AI;", "aff_unique_url": "https://web.mit.edu;https://www.facebook.com;https://mila.quebec;https://www.mcgill.ca", "aff_unique_abbr": "MIT;FB;Mila;McGill", "aff_campus_unique_index": "0;1;3", "aff_campus_unique": "Cambridge;Menlo Park;;Montreal", "aff_country_unique_index": "0;0;1+1", "aff_country_unique": "United States;Canada" }, { "id": "2021.acl-long.81", "title": "Stereotyping Norwegian Salmon: An Inventory of Pitfalls in Fairness Benchmark Datasets", "track": "main", "status": "Long", "award": false, "abstract": "Auditing NLP systems for computational harms like surfacing stereotypes is an elusive goal. Several recent efforts have focused on benchmark datasets consisting of pairs of contrastive sentences, which are often accompanied by metrics that aggregate an NLP system\u2019s behavior on these pairs into measurements of harms. We examine four such benchmarks constructed for two NLP tasks: language modeling and coreference resolution. We apply a measurement modeling lens\u2014originating from the social sciences\u2014to inventory a range of pitfalls that threaten these benchmarks\u2019 validity as measurement models for stereotyping. We find that these benchmarks frequently lack clear articulations of what is being measured, and we highlight a range of ambiguities and unstated assumptions that affect how these benchmarks conceptualize and operationalize stereotyping.", "author": "Su Lin Blodgett; Gilsinia Lopez; Alexandra Olteanu; Robert Sim; Hanna Wallach", "authorids": "/s/su-lin-blodgett/; /g/gilsinia-lopez/; /a/alexandra-olteanu/; /r/robert-sim/; /h/hanna-wallach/", "bibtex": "@inproceedings{blodgett-etal-2021-stereotyping,\n title = \"Stereotyping {N}orwegian Salmon: An Inventory of Pitfalls in Fairness Benchmark Datasets\",\n author = \"Blodgett, Su Lin and\n Lopez, Gilsinia and\n Olteanu, Alexandra and\n Sim, Robert and\n Wallach, Hanna\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.81/\",\n doi = \"10.18653/v1/2021.acl-long.81\",\n pages = \"1004--1015\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.81.pdf", "site": "https://aclanthology.org/2021.acl-long.81/", "pdf_size": 235823, "gs_citation": 335, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=18019537394373736184&as_sdt=5,47&sciodt=0,47&hl=en", "gs_version_total": 4, "aff": "Microsoft Research; Microsoft Research; Microsoft Research; Microsoft Research; Microsoft Research", "aff_domain": "microsoft.com;microsoft.com;microsoft.com;microsoft.com;microsoft.com", "email": "microsoft.com;microsoft.com;microsoft.com;microsoft.com;microsoft.com", "github": "", "project": "", "author_num": 5, "aff_unique_index": "0;0;0;0;0", "aff_unique_norm": "Microsoft Corporation", "aff_unique_dep": "Microsoft Research", "aff_unique_url": "https://www.microsoft.com/en-us/research", "aff_unique_abbr": "MSR", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0;0;0;0;0", "aff_country_unique": "United States" }, { "id": "2021.acl-long.559", "title": "StructFormer: Joint Unsupervised Induction of Dependency and Constituency Structure from Masked Language Modeling", "track": "main", "status": "Long", "award": false, "abstract": "There are two major classes of natural language grammars \u2014 the dependency grammar that models one-to-one correspondences between words and the constituency grammar that models the assembly of one or several corresponded words. While previous unsupervised parsing methods mostly focus on only inducing one class of grammars, we introduce a novel model, StructFormer, that can induce dependency and constituency structure at the same time. To achieve this, we propose a new parsing framework that can jointly generate a constituency tree and dependency graph. Then we integrate the induced dependency relations into the transformer, in a differentiable manner, through a novel dependency-constrained self-attention mechanism. Experimental results show that our model can achieve strong results on unsupervised constituency parsing, unsupervised dependency parsing, and masked language modeling at the same time.", "author": "Yikang Shen; Yi Tay; Che Zheng; Dara Bahri; Donald Metzler; Aaron Courville", "authorids": "/y/yikang-shen/; /y/yi-tay/; /c/che-zheng/; /d/dara-bahri/; /d/donald-metzler/; /a/aaron-courville/", "bibtex": "@inproceedings{shen-etal-2021-structformer,\n title = \"{S}truct{F}ormer: Joint Unsupervised Induction of Dependency and Constituency Structure from Masked Language Modeling\",\n author = \"Shen, Yikang and\n Tay, Yi and\n Zheng, Che and\n Bahri, Dara and\n Metzler, Donald and\n Courville, Aaron\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.559/\",\n doi = \"10.18653/v1/2021.acl-long.559\",\n pages = \"7196--7209\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.559.pdf", "site": "https://aclanthology.org/2021.acl-long.559/", "pdf_size": 1092705, "gs_citation": 50, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=4554141392061073199&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 8, "aff": "Mila/Universit \u00b4e de Montr \u00b4eal; Google Research; Google Research; Google Research; Google Research; Mila/Universit \u00b4e de Montr \u00b4eal", "aff_domain": "gmail.ca; ; ; ; ; ", "email": "gmail.ca; ; ; ; ; ", "github": "", "project": "", "author_num": 6, "aff_unique_index": "0;1;1;1;1;0", "aff_unique_norm": "Universit\u00e9 de Montr\u00e9al;Google", "aff_unique_dep": "Mila;Google Research", "aff_unique_url": "https://www.umontreal.ca;https://research.google", "aff_unique_abbr": "UM;Google Research", "aff_campus_unique_index": "0;1;1;1;1;0", "aff_campus_unique": "Montr\u00e9al;Mountain View", "aff_country_unique_index": "0;1;1;1;1;0", "aff_country_unique": "Canada;United States" }, { "id": "2021.acl-long.289", "title": "Structural Guidance for Transformer Language Models", "track": "main", "status": "Long", "award": false, "abstract": "Transformer-based language models pre-trained on large amounts of text data have proven remarkably successful in learning generic transferable linguistic representations. Here we study whether structural guidance leads to more human-like systematic linguistic generalization in Transformer language models without resorting to pre-training on very large amounts of data. We explore two general ideas. The \u201cGenerative Parsing\u201d idea jointly models the incremental parse and word sequence as part of the same sequence modeling task. The \u201cStructural Scaffold\u201d idea guides the language model\u2019s representation via additional structure loss that separately predicts the incremental constituency parse. We train the proposed models along with a vanilla Transformer language model baseline on a 14 million-token and a 46 million-token subset of the BLLIP dataset, and evaluate models\u2019 syntactic generalization performances on SG Test Suites and sized BLiMP. Experiment results across two benchmarks suggest converging evidence that generative structural supervisions can induce more robust and humanlike linguistic generalization in Transformer language models without the need for data intensive pre-training.", "author": "Peng Qian; Tahira Naseem; Roger Levy; Ram\u00f3n Fernandez Astudillo", "authorids": "/p/peng-qian/; /t/tahira-naseem/; /r/roger-levy/; /r/ramon-fernandez-astudillo/", "bibtex": "@inproceedings{qian-etal-2021-structural,\n title = \"Structural Guidance for Transformer Language Models\",\n author = \"Qian, Peng and\n Naseem, Tahira and\n Levy, Roger and\n Fernandez Astudillo, Ram{\\'o}n\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.289/\",\n doi = \"10.18653/v1/2021.acl-long.289\",\n pages = \"3735--3745\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.289.pdf", "site": "https://aclanthology.org/2021.acl-long.289/", "pdf_size": 421240, "gs_citation": 42, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=8187944402639941970&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 8, "aff": "Department of Brain and Cognitive Sciences, MIT; IBM Research; Department of Brain and Cognitive Sciences, MIT; IBM Research", "aff_domain": "mit.edu;us.ibm.com;mit.edu;ibm.com", "email": "mit.edu;us.ibm.com;mit.edu;ibm.com", "github": "", "project": "", "author_num": 4, "aff_unique_index": "0;1;0;1", "aff_unique_norm": "Massachusetts Institute of Technology;IBM", "aff_unique_dep": "Department of Brain and Cognitive Sciences;IBM Research", "aff_unique_url": "https://www.mit.edu;https://www.ibm.com/research", "aff_unique_abbr": "MIT;IBM", "aff_campus_unique_index": "0;0", "aff_campus_unique": "Cambridge;", "aff_country_unique_index": "0;0;0;0", "aff_country_unique": "United States" }, { "id": "2021.acl-long.46", "title": "Structural Knowledge Distillation: Tractably Distilling Information for Structured Predictor", "track": "main", "status": "Long", "award": false, "abstract": "Knowledge distillation is a critical technique to transfer knowledge between models, typically from a large model (the teacher) to a more fine-grained one (the student). The objective function of knowledge distillation is typically the cross-entropy between the teacher and the student\u2019s output distributions. However, for structured prediction problems, the output space is exponential in size; therefore, the cross-entropy objective becomes intractable to compute and optimize directly. In this paper, we derive a factorized form of the knowledge distillation objective for structured prediction, which is tractable for many typical choices of the teacher and student models. In particular, we show the tractability and empirical effectiveness of structural knowledge distillation between sequence labeling and dependency parsing models under four different scenarios: 1) the teacher and student share the same factorization form of the output structure scoring function; 2) the student factorization produces more fine-grained substructures than the teacher factorization; 3) the teacher factorization produces more fine-grained substructures than the student factorization; 4) the factorization forms from the teacher and the student are incompatible.", "author": "Xinyu Wang; Yong Jiang; Zhaohui Yan; Zixia Jia; Nguyen Bach; Tao Wang; Zhongqiang Huang; Fei Huang; Kewei Tu", "authorids": "/x/xinyu-wang/; /y/yong-jiang/; /z/zhaohui-yan/; /z/zixia-jia/; /n/nguyen-bach/; /t/tao-wang/; /z/zhongqiang-huang/; /f/fei-huang/; /k/kewei-tu/", "bibtex": "@inproceedings{wang-etal-2021-structural,\n title = \"Structural Knowledge Distillation: Tractably Distilling Information for Structured Predictor\",\n author = \"Wang, Xinyu and\n Jiang, Yong and\n Yan, Zhaohui and\n Jia, Zixia and\n Bach, Nguyen and\n Wang, Tao and\n Huang, Zhongqiang and\n Huang, Fei and\n Tu, Kewei\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.46/\",\n doi = \"10.18653/v1/2021.acl-long.46\",\n pages = \"550--564\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.46.pdf", "site": "https://aclanthology.org/2021.acl-long.46/", "pdf_size": 392414, "gs_citation": 11, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=16152028006937260634&as_sdt=5,33&sciodt=0,33&hl=en", "gs_version_total": 6, "aff": "School of Information Science and Technology, ShanghaiTech University + Shanghai Engineering Research Center of Intelligent Vision and Imaging + Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences + University of Chinese Academy of Sciences; DAMO Academy, Alibaba Group; School of Information Science and Technology, ShanghaiTech University + Shanghai Engineering Research Center of Intelligent Vision and Imaging + Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences + University of Chinese Academy of Sciences; School of Information Science and Technology, ShanghaiTech University + Shanghai Engineering Research Center of Intelligent Vision and Imaging + Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences + University of Chinese Academy of Sciences; DAMO Academy, Alibaba Group; DAMO Academy, Alibaba Group; DAMO Academy, Alibaba Group; DAMO Academy, Alibaba Group; School of Information Science and Technology, ShanghaiTech University + Shanghai Engineering Research Center of Intelligent Vision and Imaging + Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences + University of Chinese Academy of Sciences", "aff_domain": "shanghaitech.edu.cn;alibaba-inc.com;shanghaitech.edu.cn;shanghaitech.edu.cn;alibaba-inc.com;alibaba-inc.com;alibaba-inc.com;alibaba-inc.com;shanghaitech.edu.cn", "email": "shanghaitech.edu.cn;alibaba-inc.com;shanghaitech.edu.cn;shanghaitech.edu.cn;alibaba-inc.com;alibaba-inc.com;alibaba-inc.com;alibaba-inc.com;shanghaitech.edu.cn", "github": "https://github.com/Alibaba-NLP/StructuralKD", "project": "", "author_num": 9, "aff_unique_index": "0+1+2+3;4;0+1+2+3;0+1+2+3;4;4;4;4;0+1+2+3", "aff_unique_norm": "ShanghaiTech University;Shanghai Engineering Research Center of Intelligent Vision and Imaging;Shanghai Institute of Microsystem and Information Technology;University of Chinese Academy of Sciences;Alibaba Group", "aff_unique_dep": "School of Information Science and Technology;;;;DAMO Academy", "aff_unique_url": "https://www.shanghaitech.edu.cn;;http://www.sim.cas.cn;http://www.ucas.ac.cn;https://www.alibaba-group.com", "aff_unique_abbr": "ShanghaiTech;;SIM;UCAS;Alibaba", "aff_campus_unique_index": "0+0;0+0;0+0;0+0", "aff_campus_unique": "Shanghai;", "aff_country_unique_index": "0+0+0+0;0;0+0+0+0;0+0+0+0;0;0;0;0;0+0+0+0", "aff_country_unique": "China" }, { "id": "2021.acl-long.399", "title": "Structural Pre-training for Dialogue Comprehension", "track": "main", "status": "Long", "award": false, "abstract": "Pre-trained language models (PrLMs) have demonstrated superior performance due to their strong ability to learn universal language representations from self-supervised pre-training. However, even with the help of the powerful PrLMs, it is still challenging to effectively capture task-related knowledge from dialogue texts which are enriched by correlations among speaker-aware utterances. In this work, we present SPIDER, Structural Pre-traIned DialoguE Reader, to capture dialogue exclusive features. To simulate the dialogue-like features, we propose two training objectives in addition to the original LM objectives: 1) utterance order restoration, which predicts the order of the permuted utterances in dialogue context; 2) sentence backbone regularization, which regularizes the model to improve the factual correctness of summarized subject-verb-object triplets. Experimental results on widely used dialogue benchmarks verify the effectiveness of the newly introduced self-supervised tasks.", "author": "Zhuosheng Zhang; Hai Zhao", "authorids": "/z/zhuosheng-zhang/; /h/hai-zhao/", "bibtex": "@inproceedings{zhang-zhao-2021-structural,\n title = \"Structural Pre-training for Dialogue Comprehension\",\n author = \"Zhang, Zhuosheng and\n Zhao, Hai\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.399/\",\n doi = \"10.18653/v1/2021.acl-long.399\",\n pages = \"5134--5145\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.399.pdf", "site": "https://aclanthology.org/2021.acl-long.399/", "pdf_size": 1428405, "gs_citation": 33, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=268888521145725890&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 7, "aff": "Department of Computer Science and Engineering, Shanghai Jiao Tong University+Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering, Shanghai Jiao Tong University+MoE Key Lab of Arti\ufb01cial Intelligence, AI Institute, Shanghai Jiao Tong University; Department of Computer Science and Engineering, Shanghai Jiao Tong University+Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering, Shanghai Jiao Tong University+MoE Key Lab of Arti\ufb01cial Intelligence, AI Institute, Shanghai Jiao Tong University", "aff_domain": "sjtu.edu.cn;cs.sjtu.edu.cn", "email": "sjtu.edu.cn;cs.sjtu.edu.cn", "github": "", "project": "", "author_num": 2, "aff_unique_index": "0+0+0;0+0+0", "aff_unique_norm": "Shanghai Jiao Tong University", "aff_unique_dep": "Department of Computer Science and Engineering", "aff_unique_url": "https://www.sjtu.edu.cn", "aff_unique_abbr": "SJTU", "aff_campus_unique_index": "1+1;1+1", "aff_campus_unique": ";Shanghai", "aff_country_unique_index": "0+0+0;0+0+0", "aff_country_unique": "China" }, { "id": "2021.acl-long.493", "title": "StructuralLM: Structural Pre-training for Form Understanding", "track": "main", "status": "Long", "award": false, "abstract": "Large pre-trained language models achieve state-of-the-art results when fine-tuned on downstream NLP tasks. However, they almost exclusively focus on text-only representation, while neglecting cell-level layout information that is important for form image understanding. In this paper, we propose a new pre-training approach, StructuralLM, to jointly leverage cell and layout information from scanned documents. Specifically, we pre-train StructuralLM with two new designs to make the most of the interactions of cell and layout information: 1) each cell as a semantic unit; 2) classification of cell positions. The pre-trained StructuralLM achieves new state-of-the-art results in different types of downstream tasks, including form understanding (from 78.95 to 85.14), document visual question answering (from 72.59 to 83.94) and document image classification (from 94.43 to 96.08).", "author": "Chenliang Li; Bin Bi; Ming Yan; Wei Wang; Songfang Huang; Fei Huang; Luo Si", "authorids": "/c/chenliang-li/; /b/bin-bi/; /m/ming-yan/; /w/wei-wang/; /s/songfang-huang/; /f/fei-huang/; /l/luo-si/", "bibtex": "@inproceedings{li-etal-2021-structurallm,\n title = \"{S}tructural{LM}: Structural Pre-training for Form Understanding\",\n author = \"Li, Chenliang and\n Bi, Bin and\n Yan, Ming and\n Wang, Wei and\n Huang, Songfang and\n Huang, Fei and\n Si, Luo\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.493/\",\n doi = \"10.18653/v1/2021.acl-long.493\",\n pages = \"6309--6318\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.493.pdf", "site": "https://aclanthology.org/2021.acl-long.493/", "pdf_size": 1508434, "gs_citation": 134, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=16653480613583453800&as_sdt=5,33&sciodt=0,33&hl=en", "gs_version_total": 4, "aff": "Alibaba Group; Alibaba Group; Alibaba Group; Alibaba Group; Alibaba Group; Alibaba Group; Alibaba Group", "aff_domain": "alibaba-inc.com;alibaba-inc.com;alibaba-inc.com;alibaba-inc.com;alibaba-inc.com;alibaba-inc.com;alibaba-inc.com", "email": "alibaba-inc.com;alibaba-inc.com;alibaba-inc.com;alibaba-inc.com;alibaba-inc.com;alibaba-inc.com;alibaba-inc.com", "github": "", "project": "", "author_num": 7, "aff_unique_index": "0;0;0;0;0;0;0", "aff_unique_norm": "Alibaba Group", "aff_unique_dep": "", "aff_unique_url": "https://www.alibaba.com", "aff_unique_abbr": "Alibaba", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0;0;0;0;0;0;0", "aff_country_unique": "China" }, { "id": "2021.acl-long.263", "title": "Structured Sentiment Analysis as Dependency Graph Parsing", "track": "main", "status": "Long", "award": false, "abstract": "Structured sentiment analysis attempts to extract full opinion tuples from a text, but over time this task has been subdivided into smaller and smaller sub-tasks, e.g., target extraction or targeted polarity classification. We argue that this division has become counterproductive and propose a new unified framework to remedy the situation. We cast the structured sentiment problem as dependency graph parsing, where the nodes are spans of sentiment holders, targets and expressions, and the arcs are the relations between them. We perform experiments on five datasets in four languages (English, Norwegian, Basque, and Catalan) and show that this approach leads to strong improvements over state-of-the-art baselines. Our analysis shows that refining the sentiment graphs with syntactic dependency information further improves results.", "author": "Jeremy Barnes; Robin Kurtz; Stephan Oepen; Lilja \u00d8vrelid; Erik Velldal", "authorids": "/j/jeremy-barnes/; /r/robin-kurtz/; /s/stephan-oepen/; /l/lilja-ovrelid/; /e/erik-velldal/", "bibtex": "@inproceedings{barnes-etal-2021-structured,\n title = \"Structured Sentiment Analysis as Dependency Graph Parsing\",\n author = \"Barnes, Jeremy and\n Kurtz, Robin and\n Oepen, Stephan and\n {\\O}vrelid, Lilja and\n Velldal, Erik\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.263/\",\n doi = \"10.18653/v1/2021.acl-long.263\",\n pages = \"3387--3402\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.263.pdf", "site": "https://aclanthology.org/2021.acl-long.263/", "pdf_size": 493597, "gs_citation": 81, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=7429407186032573735&as_sdt=40005&sciodt=0,10&hl=en", "gs_version_total": 5, "aff": "University of Oslo, Department of Informatics; National Library of Sweden, KBLab; University of Oslo, Department of Informatics; University of Oslo, Department of Informatics; University of Oslo, Department of Informatics", "aff_domain": "ifi.uio.no;kb.se;ifi.uio.no;ifi.uio.no;ifi.uio.no", "email": "ifi.uio.no;kb.se;ifi.uio.no;ifi.uio.no;ifi.uio.no", "github": "", "project": "", "author_num": 5, "aff_unique_index": "0;1;0;0;0", "aff_unique_norm": "University of Oslo;National Library of Sweden", "aff_unique_dep": "Department of Informatics;KBLab", "aff_unique_url": "https://www.uio.no;https://www.kb.se", "aff_unique_abbr": "UiO;KB", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0;1;0;0;0", "aff_country_unique": "Norway;Sweden" }, { "id": "2021.acl-long.51", "title": "Structurizing Misinformation Stories via Rationalizing Fact-Checks", "track": "main", "status": "Long", "award": false, "abstract": "Misinformation has recently become a well-documented matter of public concern. Existing studies on this topic have hitherto adopted a coarse concept of misinformation, which incorporates a broad spectrum of story types ranging from political conspiracies to misinterpreted pranks. This paper aims to structurize these misinformation stories by leveraging fact-check articles. Our intuition is that key phrases in a fact-check article that identify the misinformation type(s) (e.g., doctored images, urban legends) also act as rationales that determine the verdict of the fact-check (e.g., false). We experiment on rationalized models with domain knowledge as weak supervision to extract these phrases as rationales, and then cluster semantically similar rationales to summarize prevalent misinformation types. Using archived fact-checks from Snopes.com, we identify ten types of misinformation stories. We discuss how these types have evolved over the last ten years and compare their prevalence between the 2016/2020 US presidential elections and the H1N1/COVID-19 pandemics.", "author": "Shan Jiang; Christo Wilson", "authorids": "/s/shan-jiang/; /c/christo-wilson/", "bibtex": "@inproceedings{jiang-wilson-2021-structurizing,\n title = \"Structurizing Misinformation Stories via Rationalizing Fact-Checks\",\n author = \"Jiang, Shan and\n Wilson, Christo\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.51/\",\n doi = \"10.18653/v1/2021.acl-long.51\",\n pages = \"617--631\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.51.pdf", "site": "https://aclanthology.org/2021.acl-long.51/", "pdf_size": 894968, "gs_citation": 14, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=11162486671006943476&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 6, "aff": "Northeastern University, USA; Northeastern University, USA", "aff_domain": "ccs.neu.edu;ccs.neu.edu", "email": "ccs.neu.edu;ccs.neu.edu", "github": "", "project": "", "author_num": 2, "aff_unique_index": "0;0", "aff_unique_norm": "Northeastern University", "aff_unique_dep": "", "aff_unique_url": "https://www.northeastern.edu", "aff_unique_abbr": "NEU", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0;0", "aff_country_unique": "United States" }, { "id": "2021.acl-long.185", "title": "Style is NOT a single variable: Case Studies for Cross-Stylistic Language Understanding", "track": "main", "status": "Long", "award": false, "abstract": "Every natural text is written in some style. Style is formed by a complex combination of different stylistic factors, including formality markers, emotions, metaphors, etc. One cannot form a complete understanding of a text without considering these factors. The factors combine and co-vary in complex ways to form styles. Studying the nature of the covarying combinations sheds light on stylistic language in general, sometimes called cross-style language understanding. This paper provides the benchmark corpus (XSLUE) that combines existing datasets and collects a new one for sentence-level cross-style language understanding and evaluation. The benchmark contains text in 15 different styles under the proposed four theoretical groupings: figurative, personal, affective, and interpersonal groups. For valid evaluation, we collect an additional diagnostic set by annotating all 15 styles on the same text. Using XSLUE, we propose three interesting cross-style applications in classification, correlation, and generation. First, our proposed cross-style classifier trained with multiple styles together helps improve overall classification performance against individually-trained style classifiers. Second, our study shows that some styles are highly dependent on each other in human-written text. Finally, we find that combinations of some contradictive styles likely generate stylistically less appropriate text. We believe our benchmark and case studies help explore interesting future directions for cross-style research. The preprocessed datasets and code are publicly available.", "author": "Dongyeop Kang; Eduard Hovy", "authorids": "/d/dongyeop-kang/; /e/eduard-hovy/", "bibtex": "@inproceedings{kang-hovy-2021-style,\n title = \"Style is {NOT} a single variable: Case Studies for Cross-Stylistic Language Understanding\",\n author = \"Kang, Dongyeop and\n Hovy, Eduard\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.185/\",\n doi = \"10.18653/v1/2021.acl-long.185\",\n pages = \"2376--2387\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.185.pdf", "site": "https://aclanthology.org/2021.acl-long.185/", "pdf_size": 727097, "gs_citation": 27, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=13317165444339726220&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 3, "aff": "UC Berkeley+CMU; Carnegie Mellon University", "aff_domain": "berkeley.edu;cs.cmu.edu", "email": "berkeley.edu;cs.cmu.edu", "github": "https://github.com/dykang/xslue", "project": "", "author_num": 2, "aff_unique_index": "0+1;1", "aff_unique_norm": "University of California, Berkeley;Carnegie Mellon University", "aff_unique_dep": ";", "aff_unique_url": "https://www.berkeley.edu;https://www.cmu.edu", "aff_unique_abbr": "UC Berkeley;CMU", "aff_campus_unique_index": "0", "aff_campus_unique": "Berkeley;", "aff_country_unique_index": "0+0;0", "aff_country_unique": "United States" }, { "id": "2021.acl-long.332", "title": "Subsequence Based Deep Active Learning for Named Entity Recognition", "track": "main", "status": "Long", "award": false, "abstract": "Active Learning (AL) has been successfully applied to Deep Learning in order to drastically reduce the amount of data required to achieve high performance. Previous works have shown that lightweight architectures for Named Entity Recognition (NER) can achieve optimal performance with only 25% of the original training data. However, these methods do not exploit the sequential nature of language and the heterogeneity of uncertainty within each instance, requiring the labelling of whole sentences. Additionally, this standard method requires that the annotator has access to the full sentence when labelling. In this work, we overcome these limitations by allowing the AL algorithm to query subsequences within sentences, and propagate their labels to other sentences. We achieve highly efficient results on OntoNotes 5.0, only requiring 13% of the original training data, and CoNLL 2003, requiring only 27%. This is an improvement of 39% and 37% compared to querying full sentences.", "author": "Puria Radmard; Yassir Fathullah; Aldo Lipani", "authorids": "/p/puria-radmard/; /y/yassir-fathullah/; /a/aldo-lipani/", "bibtex": "@inproceedings{radmard-etal-2021-subsequence,\n title = \"Subsequence Based Deep Active Learning for Named Entity Recognition\",\n author = \"Radmard, Puria and\n Fathullah, Yassir and\n Lipani, Aldo\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.332/\",\n doi = \"10.18653/v1/2021.acl-long.332\",\n pages = \"4310--4321\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.332.pdf", "site": "https://aclanthology.org/2021.acl-long.332/", "pdf_size": 677823, "gs_citation": 36, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=12229538847485641265&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 4, "aff": "University College London + Vector AI; University of Cambridge; University College London + Vector AI", "aff_domain": "cam.ac.uk;cam.ac.uk;ucl.ac.uk", "email": "cam.ac.uk;cam.ac.uk;ucl.ac.uk", "github": "https://github.com/puria-radmard/RFL-SBDALNER", "project": "", "author_num": 3, "aff_unique_index": "0+1;2;0+1", "aff_unique_norm": "University College London;Vector AI;University of Cambridge", "aff_unique_dep": ";;", "aff_unique_url": "https://www.ucl.ac.uk;https://www.vector.ai;https://www.cam.ac.uk", "aff_unique_abbr": "UCL;Vector AI;Cambridge", "aff_campus_unique_index": ";1;", "aff_campus_unique": ";Cambridge", "aff_country_unique_index": "0+1;0;0+1", "aff_country_unique": "United Kingdom;United States" }, { "id": "2021.acl-long.510", "title": "Super Tickets in Pre-Trained Language Models: From Model Compression to Improving Generalization", "track": "main", "status": "Long", "award": false, "abstract": "The Lottery Ticket Hypothesis suggests that an over-parametrized network consists of \u201dlottery tickets\u201d, and training a certain collection of them (i.e., a subnetwork) can match the performance of the full model. In this paper, we study such a collection of tickets, which is referred to as \u201dwinning tickets\u201d, in extremely over-parametrized models, e.g., pre-trained language models. We observe that at certain compression ratios, the generalization performance of the winning tickets can not only match but also exceed that of the full model. In particular, we observe a phase transition phenomenon: As the compression ratio increases, generalization performance of the winning tickets first improves then deteriorates after a certain threshold. We refer to the tickets on the threshold as \u201dsuper tickets\u201d. We further show that the phase transition is task and model dependent \u2014 as the model size becomes larger and the training data set becomes smaller, the transition becomes more pronounced. Our experiments on the GLUE benchmark show that the super tickets improve single task fine-tuning by 0.9 points on BERT-base and 1.0 points on BERT-large, in terms of task-average score. We also demonstrate that adaptively sharing the super tickets across tasks benefits multi-task learning.", "author": "Chen Liang; Simiao Zuo; Minshuo Chen; Haoming Jiang; Xiaodong Liu; Pengcheng He; Tuo Zhao; Weizhu Chen", "authorids": "/c/chen-liang/; /s/simiao-zuo/; /m/minshuo-chen/; /h/haoming-jiang/; /x/xiaodong-liu/; /p/pengcheng-he/; /t/tuo-zhao/; /w/weizhu-chen/", "bibtex": "@inproceedings{liang-etal-2021-super,\n title = \"Super Tickets in Pre-Trained Language Models: From Model Compression to Improving Generalization\",\n author = \"Liang, Chen and\n Zuo, Simiao and\n Chen, Minshuo and\n Jiang, Haoming and\n Liu, Xiaodong and\n He, Pengcheng and\n Zhao, Tuo and\n Chen, Weizhu\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.510/\",\n doi = \"10.18653/v1/2021.acl-long.510\",\n pages = \"6524--6538\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.510.pdf", "site": "https://aclanthology.org/2021.acl-long.510/", "pdf_size": 1830698, "gs_citation": 66, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=358586170074938252&as_sdt=8000005&sciodt=0,19&hl=en", "gs_version_total": 7, "aff": "Georgia Institute of Technology; Georgia Institute of Technology; Georgia Institute of Technology; Georgia Institute of Technology; Microsoft Research; Microsoft Azure AI; Georgia Institute of Technology; Microsoft Azure AI", "aff_domain": "gatech.edu;gatech.edu;gatech.edu;gatech.edu;microsoft.com;microsoft.com;gatech.edu;microsoft.com", "email": "gatech.edu;gatech.edu;gatech.edu;gatech.edu;microsoft.com;microsoft.com;gatech.edu;microsoft.com", "github": "https://github.com/cliang1453/super-structured-lottery-tickets", "project": "", "author_num": 8, "aff_unique_index": "0;0;0;0;1;2;0;2", "aff_unique_norm": "Georgia Institute of Technology;Microsoft Corporation;Microsoft", "aff_unique_dep": ";Microsoft Research;Azure AI", "aff_unique_url": "https://www.gatech.edu;https://www.microsoft.com/en-us/research;https://azure.microsoft.com/en-us/ai", "aff_unique_abbr": "Georgia Tech;MSR;Microsoft Azure AI", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0;0;0;0;0;0;0;0", "aff_country_unique": "United States" }, { "id": "2021.acl-long.279", "title": "Superbizarre Is Not Superb: Derivational Morphology Improves BERT\u2019s Interpretation of Complex Words", "track": "main", "status": "Long", "award": false, "abstract": "How does the input segmentation of pretrained language models (PLMs) affect their interpretations of complex words? We present the first study investigating this question, taking BERT as the example PLM and focusing on its semantic representations of English derivatives. We show that PLMs can be interpreted as serial dual-route models, i.e., the meanings of complex words are either stored or else need to be computed from the subwords, which implies that maximally meaningful input tokens should allow for the best generalization on new words. This hypothesis is confirmed by a series of semantic probing tasks on which DelBERT (Derivation leveraging BERT), a model with derivational input segmentation, substantially outperforms BERT with WordPiece segmentation. Our results suggest that the generalization capabilities of PLMs could be further improved if a morphologically-informed vocabulary of input tokens were used.", "author": "Valentin Hofmann; Janet Pierrehumbert; Hinrich Sch\u00fctze", "authorids": "/v/valentin-hofmann/; /j/janet-pierrehumbert/; /h/hinrich-schutze/", "bibtex": "@inproceedings{hofmann-etal-2021-superbizarre,\n title = \"Superbizarre Is Not Superb: Derivational Morphology Improves {BERT}`s Interpretation of Complex Words\",\n author = {Hofmann, Valentin and\n Pierrehumbert, Janet and\n Sch{\\\"u}tze, Hinrich},\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.279/\",\n doi = \"10.18653/v1/2021.acl-long.279\",\n pages = \"3594--3608\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.279.pdf", "site": "https://aclanthology.org/2021.acl-long.279/", "pdf_size": 1648451, "gs_citation": 74, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=7817165163149435661&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 7, "aff": "Faculty of Linguistics, University of Oxford+Center for Information and Language Processing, LMU Munich; Department of Engineering Science, University of Oxford+Center for Information and Language Processing, LMU Munich; Center for Information and Language Processing, LMU Munich", "aff_domain": "ling-phil.ox.ac.uk; ; ", "email": "ling-phil.ox.ac.uk; ; ", "github": "", "project": "", "author_num": 3, "aff_unique_index": "0+1;0+1;1", "aff_unique_norm": "University of Oxford;LMU Munich", "aff_unique_dep": "Faculty of Linguistics;Center for Information and Language Processing", "aff_unique_url": "https://www.ox.ac.uk;https://www.lmu.de", "aff_unique_abbr": "Oxford;LMU", "aff_campus_unique_index": "0+1;0+1;1", "aff_campus_unique": "Oxford;Munich", "aff_country_unique_index": "0+1;0+1;1", "aff_country_unique": "United Kingdom;Germany" }, { "id": "2021.acl-long.314", "title": "Supporting Cognitive and Emotional Empathic Writing of Students", "track": "main", "status": "Long", "award": false, "abstract": "We present an annotation approach to capturing emotional and cognitive empathy in student-written peer reviews on business models in German. We propose an annotation scheme that allows us to model emotional and cognitive empathy scores based on three types of review components. Also, we conducted an annotation study with three annotators based on 92 student essays to evaluate our annotation scheme. The obtained inter-rater agreement of \u03b1=0.79 for the components and the multi-\u03c0=0.41 for the empathy scores indicate that the proposed annotation scheme successfully guides annotators to a substantial to moderate agreement. Moreover, we trained predictive models to detect the annotated empathy structures and embedded them in an adaptive writing support system for students to receive individual empathy feedback independent of an instructor, time, and location. We evaluated our tool in a peer learning exercise with 58 students and found promising results for perceived empathy skill learning, perceived feedback accuracy, and intention to use. Finally, we present our freely available corpus of 500 empathy-annotated, student-written peer reviews on business models and our annotation guidelines to encourage future research on the design and development of empathy support systems.", "author": "Thiemo Wambsganss; Christina Niklaus; Matthias S\u00f6llner; Siegfried Handschuh; Jan Marco Leimeister", "authorids": "/t/thiemo-wambsganss/; /c/christina-niklaus/; /m/matthias-sollner/; /s/siegfried-handschuh/; /j/jan-marco-leimeister/", "bibtex": "@inproceedings{wambsganss-etal-2021-supporting,\n title = \"Supporting Cognitive and Emotional Empathic Writing of Students\",\n author = {Wambsganss, Thiemo and\n Niklaus, Christina and\n S{\\\"o}llner, Matthias and\n Handschuh, Siegfried and\n Leimeister, Jan Marco},\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.314/\",\n doi = \"10.18653/v1/2021.acl-long.314\",\n pages = \"4063--4077\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.314.pdf", "site": "https://aclanthology.org/2021.acl-long.314/", "pdf_size": 990662, "gs_citation": 28, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=15216925833177981960&as_sdt=400005&sciodt=0,14&hl=en", "gs_version_total": 8, "aff": "University of St.Gallen+Carnegie Mellon University; University of St.Gallen+University of Passau; University of Kassel; University of St.Gallen+University of Passau; University of St.Gallen+University of Kassel", "aff_domain": "unisg.ch;unisg.ch;uni-kassel.de;unisg.ch;unisg.ch", "email": "unisg.ch;unisg.ch;uni-kassel.de;unisg.ch;unisg.ch", "github": "", "project": "", "author_num": 5, "aff_unique_index": "0+1;0+2;3;0+2;0+3", "aff_unique_norm": "University of St.Gallen;Carnegie Mellon University;University of Passau;University of Kassel", "aff_unique_dep": ";;;", "aff_unique_url": "https://www.unisg.ch;https://www.cmu.edu;https://www.uni-passau.de;https://www.uni-kassel.de", "aff_unique_abbr": "HSG;CMU;UP;UKassel", "aff_campus_unique_index": ";;;", "aff_campus_unique": "", "aff_country_unique_index": "0+1;0+2;2;0+2;0+2", "aff_country_unique": "Switzerland;United States;Germany" }, { "id": "2021.acl-long.320", "title": "Supporting Land Reuse of Former Open Pit Mining Sites using Text Classification and Active Learning", "track": "main", "status": "Long", "award": false, "abstract": "Open pit mines left many regions worldwide inhospitable or uninhabitable. Many sites are left behind in a hazardous or contaminated state, show remnants of waste, or have other restrictions imposed upon them, e.g., for the protection of human or nature. Such information has to be permanently managed in order to reuse those areas in the future. In this work we present and evaluate an automated workflow for supporting the post-mining management of former lignite open pit mines in the eastern part of Germany, where prior to any planned land reuse, aforementioned information has to be acquired to ensure the safety and validity of such an endeavor. Usually, this information is found in expert reports, either in the form of paper documents, or in the best case as digitized unstructured text\u2014all of them in German language. However, due to the size and complexity of these documents, any inquiry is tedious and time-consuming, thereby slowing down or even obstructing the reuse of related areas. Since no training data is available, we employ active learning in order to perform multi-label sentence classification for two categories of restrictions and seven categories of topics. The final system integrates optical character recognition (OCR), active-learning-based text classification, and geographic information system visualization in order to effectively extract, query, and visualize this information for any area of interest. Active learning and text classification results are twofold: Whereas the restriction categories were reasonably accurate (>0.85 F1), the seven topic-oriented categories seemed to be complex even for human annotators and achieved mediocre evaluation scores (<0.70 F1).", "author": "Christopher Schr\u00f6der; Kim B\u00fcrgl; Yves Annanias; Andreas Niekler; Lydia M\u00fcller; Daniel Wiegreffe; Christian Bender; Christoph Mengs; Gerik Scheuermann; Gerhard Heyer", "authorids": "/c/christopher-schroder/; /k/kim-burgl/; /y/yves-annanias/; /a/andreas-niekler/; /l/lydia-muller/; /d/daniel-wiegreffe/; /c/christian-bender/; /c/christoph-mengs/; /g/gerik-scheuermann/; /g/gerhard-heyer/", "bibtex": "@inproceedings{schroder-etal-2021-supporting,\n title = \"Supporting Land Reuse of Former Open Pit Mining Sites using Text Classification and Active Learning\",\n author = {Schr{\\\"o}der, Christopher and\n B{\\\"u}rgl, Kim and\n Annanias, Yves and\n Niekler, Andreas and\n M{\\\"u}ller, Lydia and\n Wiegreffe, Daniel and\n Bender, Christian and\n Mengs, Christoph and\n Scheuermann, Gerik and\n Heyer, Gerhard},\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.320/\",\n doi = \"10.18653/v1/2021.acl-long.320\",\n pages = \"4141--4152\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.320.pdf", "site": "https://aclanthology.org/2021.acl-long.320/", "pdf_size": 1235768, "gs_citation": 14, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=10758961653264062907&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 7, "aff": "Natural Language Processing Group+Institute for Applied Informatics (InfAI), Leipzig, Germany+Leipzig University, Germany; Natural Language Processing Group+Institute for Applied Informatics (InfAI), Leipzig, Germany+Leipzig University, Germany; Image and Signal Processing Group+Leipzig University, Germany; Natural Language Processing Group+Leipzig University, Germany; Natural Language Processing Group+Institute for Applied Informatics (InfAI), Leipzig, Germany+Leipzig University, Germany; Image and Signal Processing Group+Leipzig University, Germany; Institute of Public Finance and Public Management+Leipzig University, Germany; Institute of Public Finance and Public Management+Leipzig University, Germany; Image and Signal Processing Group+Institute for Applied Informatics (InfAI), Leipzig, Germany+Leipzig University, Germany; Natural Language Processing Group+Institute for Applied Informatics (InfAI), Leipzig, Germany+Leipzig University, Germany", "aff_domain": "informatik.uni-leipzig.de;informatik.uni-leipzig.de;informatik.uni-leipzig.de;informatik.uni-leipzig.de;informatik.uni-leipzig.de;informatik.uni-leipzig.de;wifa.uni-leipzig.de;wifa.uni-leipzig.de;informatik.uni-leipzig.de;informatik.uni-leipzig.de", "email": "informatik.uni-leipzig.de;informatik.uni-leipzig.de;informatik.uni-leipzig.de;informatik.uni-leipzig.de;informatik.uni-leipzig.de;informatik.uni-leipzig.de;wifa.uni-leipzig.de;wifa.uni-leipzig.de;informatik.uni-leipzig.de;informatik.uni-leipzig.de", "github": "", "project": "", "author_num": 10, "aff_unique_index": "0+1+2;0+1+2;3+2;0+2;0+1+2;3+2;4+2;4+2;3+1+2;0+1+2", "aff_unique_norm": "Natural Language Processing Group;Institute for Applied Informatics;Leipzig University;Image and Signal Processing Group;Institute of Public Finance and Public Management", "aff_unique_dep": "Department of Natural Language Processing;Applied Informatics;;Department of Image and Signal Processing;Public Finance and Public Management", "aff_unique_url": ";;https://www.uni-leipzig.de;;", "aff_unique_abbr": ";InfAI;Uni Leipzig;;", "aff_campus_unique_index": "1;1;;;1;;;;1;1", "aff_campus_unique": ";Leipzig", "aff_country_unique_index": "1+1;1+1;1;1;1+1;1;1;1;1+1;1+1", "aff_country_unique": ";Germany" }, { "id": "2021.acl-long.290", "title": "Surprisal Estimators for Human Reading Times Need Character Models", "track": "main", "status": "Long", "award": false, "abstract": "While the use of character models has been popular in NLP applications, it has not been explored much in the context of psycholinguistic modeling. This paper presents a character model that can be applied to a structural parser-based processing model to calculate word generation probabilities. Experimental results show that surprisal estimates from a structural processing model using this character model deliver substantially better fits to self-paced reading, eye-tracking, and fMRI data than those from large-scale language models trained on much more data. This may suggest that the proposed processing model provides a more humanlike account of sentence processing, which assumes a larger role of morphology, phonotactics, and orthographic complexity than was previously thought.", "author": "Byung-Doh Oh; Christian Clark; William Schuler", "authorids": "/b/byung-doh-oh/; /c/christian-clark/; /w/william-schuler/", "bibtex": "@inproceedings{oh-etal-2021-surprisal,\n title = \"Surprisal Estimators for Human Reading Times Need Character Models\",\n author = \"Oh, Byung-Doh and\n Clark, Christian and\n Schuler, William\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.290/\",\n doi = \"10.18653/v1/2021.acl-long.290\",\n pages = \"3746--3757\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.290.pdf", "site": "https://aclanthology.org/2021.acl-long.290/", "pdf_size": 1120996, "gs_citation": 20, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=2550182894005064816&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 5, "aff": "Department of Linguistics, The Ohio State University; Department of Linguistics, The Ohio State University; Department of Linguistics, The Ohio State University", "aff_domain": "osu.edu;osu.edu;osu.edu", "email": "osu.edu;osu.edu;osu.edu", "github": "https://github.com/byungdoh/acl21_semproc", "project": "", "author_num": 3, "aff_unique_index": "0;0;0", "aff_unique_norm": "The Ohio State University", "aff_unique_dep": "Department of Linguistics", "aff_unique_url": "https://www.osu.edu", "aff_unique_abbr": "OSU", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0;0;0", "aff_country_unique": "United States" }, { "id": "2021.acl-long.420", "title": "Syntax-Enhanced Pre-trained Model", "track": "main", "status": "Long", "award": false, "abstract": "We study the problem of leveraging the syntactic structure of text to enhance pre-trained models such as BERT and RoBERTa. Existing methods utilize syntax of text either in the pre-training stage or in the fine-tuning stage, so that they suffer from discrepancy between the two stages. Such a problem would lead to the necessity of having human-annotated syntactic information, which limits the application of existing methods to broader scenarios. To address this, we present a model that utilizes the syntax of text in both pre-training and fine-tuning stages. Our model is based on Transformer with a syntax-aware attention layer that considers the dependency tree of the text. We further introduce a new pre-training task of predicting the syntactic distance among tokens in the dependency tree. We evaluate the model on three downstream tasks, including relation classification, entity typing, and question answering. Results show that our model achieves state-of-the-art performance on six public benchmark datasets. We have two major findings. First, we demonstrate that infusing automatically produced syntax of text improves pre-trained models. Second, global syntactic distances among tokens bring larger performance gains compared to local head relations between contiguous tokens.", "author": "Zenan Xu; Daya Guo; Duyu Tang; Qinliang Su; Linjun Shou; Ming Gong; Wanjun Zhong; Xiaojun Quan; Daxin Jiang; Nan Duan", "authorids": "/z/zenan-xu/; /d/daya-guo/; /d/duyu-tang/; /q/qinliang-su/; /l/linjun-shou/; /m/ming-gong/; /w/wanjun-zhong/; /x/xiaojun-quan/; /d/daxin-jiang/; /n/nan-duan/", "bibtex": "@inproceedings{xu-etal-2021-syntax,\n title = \"Syntax-Enhanced Pre-trained Model\",\n author = \"Xu, Zenan and\n Guo, Daya and\n Tang, Duyu and\n Su, Qinliang and\n Shou, Linjun and\n Gong, Ming and\n Zhong, Wanjun and\n Quan, Xiaojun and\n Jiang, Daxin and\n Duan, Nan\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.420/\",\n doi = \"10.18653/v1/2021.acl-long.420\",\n pages = \"5412--5422\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.420.pdf", "site": "https://aclanthology.org/2021.acl-long.420/", "pdf_size": 605950, "gs_citation": 50, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=4864884555486056178&as_sdt=5,33&sciodt=0,33&hl=en", "gs_version_total": 6, "aff": "School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China+Guangdong Key Laboratory of Big Data Analysis and Processing, Guangzhou, China+Key Lab. of Machine Intelligence and Advanced Computing, Ministry of Education, China; School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China+Guangdong Key Laboratory of Big Data Analysis and Processing, Guangzhou, China+Key Lab. of Machine Intelligence and Advanced Computing, Ministry of Education, China; Microsoft Research Asia, Beijing, China; School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China+Guangdong Key Laboratory of Big Data Analysis and Processing, Guangzhou, China+Key Lab. of Machine Intelligence and Advanced Computing, Ministry of Education, China; Microsoft Search Technology Center Asia, Beijing, China; Microsoft Search Technology Center Asia, Beijing, China; School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China+Guangdong Key Laboratory of Big Data Analysis and Processing, Guangzhou, China+Key Lab. of Machine Intelligence and Advanced Computing, Ministry of Education, China; School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China; Microsoft Search Technology Center Asia, Beijing, China; Microsoft Research Asia, Beijing, China", "aff_domain": "mail2.sysu.edu.cn;mail2.sysu.edu.cn;microsoft.com;mail.sysu.edu.cn;microsoft.com;microsoft.com;mail2.sysu.edu.cn;mail.sysu.edu.cn;microsoft.com;microsoft.com", "email": "mail2.sysu.edu.cn;mail2.sysu.edu.cn;microsoft.com;mail.sysu.edu.cn;microsoft.com;microsoft.com;mail2.sysu.edu.cn;mail.sysu.edu.cn;microsoft.com;microsoft.com", "github": "https://github.com/Hi-ZenanXu/Syntax-Enhanced Pre-trained Model.data", "project": "", "author_num": 10, "aff_unique_index": "0+1+2;0+1+2;3;0+1+2;4;4;0+1+2;0;4;3", "aff_unique_norm": "Sun Yat-sen University;Guangdong Key Laboratory of Big Data Analysis and Processing;Key Laboratory of Machine Intelligence and Advanced Computing;Microsoft Research Asia;Microsoft Search Technology Center Asia", "aff_unique_dep": "School of Computer Science and Engineering;;Ministry of Education;Research;Search Technology Center", "aff_unique_url": "http://www.sysu.edu.cn;;;https://www.microsoft.com/en-us/research/group/asia;https://www.microsoft.com", "aff_unique_abbr": "SYSU;;;MSRA;MSTC Asia", "aff_campus_unique_index": "0+0;0+0;2;0+0;2;2;0+0;0;2;2", "aff_campus_unique": "Guangzhou;;Beijing", "aff_country_unique_index": "0+0+0;0+0+0;0;0+0+0;0;0;0+0+0;0;0;0", "aff_country_unique": "China" }, { "id": "2021.acl-long.350", "title": "Syntax-augmented Multilingual BERT for Cross-lingual Transfer", "track": "main", "status": "Long", "award": false, "abstract": "In recent years, we have seen a colossal effort in pre-training multilingual text encoders using large-scale corpora in many languages to facilitate cross-lingual transfer learning. However, due to typological differences across languages, the cross-lingual transfer is challenging. Nevertheless, language syntax, e.g., syntactic dependencies, can bridge the typological gap. Previous works have shown that pre-trained multilingual encoders, such as mBERT (CITATION), capture language syntax, helping cross-lingual transfer. This work shows that explicitly providing language syntax and training mBERT using an auxiliary objective to encode the universal dependency tree structure helps cross-lingual transfer. We perform rigorous experiments on four NLP tasks, including text classification, question answering, named entity recognition, and task-oriented semantic parsing. The experiment results show that syntax-augmented mBERT improves cross-lingual transfer on popular benchmarks, such as PAWS-X and MLQA, by 1.4 and 1.6 points on average across all languages. In the generalized transfer setting, the performance boosted significantly, with 3.9 and 3.1 points on average in PAWS-X and MLQA.", "author": "Wasi Ahmad; Haoran Li; Kai-Wei Chang; Yashar Mehdad", "authorids": "/w/wasi-ahmad/; /h/haoran-li/; /k/kai-wei-chang/; /y/yashar-mehdad/", "bibtex": "@inproceedings{ahmad-etal-2021-syntax,\n title = \"Syntax-augmented Multilingual {BERT} for Cross-lingual Transfer\",\n author = \"Ahmad, Wasi and\n Li, Haoran and\n Chang, Kai-Wei and\n Mehdad, Yashar\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.350/\",\n doi = \"10.18653/v1/2021.acl-long.350\",\n pages = \"4538--4554\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.350.pdf", "site": "https://aclanthology.org/2021.acl-long.350/", "pdf_size": 1324034, "gs_citation": 39, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=17484710204160601203&as_sdt=5,33&sciodt=0,33&hl=en", "gs_version_total": 4, "aff": "University of California, Los Angeles; Facebook AI; University of California, Los Angeles; Facebook AI", "aff_domain": "cs.ucla.edu;fb.com;cs.ucla.edu;fb.com", "email": "cs.ucla.edu;fb.com;cs.ucla.edu;fb.com", "github": "", "project": "", "author_num": 4, "aff_unique_index": "0;1;0;1", "aff_unique_norm": "University of California, Los Angeles;Facebook", "aff_unique_dep": ";Facebook AI", "aff_unique_url": "https://www.ucla.edu;https://www.facebook.com", "aff_unique_abbr": "UCLA;Facebook AI", "aff_campus_unique_index": "0;0", "aff_campus_unique": "Los Angeles;", "aff_country_unique_index": "0;0;0;0", "aff_country_unique": "United States" }, { "id": "2021.acl-long.126", "title": "Syntopical Graphs for Computational Argumentation Tasks", "track": "main", "status": "Long", "award": false, "abstract": "Approaches to computational argumentation tasks such as stance detection and aspect detection have largely focused on the text of independent claims, losing out on potentially valuable context provided by the rest of the collection. We introduce a general approach to these tasks motivated by syntopical reading, a reading process that emphasizes comparing and contrasting viewpoints in order to improve topic understanding. To capture collection-level context, we introduce the syntopical graph, a data structure for linking claims within a collection. A syntopical graph is a typed multi-graph where nodes represent claims and edges represent different possible pairwise relationships, such as entailment, paraphrase, or support. Experiments applying syntopical graphs to the problems of detecting stance and aspects demonstrate state-of-the-art performance in each domain, significantly outperforming approaches that do not utilize collection-level information.", "author": "Joe Barrow; Rajiv Jain; Nedim Lipka; Franck Dernoncourt; Vlad Morariu; Varun Manjunatha; Douglas Oard; Philip Resnik; Henning Wachsmuth", "authorids": "/j/joe-barrow/; /r/rajiv-jain/; /n/nedim-lipka/; /f/franck-dernoncourt/; /v/vlad-morariu/; /v/varun-manjunatha/; /d/douglas-w-oard/; /p/philip-resnik/; /h/henning-wachsmuth/", "bibtex": "@inproceedings{barrow-etal-2021-syntopical,\n title = \"Syntopical Graphs for Computational Argumentation Tasks\",\n author = \"Barrow, Joe and\n Jain, Rajiv and\n Lipka, Nedim and\n Dernoncourt, Franck and\n Morariu, Vlad and\n Manjunatha, Varun and\n Oard, Douglas and\n Resnik, Philip and\n Wachsmuth, Henning\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.126/\",\n doi = \"10.18653/v1/2021.acl-long.126\",\n pages = \"1583--1595\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.126.pdf", "site": "https://aclanthology.org/2021.acl-long.126/", "pdf_size": 660931, "gs_citation": 5, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=5781151706018056338&as_sdt=5,33&sciodt=0,33&hl=en", "gs_version_total": 4, "aff": "University of Maryland; Adobe Research; Adobe Research; Adobe Research; Adobe Research; Adobe Research; University of Maryland; University of Maryland; Paderborn University", "aff_domain": "cs.umd.edu;adobe.com;adobe.com;adobe.com;adobe.com;adobe.com;umd.edu;umd.edu;upb.de", "email": "cs.umd.edu;adobe.com;adobe.com;adobe.com;adobe.com;adobe.com;umd.edu;umd.edu;upb.de", "github": "", "project": "", "author_num": 9, "aff_unique_index": "0;1;1;1;1;1;0;0;2", "aff_unique_norm": "University of Maryland;Adobe;Paderborn University", "aff_unique_dep": ";Adobe Research;", "aff_unique_url": "https://www/umd.edu;https://research.adobe.com;https://www.upb.de/", "aff_unique_abbr": "UMD;Adobe;UPB", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0;0;0;0;0;0;0;0;1", "aff_country_unique": "United States;Germany" }, { "id": "2021.acl-long.299", "title": "TAN-NTM: Topic Attention Networks for Neural Topic Modeling", "track": "main", "status": "Long", "award": false, "abstract": "Topic models have been widely used to learn text representations and gain insight into document corpora. To perform topic discovery, most existing neural models either take document bag-of-words (BoW) or sequence of tokens as input followed by variational inference and BoW reconstruction to learn topic-word distribution. However, leveraging topic-word distribution for learning better features during document encoding has not been explored much. To this end, we develop a framework TAN-NTM, which processes document as a sequence of tokens through a LSTM whose contextual outputs are attended in a topic-aware manner. We propose a novel attention mechanism which factors in topic-word distribution to enable the model to attend on relevant words that convey topic related cues. The output of topic attention module is then used to carry out variational inference. We perform extensive ablations and experiments resulting in ~9-15 percentage improvement over score of existing SOTA topic models in NPMI coherence on several benchmark datasets - 20Newsgroups, Yelp Review Polarity and AGNews. Further, we show that our method learns better latent document-topic features compared to existing topic models through improvement on two downstream tasks: document classification and topic guided keyphrase generation.", "author": "Madhur Panwar; Shashank Shailabh; Milan Aggarwal; Balaji Krishnamurthy", "authorids": "/m/madhur-panwar/; /s/shashank-shailabh/; /m/milan-aggarwal/; /b/balaji-krishnamurthy/", "bibtex": "@inproceedings{panwar-etal-2021-tan,\n title = \"{TAN}-{NTM}: {T}opic Attention Networks for Neural Topic Modeling\",\n author = \"Panwar, Madhur and\n Shailabh, Shashank and\n Aggarwal, Milan and\n Krishnamurthy, Balaji\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.299/\",\n doi = \"10.18653/v1/2021.acl-long.299\",\n pages = \"3865--3880\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.299.pdf", "site": "https://aclanthology.org/2021.acl-long.299/", "pdf_size": 1040326, "gs_citation": 26, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=15199699741209969537&as_sdt=400005&sciodt=0,14&hl=en", "gs_version_total": 4, "aff": "Media and Data Science Research Labs, Adobe+Indian Institute of Technology Kanpur (IIT Kanpur), India; Indian Institute of Technology Kanpur (IIT Kanpur), India; Media and Data Science Research Labs, Adobe; Media and Data Science Research Labs, Adobe", "aff_domain": "gmail.com;gmail.com; ; ", "email": "gmail.com;gmail.com; ; ", "github": "", "project": "", "author_num": 4, "aff_unique_index": "0+1;1;0;0", "aff_unique_norm": "Adobe;Indian Institute of Technology Kanpur", "aff_unique_dep": "Media and Data Science Research Labs;", "aff_unique_url": "https://www.adobe.com;https://www.iitk.ac.in", "aff_unique_abbr": "Adobe;IIT Kanpur", "aff_campus_unique_index": "1;1", "aff_campus_unique": ";Kanpur", "aff_country_unique_index": "0+1;1;0;0", "aff_country_unique": "United States;India" }, { "id": "2021.acl-long.254", "title": "TAT-QA: A Question Answering Benchmark on a Hybrid of Tabular and Textual Content in Finance", "track": "main", "status": "Long", "award": false, "abstract": "Hybrid data combining both tabular and textual content (e.g., financial reports) are quite pervasive in the real world. However, Question Answering (QA) over such hybrid data is largely neglected in existing research. In this work, we extract samples from real financial reports to build a new large-scale QA dataset containing both Tabular And Textual data, named TAT-QA, where numerical reasoning is usually required to infer the answer, such as addition, subtraction, multiplication, division, counting, comparison/sorting, and the compositions. We further propose a novel QA model termed TAGOP, which is capable of reasoning over both tables and text. It adopts sequence tagging to extract relevant cells from the table along with relevant spans from the text to infer their semantics, and then applies symbolic reasoning over them with a set of aggregation operators to arrive at the final answer. TAGOP achieves 58.0% inF1, which is an 11.1% absolute increase over the previous best baseline model, according to our experiments on TAT-QA. But this result still lags far behind performance of expert human, i.e.90.8% in F1. It is demonstrated that our TAT-QA is very challenging and can serve as a benchmark for training and testing powerful QA models that address hybrid form data.", "author": "Fengbin Zhu; Wenqiang Lei; Youcheng Huang; Chao Wang; Shuo Zhang; Jiancheng Lv; Fuli Feng; Tat-Seng Chua", "authorids": "/f/fengbin-zhu/; /w/wenqiang-lei/; /y/youcheng-huang/; /c/chao-wang/; /s/shuo-zhang/; /j/jiancheng-lv/; /f/fuli-feng/; /t/tat-seng-chua/", "bibtex": "@inproceedings{zhu-etal-2021-tat,\n title = \"{TAT}-{QA}: A Question Answering Benchmark on a Hybrid of Tabular and Textual Content in Finance\",\n author = \"Zhu, Fengbin and\n Lei, Wenqiang and\n Huang, Youcheng and\n Wang, Chao and\n Zhang, Shuo and\n Lv, Jiancheng and\n Feng, Fuli and\n Chua, Tat-Seng\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.254/\",\n doi = \"10.18653/v1/2021.acl-long.254\",\n pages = \"3277--3287\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.254.pdf", "site": "https://aclanthology.org/2021.acl-long.254/", "pdf_size": 462319, "gs_citation": 279, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=12805551092898522923&as_sdt=5,33&sciodt=0,33&hl=en", "gs_version_total": 7, "aff": "National University of Singapore; National University of Singapore; Sichuan University; Estates Pte Ltd; Bloomberg; Sichuan University; National University of Singapore; National University of Singapore", "aff_domain": "gmail.com;gmail.com; ;6estates.com; ; ; ;", "email": "gmail.com;gmail.com; ;6estates.com; ; ; ;", "github": "https://nextplusplus.github.io/TAT-QA/", "project": "", "author_num": 8, "aff_unique_index": "0;0;1;2;3;1;0;0", "aff_unique_norm": "National University of Singapore;Sichuan University;Estates Private Limited;Bloomberg", "aff_unique_dep": ";;;", "aff_unique_url": "https://www.nus.edu.sg;https://www.scu.edu.cn;;https://www.bloomberg.com", "aff_unique_abbr": "NUS;SCU;;Bloomberg", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0;0;1;3;1;0;0", "aff_country_unique": "Singapore;China;;United States" }, { "id": "2021.acl-long.469", "title": "TGEA: An Error-Annotated Dataset and Benchmark Tasks for TextGeneration from Pretrained Language Models", "track": "main", "status": "Long", "award": false, "abstract": "In order to deeply understand the capability of pretrained language models in text generation and conduct a diagnostic evaluation, we propose TGEA, an error-annotated dataset with multiple benchmark tasks for text generation from pretrained language models (PLMs). We use carefully selected prompt words to guide GPT-2 to generate candidate sentences, from which we select 47K for error annotation. Crowdsourced workers manually check each of these sentences and detect 12k erroneous sentences. We create an error taxonomy to cover 24 types of errors occurring in these erroneous sentences according to the nature of errors with respect to linguistics and knowledge (e.g., common sense). For each erroneous span in PLM-generated sentences, we also detect another span that is closely associated with it. Each error is hence manually labeled with comprehensive annotations, including the span of the error, the associated span, minimal correction to the error, the type of the error, and rationale behind the error. Apart from the fully annotated dataset, we also present a detailed description of the data collection procedure, statistics and analysis of the dataset. This is the first dataset with comprehensive annotations for PLM-generated texts, which facilitates the diagnostic evaluation of PLM-based text generation. Furthermore, we use TGEA as a benchmark dataset and propose a series of automatic diagnosis tasks, including error detection, error type classification, associated span detection, error rationale generation, to further promote future study on the automatic error detection and correction on texts generated by pretrained language models.", "author": "Jie He; Bo Peng; Yi Liao; Qun Liu; Deyi Xiong", "authorids": "/j/jie-he/; /b/bo-peng/; /y/yi-liao/; /q/qun-liu/; /d/deyi-xiong/", "bibtex": "@inproceedings{he-etal-2021-tgea,\n title = \"{TGEA}: An Error-Annotated Dataset and Benchmark Tasks for {T}ext{G}eneration from Pretrained Language Models\",\n author = \"He, Jie and\n Peng, Bo and\n Liao, Yi and\n Liu, Qun and\n Xiong, Deyi\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.469/\",\n doi = \"10.18653/v1/2021.acl-long.469\",\n pages = \"6012--6025\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.469.pdf", "site": "https://aclanthology.org/2021.acl-long.469/", "pdf_size": 1465035, "gs_citation": -1, "gs_cited_by_link": "", "gs_version_total": 0, "aff": "College of Intelligence and Computing, Tianjin University, Tianjin, China+Huawei Noah\u2019s Ark Lab, Hong Kong, China; Huawei Noah\u2019s Ark Lab, Hong Kong, China; Huawei Noah\u2019s Ark Lab, Hong Kong, China; Huawei Noah\u2019s Ark Lab, Hong Kong, China; College of Intelligence and Computing, Tianjin University, Tianjin, China", "aff_domain": "tju.edu.cn;huawei.com;huawei.com;huawei.com;tju.edu.cn", "email": "tju.edu.cn;huawei.com;huawei.com;huawei.com;tju.edu.cn", "github": "", "project": "https://download.mindspore.cn/dataset/TGEA/", "author_num": 5, "aff_unique_index": "0+1;1;1;1;0", "aff_unique_norm": "Tianjin University;Huawei Noah\u2019s Ark Lab", "aff_unique_dep": "College of Intelligence and Computing;", "aff_unique_url": "http://www.tju.edu.cn;https://www.huawei.com/en/ai/noahs-ark-lab", "aff_unique_abbr": "Tianjin University;HNAI", "aff_campus_unique_index": "0+1;1;1;1;0", "aff_campus_unique": "Tianjin;Hong Kong", "aff_country_unique_index": "0+0;0;0;0;0", "aff_country_unique": "China" }, { "id": "2021.acl-long.549", "title": "TIMEDIAL: Temporal Commonsense Reasoning in Dialog", "track": "main", "status": "Long", "award": false, "abstract": "Everyday conversations require understanding everyday events, which in turn, requires understanding temporal commonsense concepts interwoven with those events. Despite recent progress with massive pre-trained language models (LMs) such as T5 and GPT-3, their capability of temporal reasoning in dialogs remains largely under-explored. In this paper, we present the first study to investigate pre-trained LMs for their temporal reasoning capabilities in dialogs by introducing a new task and a crowd-sourced English challenge set, TimeDial. We formulate TimeDial as a multiple choice cloze task with over 1.1K carefully curated dialogs. Empirical results demonstrate that even the best performing models struggle on this task compared to humans, with 23 absolute points of gap in accuracy. Furthermore, our analysis reveals that the models fail to reason about dialog context correctly; instead, they rely on shallow cues based on existing temporal patterns in context, motivating future research for modeling temporal concepts in text and robust contextual reasoning about them. The dataset is publicly available at https://github.com/google-research-datasets/timedial.", "author": "Lianhui Qin; Aditya Gupta; Shyam Upadhyay; Luheng He; Yejin Choi; Manaal Faruqui", "authorids": "/l/lianhui-qin/; /a/aditya-gupta/; /s/shyam-upadhyay/; /l/luheng-he/; /y/yejin-choi/; /m/manaal-faruqui/", "bibtex": "@inproceedings{qin-etal-2021-timedial,\n title = \"{TIMEDIAL}: Temporal Commonsense Reasoning in Dialog\",\n author = \"Qin, Lianhui and\n Gupta, Aditya and\n Upadhyay, Shyam and\n He, Luheng and\n Choi, Yejin and\n Faruqui, Manaal\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.549/\",\n doi = \"10.18653/v1/2021.acl-long.549\",\n pages = \"7066--7076\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.549.pdf", "site": "https://aclanthology.org/2021.acl-long.549/", "pdf_size": 487423, "gs_citation": 74, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=12324264331840914588&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 6, "aff": "Google Assistant + Paul G. Allen School of Computer Science & Engineering, University of Washington; Google Assistant; Google Assistant; Google Assistant; Paul G. Allen School of Computer Science & Engineering, University of Washington; Google Assistant", "aff_domain": "cs.washington.edu;google.com;google.com;google.com;cs.washington.edu;google.com", "email": "cs.washington.edu;google.com;google.com;google.com;cs.washington.edu;google.com", "github": "https://github.com/google-research-datasets/timedial", "project": "", "author_num": 6, "aff_unique_index": "0+1;0;0;0;1;0", "aff_unique_norm": "Google;University of Washington", "aff_unique_dep": "Google Assistant;Paul G. Allen School of Computer Science & Engineering", "aff_unique_url": "https://assistant.google.com;https://www.washington.edu", "aff_unique_abbr": "Google Assistant;UW", "aff_campus_unique_index": "0+1;0;0;0;1;0", "aff_campus_unique": "Mountain View;Seattle", "aff_country_unique_index": "0+0;0;0;0;0;0", "aff_country_unique": "United States" }, { "id": "2021.acl-short.67", "title": "TIMERS: Document-level Temporal Relation Extraction", "track": "main", "status": "Short", "award": false, "abstract": "We present TIMERS - a TIME, Rhetorical and Syntactic-aware model for document-level temporal relation classification in the English language. Our proposed method leverages rhetorical discourse features and temporal arguments from semantic role labels, in addition to traditional local syntactic features, trained through a Gated Relational-GCN. Extensive experiments show that the proposed model outperforms previous methods by 5-18% on the TDDiscourse, TimeBank-Dense, and MATRES datasets due to our discourse-level modeling.", "author": "Puneet Mathur; Rajiv Jain; Franck Dernoncourt; Vlad Morariu; Quan Hung Tran; Dinesh Manocha", "authorids": "/p/puneet-mathur/; /r/rajiv-jain/; /f/franck-dernoncourt/; /v/vlad-morariu/; /q/quan-hung-tran/; /d/dinesh-manocha/", "bibtex": "@inproceedings{mathur-etal-2021-timers,\n title = \"{TIMERS}: Document-level Temporal Relation Extraction\",\n author = \"Mathur, Puneet and\n Jain, Rajiv and\n Dernoncourt, Franck and\n Morariu, Vlad and\n Tran, Quan Hung and\n Manocha, Dinesh\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-short.67/\",\n doi = \"10.18653/v1/2021.acl-short.67\",\n pages = \"524--533\"\n}", "pdf": "https://aclanthology.org/2021.acl-short.67.pdf", "site": "https://aclanthology.org/2021.acl-short.67/", "pdf_size": 1288694, "gs_citation": 61, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=9042755782215821135&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 5, "aff": "University of Maryland, College Park; Adobe Research; Adobe Research; Adobe Research; Adobe Research; University of Maryland, College Park", "aff_domain": "umd.edu;adobe.com;adobe.com;adobe.com;adobe.com;umd.edu", "email": "umd.edu;adobe.com;adobe.com;adobe.com;adobe.com;umd.edu", "github": "", "project": "", "author_num": 6, "aff_unique_index": "0;1;1;1;1;0", "aff_unique_norm": "University of Maryland;Adobe", "aff_unique_dep": ";Adobe Research", "aff_unique_url": "https://www/umd.edu;https://research.adobe.com", "aff_unique_abbr": "UMD;Adobe", "aff_campus_unique_index": "0;0", "aff_campus_unique": "College Park;", "aff_country_unique_index": "0;0;0;0;0;0", "aff_country_unique": "United States" }, { "id": "2021.acl-long.356", "title": "TWAG: A Topic-Guided Wikipedia Abstract Generator", "track": "main", "status": "Long", "award": false, "abstract": "Wikipedia abstract generation aims to distill a Wikipedia abstract from web sources and has met significant success by adopting multi-document summarization techniques. However, previous works generally view the abstract as plain text, ignoring the fact that it is a description of a certain entity and can be decomposed into different topics. In this paper, we propose a two-stage model TWAG that guides the abstract generation with topical information. First, we detect the topic of each input paragraph with a classifier trained on existing Wikipedia articles to divide input documents into different topics. Then, we predict the topic distribution of each abstract sentence, and decode the sentence from topic-aware representations with a Pointer-Generator network. We evaluate our model on the WikiCatSum dataset, and the results show that TWAG outperforms various existing baselines and is capable of generating comprehensive abstracts.", "author": "Fangwei Zhu; Shangqing Tu; Jiaxin Shi; Juanzi Li; Lei Hou; Tong Cui", "authorids": "/f/fangwei-zhu/; /s/shangqing-tu/; /j/jiaxin-shi/; /j/juanzi-li/; /l/lei-hou/; /t/tong-cui/", "bibtex": "@inproceedings{zhu-etal-2021-twag,\n title = \"{TWAG}: A Topic-Guided {W}ikipedia Abstract Generator\",\n author = \"Zhu, Fangwei and\n Tu, Shangqing and\n Shi, Jiaxin and\n Li, Juanzi and\n Hou, Lei and\n Cui, Tong\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.356/\",\n doi = \"10.18653/v1/2021.acl-long.356\",\n pages = \"4623--4635\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.356.pdf", "site": "https://aclanthology.org/2021.acl-long.356/", "pdf_size": 355528, "gs_citation": 15, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=1728561188968304717&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 5, "aff": "Dept. of Computer Sci.&Tech., BNRist, Tsinghua University; School of Computer Science and Engineering, Beihang University; Dept. of Computer Sci.&Tech., BNRist, Tsinghua University; Dept. of Computer Sci.&Tech., BNRist, Tsinghua University; Dept. of Computer Sci.&Tech., BNRist, Tsinghua University; Noah\u2019s Ark Lab, Huawei Inc.", "aff_domain": "mails.tsinghua.edu.cn;buaa.edu.cn;mails.tsinghua.edu.cn;tsinghua.edu.cn;tsinghua.edu.cn;huawei.com", "email": "mails.tsinghua.edu.cn;buaa.edu.cn;mails.tsinghua.edu.cn;tsinghua.edu.cn;tsinghua.edu.cn;huawei.com", "github": "https://github.com/THU-KEG/TWAG", "project": "", "author_num": 6, "aff_unique_index": "0;1;0;0;0;2", "aff_unique_norm": "Tsinghua University;Beihang University;Huawei Inc.", "aff_unique_dep": "Department of Computer Science and Technology;School of Computer Science and Engineering;Noah\u2019s Ark Lab", "aff_unique_url": "https://www.tsinghua.edu.cn;http://www.buaa.edu.cn;https://www.huawei.com", "aff_unique_abbr": "THU;BUAA;Huawei", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0;0;0;0;0;0", "aff_country_unique": "China" }, { "id": "2021.acl-long.385", "title": "Tail-to-Tail Non-Autoregressive Sequence Prediction for Chinese Grammatical Error Correction", "track": "main", "status": "Long", "award": false, "abstract": "We investigate the problem of Chinese Grammatical Error Correction (CGEC) and present a new framework named Tail-to-Tail (TtT) non-autoregressive sequence prediction to address the deep issues hidden in CGEC. Considering that most tokens are correct and can be conveyed directly from source to target, and the error positions can be estimated and corrected based on the bidirectional context information, thus we employ a BERT-initialized Transformer Encoder as the backbone model to conduct information modeling and conveying. Considering that only relying on the same position substitution cannot handle the variable-length correction cases, various operations such substitution, deletion, insertion, and local paraphrasing are required jointly. Therefore, a Conditional Random Fields (CRF) layer is stacked on the up tail to conduct non-autoregressive sequence prediction by modeling the token dependencies. Since most tokens are correct and easily to be predicted/conveyed to the target, then the models may suffer from a severe class imbalance issue. To alleviate this problem, focal loss penalty strategies are integrated into the loss functions. Moreover, besides the typical fix-length error correction datasets, we also construct a variable-length corpus to conduct experiments. Experimental results on standard datasets, especially on the variable-length datasets, demonstrate the effectiveness of TtT in terms of sentence-level Accuracy, Precision, Recall, and F1-Measure on tasks of error Detection and Correction.", "author": "Piji Li; Shuming Shi", "authorids": "/p/piji-li/; /s/shuming-shi/", "bibtex": "@inproceedings{li-shi-2021-tail,\n title = \"Tail-to-Tail Non-Autoregressive Sequence Prediction for {C}hinese Grammatical Error Correction\",\n author = \"Li, Piji and\n Shi, Shuming\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.385/\",\n doi = \"10.18653/v1/2021.acl-long.385\",\n pages = \"4973--4984\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.385.pdf", "site": "https://aclanthology.org/2021.acl-long.385/", "pdf_size": 537735, "gs_citation": 43, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=10242920039074982817&as_sdt=5,33&sciodt=0,33&hl=en", "gs_version_total": 6, "aff": "Tencent AI Lab, Shenzhen, China; Tencent AI Lab, Shenzhen, China", "aff_domain": "tencent.com;tencent.com", "email": "tencent.com;tencent.com", "github": "https://github.com/lipiji/TtT", "project": "", "author_num": 2, "aff_unique_index": "0;0", "aff_unique_norm": "Tencent AI Lab", "aff_unique_dep": "AI Lab", "aff_unique_url": "https://ai.tencent.com", "aff_unique_abbr": "Tencent AI Lab", "aff_campus_unique_index": "0;0", "aff_campus_unique": "Shenzhen", "aff_country_unique_index": "0;0", "aff_country_unique": "China" }, { "id": "2021.acl-long.259", "title": "Taming Pre-trained Language Models with N-gram Representations for Low-Resource Domain Adaptation", "track": "main", "status": "Long", "award": false, "abstract": "Large pre-trained models such as BERT are known to improve different downstream NLP tasks, even when such a model is trained on a generic domain. Moreover, recent studies have shown that when large domain-specific corpora are available, continued pre-training on domain-specific data can further improve the performance of in-domain tasks. However, this practice requires significant domain-specific data and computational resources which may not always be available. In this paper, we aim to adapt a generic pretrained model with a relatively small amount of domain-specific data. We demonstrate that by explicitly incorporating multi-granularity information of unseen and domain-specific words via the adaptation of (word based) n-grams, the performance of a generic pretrained model can be greatly improved. Specifically, we introduce a Transformer-based Domain-aware N-gram Adaptor, T-DNA, to effectively learn and incorporate the semantic representation of different combinations of words in the new domain. Experimental results illustrate the effectiveness of T-DNA on eight low-resource downstream tasks from four domains. We show that T-DNA is able to achieve significant improvements compared to existing methods on most tasks using limited data with lower computational costs. Moreover, further analyses demonstrate the importance and effectiveness of both unseen words and the information of different granularities. Our code is available at https://github.com/shizhediao/T-DNA.", "author": "Shizhe Diao; Ruijia Xu; Hongjin Su; Yilei Jiang; Yan Song; Tong Zhang", "authorids": "/s/shizhe-diao/; /r/ruijia-xu/; /h/hongjin-su/; /y/yilei-jiang/; /y/yan-song/; /t/tong-zhang/", "bibtex": "@inproceedings{diao-etal-2021-taming,\n title = \"Taming Pre-trained Language Models with N-gram Representations for Low-Resource Domain Adaptation\",\n author = \"Diao, Shizhe and\n Xu, Ruijia and\n Su, Hongjin and\n Jiang, Yilei and\n Song, Yan and\n Zhang, Tong\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.259/\",\n doi = \"10.18653/v1/2021.acl-long.259\",\n pages = \"3336--3349\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.259.pdf", "site": "https://aclanthology.org/2021.acl-long.259/", "pdf_size": 525593, "gs_citation": 64, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=3515282089142604500&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 6, "aff": "The Hong Kong University of Science and Technology\u2666; The Hong Kong University of Science and Technology\u2666; The Chinese University of Hong Kong\u2663; The Chinese University of Hong Kong\u2663; The Chinese University of Hong Kong (Shenzhen)\u2660+Shenzhen Research Institute of Big Data\u2665; The Hong Kong University of Science and Technology\u2666", "aff_domain": "ust.hk;ust.hk; ; ;cuhk.edu.cn;ust.hk", "email": "ust.hk;ust.hk; ; ;cuhk.edu.cn;ust.hk", "github": "https://github.com/shizhediao/T-DNA", "project": "", "author_num": 6, "aff_unique_index": "0;0;1;1;1+2;0", "aff_unique_norm": "Hong Kong University of Science and Technology;The Chinese University of Hong Kong;Shenzhen Research Institute of Big Data", "aff_unique_dep": ";;", "aff_unique_url": "https://www.ust.hk;https://www.cuhk.edu.hk;http://www.sribd.cn", "aff_unique_abbr": "HKUST;CUHK;", "aff_campus_unique_index": "1", "aff_campus_unique": ";Shenzhen", "aff_country_unique_index": "0;0;0;0;0+0;0", "aff_country_unique": "China" }, { "id": "2021.acl-short.85", "title": "Targeting the Benchmark: On Methodology in Current Natural Language Processing Research", "track": "main", "status": "Short", "award": false, "abstract": "It has become a common pattern in our field: One group introduces a language task, exemplified by a dataset, which they argue is challenging enough to serve as a benchmark. They also provide a baseline model for it, which then soon is improved upon by other groups. Often, research efforts then move on, and the pattern repeats itself. What is typically left implicit is the argumentation for why this constitutes progress, and progress towards what. In this paper, we try to step back for a moment from this pattern and work out possible argumentations and their parts.", "author": "David Schlangen", "authorids": "/d/david-schlangen/", "bibtex": "@inproceedings{schlangen-2021-targeting,\n title = \"Targeting the Benchmark: On Methodology in Current Natural Language Processing Research\",\n author = \"Schlangen, David\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-short.85/\",\n doi = \"10.18653/v1/2021.acl-short.85\",\n pages = \"670--674\"\n}", "pdf": "https://aclanthology.org/2021.acl-short.85.pdf", "site": "https://aclanthology.org/2021.acl-short.85/", "pdf_size": 240816, "gs_citation": 67, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=9553592566850782809&as_sdt=5,33&sciodt=0,33&hl=en", "gs_version_total": 6, "aff": "CoLabPotsdam / Computational Linguistics, Department of Linguistics, University of Potsdam", "aff_domain": "uni-potsdam.de", "email": "uni-potsdam.de", "github": "", "project": "", "author_num": 1, "aff_unique_index": "0", "aff_unique_norm": "University of Potsdam", "aff_unique_dep": "Department of Linguistics", "aff_unique_url": "https://www.uni-potsdam.de", "aff_unique_abbr": "UP", "aff_campus_unique_index": "0", "aff_campus_unique": "Potsdam", "aff_country_unique_index": "0", "aff_country_unique": "Germany" }, { "id": "2021.acl-long.411", "title": "Text-Free Image-to-Speech Synthesis Using Learned Segmental Units", "track": "main", "status": "Long", "award": false, "abstract": "In this paper we present the first model for directly synthesizing fluent, natural-sounding spoken audio captions for images that does not require natural language text as an intermediate representation or source of supervision. Instead, we connect the image captioning module and the speech synthesis module with a set of discrete, sub-word speech units that are discovered with a self-supervised visual grounding task. We conduct experiments on the Flickr8k spoken caption dataset in addition to a novel corpus of spoken audio captions collected for the popular MSCOCO dataset, demonstrating that our generated captions also capture diverse visual semantics of the images they describe. We investigate several different intermediate speech representations, and empirically find that the representation must satisfy several important properties to serve as drop-in replacements for text.", "author": "Wei-Ning Hsu; David Harwath; Tyler Miller; Christopher Song; James Glass", "authorids": "/w/wei-ning-hsu/; /d/david-harwath/; /t/tyler-miller/; /c/christopher-song/; /j/james-glass/", "bibtex": "@inproceedings{hsu-etal-2021-text,\n title = \"Text-Free Image-to-Speech Synthesis Using Learned Segmental Units\",\n author = \"Hsu, Wei-Ning and\n Harwath, David and\n Miller, Tyler and\n Song, Christopher and\n Glass, James\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.411/\",\n doi = \"10.18653/v1/2021.acl-long.411\",\n pages = \"5284--5300\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.411.pdf", "site": "https://aclanthology.org/2021.acl-long.411/", "pdf_size": 3721074, "gs_citation": 87, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=6886446757875274000&as_sdt=400005&sciodt=0,14&hl=en", "gs_version_total": 10, "aff": "Massachusetts Institute of Technology; University of Texas at Austin; University of Texas at Austin; Johns Hopkins University; Massachusetts Institute of Technology", "aff_domain": "csail.mit.edu;utexas.edu; ; ; ", "email": "csail.mit.edu;utexas.edu; ; ; ", "github": "", "project": "", "author_num": 5, "aff_unique_index": "0;1;1;2;0", "aff_unique_norm": "Massachusetts Institute of Technology;University of Texas at Austin;Johns Hopkins University", "aff_unique_dep": ";;", "aff_unique_url": "https://web.mit.edu;https://www.utexas.edu;https://www.jhu.edu", "aff_unique_abbr": "MIT;UT Austin;JHU", "aff_campus_unique_index": "1;1", "aff_campus_unique": ";Austin", "aff_country_unique_index": "0;0;0;0;0", "aff_country_unique": "United States" }, { "id": "2021.acl-long.217", "title": "Text2Event: Controllable Sequence-to-Structure Generation for End-to-end Event Extraction", "track": "main", "status": "Long", "award": false, "abstract": "Event extraction is challenging due to the complex structure of event records and the semantic gap between text and event. Traditional methods usually extract event records by decomposing the complex structure prediction task into multiple subtasks. In this paper, we propose Text2Event, a sequence-to-structure generation paradigm that can directly extract events from the text in an end-to-end manner. Specifically, we design a sequence-to-structure network for unified event extraction, a constrained decoding algorithm for event knowledge injection during inference, and a curriculum learning algorithm for efficient model learning. Experimental results show that, by uniformly modeling all tasks in a single model and universally predicting different labels, our method can achieve competitive performance using only record-level annotations in both supervised learning and transfer learning settings.", "author": "Yaojie Lu; Hongyu Lin; Jin Xu; Xianpei Han; Jialong Tang; Annan Li; Le Sun; Meng Liao; Shaoyi Chen", "authorids": "/y/yaojie-lu/; /h/hongyu-lin/; /j/jin-xu/; /x/xianpei-han/; /j/jialong-tang/; /a/annan-li/; /l/le-sun/; /m/meng-liao/; /s/shaoyi-chen/", "bibtex": "@inproceedings{lu-etal-2021-text2event,\n title = \"{T}ext2{E}vent: Controllable Sequence-to-Structure Generation for End-to-end Event Extraction\",\n author = \"Lu, Yaojie and\n Lin, Hongyu and\n Xu, Jin and\n Han, Xianpei and\n Tang, Jialong and\n Li, Annan and\n Sun, Le and\n Liao, Meng and\n Chen, Shaoyi\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.217/\",\n doi = \"10.18653/v1/2021.acl-long.217\",\n pages = \"2795--2806\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.217.pdf", "site": "https://aclanthology.org/2021.acl-long.217/", "pdf_size": 720423, "gs_citation": 324, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=13451032032832364879&as_sdt=5,33&sciodt=0,33&hl=en", "gs_version_total": 5, "aff": "Chinese Information Processing Laboratory+University of Chinese Academy of Sciences; Chinese Information Processing Laboratory; State Key Laboratory of Computer Science+Institute of Software, Chinese Academy of Sciences; Data Quality Team, WeChat, Tencent Inc.; Chinese Information Processing Laboratory+University of Chinese Academy of Sciences; Chinese Information Processing Laboratory+University of Chinese Academy of Sciences; State Key Laboratory of Computer Science+Institute of Software, Chinese Academy of Sciences; Data Quality Team, WeChat, Tencent Inc.; Data Quality Team, WeChat, Tencent Inc.", "aff_domain": "iscas.ac.cn;iscas.ac.cn;tencent.com;iscas.ac.cn;iscas.ac.cn;iscas.ac.cn;iscas.ac.cn;tencent.com;tencent.com", "email": "iscas.ac.cn;iscas.ac.cn;tencent.com;iscas.ac.cn;iscas.ac.cn;iscas.ac.cn;iscas.ac.cn;tencent.com;tencent.com", "github": "", "project": "", "author_num": 9, "aff_unique_index": "0+1;0;2+3;4;0+1;0+1;2+3;4;4", "aff_unique_norm": "Chinese Information Processing Laboratory;University of Chinese Academy of Sciences;State Key Laboratory of Computer Science;Chinese Academy of Sciences;Tencent Inc.", "aff_unique_dep": "Information Processing;;;Institute of Software;Data Quality Team", "aff_unique_url": ";http://www.ucas.ac.cn;;http://www.ios.ac.cn;https://www.tencent.com", "aff_unique_abbr": ";UCAS;;CAS;Tencent", "aff_campus_unique_index": ";;;;", "aff_campus_unique": "", "aff_country_unique_index": "0+0;0;0+0;0;0+0;0+0;0+0;0;0", "aff_country_unique": "China" }, { "id": "2021.acl-long.293", "title": "TextSETTR: Few-Shot Text Style Extraction and Tunable Targeted Restyling", "track": "main", "status": "Long", "award": false, "abstract": "We present a novel approach to the problem of text style transfer. Unlike previous approaches requiring style-labeled training data, our method makes use of readily-available unlabeled text by relying on the implicit connection in style between adjacent sentences, and uses labeled data only at inference time. We adapt T5 (Raffel et al., 2020), a strong pretrained text-to-text model, to extract a style vector from text and use it to condition the decoder to perform style transfer. As our label-free training results in a style vector space encoding many facets of style, we recast transfers as \u201ctargeted restyling\u201d vector operations that adjust specific attributes of the input while preserving others. We demonstrate that training on unlabeled Amazon reviews data results in a model that is competitive on sentiment transfer, even compared to models trained fully on labeled data. Furthermore, applying our novel method to a diverse corpus of unlabeled web text results in a single model capable of transferring along multiple dimensions of style (dialect, emotiveness, formality, politeness, sentiment) despite no additional training and using only a handful of exemplars at inference time.", "author": "Parker Riley; Noah Constant; Mandy Guo; Girish Kumar; David Uthus; Zarana Parekh", "authorids": "/p/parker-riley/; /n/noah-constant/; /m/mandy-guo/; /g/girish-kumar/; /d/david-c-uthus/; /z/zarana-parekh/", "bibtex": "@inproceedings{riley-etal-2021-textsettr,\n title = \"{T}ext{SETTR}: Few-Shot Text Style Extraction and Tunable Targeted Restyling\",\n author = \"Riley, Parker and\n Constant, Noah and\n Guo, Mandy and\n Kumar, Girish and\n Uthus, David and\n Parekh, Zarana\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.293/\",\n doi = \"10.18653/v1/2021.acl-long.293\",\n pages = \"3786--3800\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.293.pdf", "site": "https://aclanthology.org/2021.acl-long.293/", "pdf_size": 3018531, "gs_citation": 62, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=790506741561623682&as_sdt=400005&sciodt=0,14&hl=en", "gs_version_total": 9, "aff": "University of Rochester; Google Research; Google Research; Stanford University; Google Research; Google Research", "aff_domain": "cs.rochester.edu;google.com;google.com; ; ; ", "email": "cs.rochester.edu;google.com;google.com; ; ; ", "github": "", "project": "", "author_num": 6, "aff_unique_index": "0;1;1;2;1;1", "aff_unique_norm": "University of Rochester;Google;Stanford University", "aff_unique_dep": ";Google Research;", "aff_unique_url": "https://www.rochester.edu;https://research.google;https://www.stanford.edu", "aff_unique_abbr": "U of R;Google Research;Stanford", "aff_campus_unique_index": "1;1;2;1;1", "aff_campus_unique": ";Mountain View;Stanford", "aff_country_unique_index": "0;0;0;0;0;0", "aff_country_unique": "United States" }, { "id": "2021.acl-short.62", "title": "Thank you BART! Rewarding Pre-Trained Models Improves Formality Style Transfer", "track": "main", "status": "Short", "award": false, "abstract": "Scarcity of parallel data causes formality style transfer models to have scarce success in preserving content. We show that fine-tuning pre-trained language (GPT-2) and sequence-to-sequence (BART) models boosts content preservation, and that this is possible even with limited amounts of parallel data. Augmenting these models with rewards that target style and content \u2013the two core aspects of the task\u2013 we achieve a new state-of-the-art.", "author": "Huiyuan Lai; Antonio Toral; Malvina Nissim", "authorids": "/h/huiyuan-lai/; /a/antonio-toral/; /m/malvina-nissim/", "bibtex": "@inproceedings{lai-etal-2021-thank,\n title = \"Thank you {BART}! Rewarding Pre-Trained Models Improves Formality Style Transfer\",\n author = \"Lai, Huiyuan and\n Toral, Antonio and\n Nissim, Malvina\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-short.62/\",\n doi = \"10.18653/v1/2021.acl-short.62\",\n pages = \"484--494\"\n}", "pdf": "https://aclanthology.org/2021.acl-short.62.pdf", "site": "https://aclanthology.org/2021.acl-short.62/", "pdf_size": 463920, "gs_citation": 66, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=4919514711846219460&as_sdt=5,33&sciodt=0,33&hl=en", "gs_version_total": 7, "aff": "CLCG, University of Groningen / The Netherlands; CLCG, University of Groningen / The Netherlands; CLCG, University of Groningen / The Netherlands", "aff_domain": "rug.nl;rug.nl;rug.nl", "email": "rug.nl;rug.nl;rug.nl", "github": "", "project": "", "author_num": 3, "aff_unique_index": "0;0;0", "aff_unique_norm": "University of Groningen", "aff_unique_dep": "CLCG", "aff_unique_url": "https://www.rug.nl", "aff_unique_abbr": "", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0;0;0", "aff_country_unique": "The Netherlands" }, { "id": "2021.acl-long.84", "title": "The Art of Abstention: Selective Prediction and Error Regularization for Natural Language Processing", "track": "main", "status": "Long", "award": false, "abstract": "In selective prediction, a classifier is allowed to abstain from making predictions on low-confidence examples. Though this setting is interesting and important, selective prediction has rarely been examined in natural language processing (NLP) tasks. To fill this void in the literature, we study in this paper selective prediction for NLP, comparing different models and confidence estimators. We further propose a simple error regularization trick that improves confidence estimation without substantially increasing the computation budget. We show that recent pre-trained transformer models simultaneously improve both model accuracy and confidence estimation effectiveness. We also find that our proposed regularization improves confidence estimation and can be applied to other relevant scenarios, such as using classifier cascades for accuracy\u2013efficiency trade-offs. Source code for this paper can be found at https://github.com/castorini/transformers-selective.", "author": "Ji Xin; Raphael Tang; Yaoliang Yu; Jimmy Lin", "authorids": "/j/ji-xin/; /r/raphael-tang/; /y/yaoliang-yu/; /j/jimmy-lin/", "bibtex": "@inproceedings{xin-etal-2021-art,\n title = \"The Art of Abstention: Selective Prediction and Error Regularization for Natural Language Processing\",\n author = \"Xin, Ji and\n Tang, Raphael and\n Yu, Yaoliang and\n Lin, Jimmy\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.84/\",\n doi = \"10.18653/v1/2021.acl-long.84\",\n pages = \"1040--1051\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.84.pdf", "site": "https://aclanthology.org/2021.acl-long.84/", "pdf_size": 753898, "gs_citation": 65, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=2991775742978671675&as_sdt=5,33&sciodt=0,33&hl=en", "gs_version_total": 2, "aff": "David R. Cheriton School of Computer Science, University of Waterloo + Vector Institute for Arti\ufb01cial Intelligence; David R. Cheriton School of Computer Science, University of Waterloo + Vector Institute for Arti\ufb01cial Intelligence; David R. Cheriton School of Computer Science, University of Waterloo + Vector Institute for Arti\ufb01cial Intelligence; David R. Cheriton School of Computer Science, University of Waterloo + Vector Institute for Arti\ufb01cial Intelligence", "aff_domain": "uwaterloo.ca;uwaterloo.ca;uwaterloo.ca;uwaterloo.ca", "email": "uwaterloo.ca;uwaterloo.ca;uwaterloo.ca;uwaterloo.ca", "github": "https://github.com/castorini/transformers-selective", "project": "", "author_num": 4, "aff_unique_index": "0+1;0+1;0+1;0+1", "aff_unique_norm": "University of Waterloo;Vector Institute for Artificial Intelligence", "aff_unique_dep": "David R. Cheriton School of Computer Science;Artificial Intelligence", "aff_unique_url": "https://uwaterloo.ca;https://vectorinstitute.ai/", "aff_unique_abbr": "UWaterloo;Vector Institute", "aff_campus_unique_index": ";;;", "aff_campus_unique": "", "aff_country_unique_index": "0+0;0+0;0+0;0+0", "aff_country_unique": "Canada" }, { "id": "2021.acl-short.18", "title": "The Case for Translation-Invariant Self-Attention in Transformer-Based Language Models", "track": "main", "status": "Short", "award": false, "abstract": "Mechanisms for encoding positional information are central for transformer-based language models. In this paper, we analyze the position embeddings of existing language models, finding strong evidence of translation invariance, both for the embeddings themselves and for their effect on self-attention. The degree of translation invariance increases during training and correlates positively with model performance. Our findings lead us to propose translation-invariant self-attention (TISA), which accounts for the relative position between tokens in an interpretable fashion without needing conventional position embeddings. Our proposal has several theoretical advantages over existing position-representation approaches. Proof-of-concept experiments show that it improves on regular ALBERT on GLUE tasks, while only adding orders of magnitude less positional parameters.", "author": "Ulme Wennberg; Gustav Eje Henter", "authorids": "/u/ulme-wennberg/; /g/gustav-eje-henter/", "bibtex": "@inproceedings{wennberg-henter-2021-case,\n title = \"The Case for Translation-Invariant Self-Attention in Transformer-Based Language Models\",\n author = \"Wennberg, Ulme and\n Henter, Gustav Eje\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-short.18/\",\n doi = \"10.18653/v1/2021.acl-short.18\",\n pages = \"130--140\"\n}", "pdf": "https://aclanthology.org/2021.acl-short.18.pdf", "site": "https://aclanthology.org/2021.acl-short.18/", "pdf_size": 6114841, "gs_citation": 24, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=17897133897516298304&as_sdt=5,44&sciodt=0,44&hl=en", "gs_version_total": 8, "aff": "Division of Speech, Music and Hearing, KTH Royal Institute of Technology, Sweden; Division of Speech, Music and Hearing, KTH Royal Institute of Technology, Sweden", "aff_domain": "kth.se;kth.se", "email": "kth.se;kth.se", "github": "", "project": "", "author_num": 2, "aff_unique_index": "0;0", "aff_unique_norm": "KTH Royal Institute of Technology", "aff_unique_dep": "Division of Speech, Music and Hearing", "aff_unique_url": "https://www.kth.se", "aff_unique_abbr": "KTH", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0;0", "aff_country_unique": "Sweden" }, { "id": "2021.acl-short.77", "title": "The Curse of Dense Low-Dimensional Information Retrieval for Large Index Sizes", "track": "main", "status": "Short", "award": false, "abstract": "Information Retrieval using dense low-dimensional representations recently became popular and showed out-performance to traditional sparse-representations like BM25. However, no previous work investigated how dense representations perform with large index sizes. We show theoretically and empirically that the performance for dense representations decreases quicker than sparse representations for increasing index sizes. In extreme cases, this can even lead to a tipping point where at a certain index size sparse representations outperform dense representations. We show that this behavior is tightly connected to the number of dimensions of the representations: The lower the dimension, the higher the chance for false positives, i.e. returning irrelevant documents", "author": "Nils Reimers; Iryna Gurevych", "authorids": "/n/nils-reimers/; /i/iryna-gurevych/", "bibtex": "@inproceedings{reimers-gurevych-2021-curse,\n title = \"The Curse of Dense Low-Dimensional Information Retrieval for Large Index Sizes\",\n author = \"Reimers, Nils and\n Gurevych, Iryna\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-short.77/\",\n doi = \"10.18653/v1/2021.acl-short.77\",\n pages = \"605--611\"\n}", "pdf": "https://aclanthology.org/2021.acl-short.77.pdf", "site": "https://aclanthology.org/2021.acl-short.77/", "pdf_size": 957076, "gs_citation": 69, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=8635084622843564286&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 4, "aff": "Ubiquitous Knowledge Processing Lab (UKP-TUDA) + Department of Computer Science, Technical University of Darmstadt; Ubiquitous Knowledge Processing Lab (UKP-TUDA) + Department of Computer Science, Technical University of Darmstadt", "aff_domain": ";", "email": ";", "github": "", "project": "www.ukp.tu-darmstadt.de", "author_num": 2, "aff_unique_index": "0+1;0+1", "aff_unique_norm": "University of T\u00fcbingen;Technical University of Darmstadt", "aff_unique_dep": "Ubiquitous Knowledge Processing Lab;Department of Computer Science", "aff_unique_url": "https://www.uni-tuebingen.de/fakultaeten/informatik/lehrstuehle/ki/ukp-tuda.html;https://www.tu-darmstadt.de", "aff_unique_abbr": "UKP-TUDA;TUD", "aff_campus_unique_index": ";", "aff_campus_unique": "", "aff_country_unique_index": "0+0;0+0", "aff_country_unique": "Germany" }, { "id": "2021.acl-long.208", "title": "The Limitations of Limited Context for Constituency Parsing", "track": "main", "status": "Long", "award": false, "abstract": "Incorporating syntax into neural approaches in NLP has a multitude of practical and scientific benefits. For instance, a language model that is syntax-aware is likely to be able to produce better samples; even a discriminative model like BERT with a syntax module could be used for core NLP tasks like unsupervised syntactic parsing. Rapid progress in recent years was arguably spurred on by the empirical success of the Parsing-Reading-Predict architecture of (Shen et al., 2018a), later simplified by the Order Neuron LSTM of (Shen et al., 2019). Most notably, this is the first time neural approaches were able to successfully perform unsupervised syntactic parsing (evaluated by various metrics like F-1 score). However, even heuristic (much less fully mathematical) understanding of why and when these architectures work is lagging severely behind. In this work, we answer representational questions raised by the architectures in (Shen et al., 2018a, 2019), as well as some transition-based syntax-aware language models (Dyer et al., 2016): what kind of syntactic structure can current neural approaches to syntax represent? Concretely, we ground this question in the sandbox of probabilistic context-free-grammars (PCFGs), and identify a key aspect of the representational power of these approaches: the amount and directionality of context that the predictor has access to when forced to make parsing decision. We show that with limited context (either bounded, or unidirectional), there are PCFGs, for which these approaches cannot represent the max-likelihood parse; conversely, if the context is unlimited, they can represent the max-likelihood parse of any PCFG.", "author": "Yuchen Li; Andrej Risteski", "authorids": "/y/yuchen-li/; /a/andrej-risteski/", "bibtex": "@inproceedings{li-risteski-2021-limitations,\n title = \"The Limitations of Limited Context for Constituency Parsing\",\n author = \"Li, Yuchen and\n Risteski, Andrej\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.208/\",\n doi = \"10.18653/v1/2021.acl-long.208\",\n pages = \"2675--2687\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.208.pdf", "site": "https://aclanthology.org/2021.acl-long.208/", "pdf_size": 997633, "gs_citation": 5, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=4979483050443449219&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 4, "aff": "Carnegie Mellon University; Carnegie Mellon University", "aff_domain": "andrew.cmu.edu;andrew.cmu.edu", "email": "andrew.cmu.edu;andrew.cmu.edu", "github": "", "project": "", "author_num": 2, "aff_unique_index": "0;0", "aff_unique_norm": "Carnegie Mellon University", "aff_unique_dep": "", "aff_unique_url": "https://www.cmu.edu", "aff_unique_abbr": "CMU", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0;0", "aff_country_unique": "United States" }, { "id": "2021.acl-long.77", "title": "The Possible, the Plausible, and the Desirable: Event-Based Modality Detection for Language Processing", "track": "main", "status": "Long", "award": false, "abstract": "Modality is the linguistic ability to describe vents with added information such as how desirable, plausible, or feasible they are. Modality is important for many NLP downstream tasks such as the detection of hedging, uncertainty, speculation, and more. Previous studies that address modality detection in NLP often restrict modal expressions to a closed syntactic class, and the modal sense labels are vastly different across different studies, lacking an accepted standard. Furthermore, these senses are often analyzed independently of the events that they modify. This work builds on the theoretical foundations of the Georgetown Gradable Modal Expressions (GME) work by Rubinstein et al. (2013) to propose an event-based modality detection task where modal expressions can be words of any syntactic class and sense labels are drawn from a comprehensive taxonomy which harmonizes the modal concepts contributed by the different studies. We present experiments on the GME corpus aiming to detect and classify fine-grained modal concepts and associate them with their modified events. We show that detecting and classifying modal expressions is not only feasible, it also improves the detection of modal events in their own right.", "author": "Valentina Pyatkin; Shoval Sadde; Aynat Rubinstein; Paul Portner; Reut Tsarfaty", "authorids": "/v/valentina-pyatkin/; /s/shoval-sadde/; /a/aynat-rubinstein/; /p/paul-portner/; /r/reut-tsarfaty/", "bibtex": "@inproceedings{pyatkin-etal-2021-possible,\n title = \"The Possible, the Plausible, and the Desirable: Event-Based Modality Detection for Language Processing\",\n author = \"Pyatkin, Valentina and\n Sadde, Shoval and\n Rubinstein, Aynat and\n Portner, Paul and\n Tsarfaty, Reut\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.77/\",\n doi = \"10.18653/v1/2021.acl-long.77\",\n pages = \"953--965\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.77.pdf", "site": "https://aclanthology.org/2021.acl-long.77/", "pdf_size": 308214, "gs_citation": 18, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=6110922852774098009&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 4, "aff": "Bar Ilan University; Bar Ilan University; Hebrew University of Jerusalem; Georgetown University; Bar Ilan University", "aff_domain": "biu.ac.il;gmail.com;mail.huji.ac.il;georgetown.edu;biu.ac.il", "email": "biu.ac.il;gmail.com;mail.huji.ac.il;georgetown.edu;biu.ac.il", "github": "", "project": "", "author_num": 5, "aff_unique_index": "0;0;1;2;0", "aff_unique_norm": "Bar Ilan University;Hebrew University of Jerusalem;Georgetown University", "aff_unique_dep": ";;", "aff_unique_url": "https://www.biu.ac.il;https://www.huji.ac.il;https://www.georgetown.edu", "aff_unique_abbr": "BIU;HUJI;GU", "aff_campus_unique_index": "1", "aff_campus_unique": ";Jerusalem", "aff_country_unique_index": "0;0;0;1;0", "aff_country_unique": "Israel;United States" }, { "id": "2021.acl-long.544", "title": "The R-U-A-Robot Dataset: Helping Avoid Chatbot Deception by Detecting User Questions About Human or Non-Human Identity", "track": "main", "status": "Long", "award": false, "abstract": "Humans are increasingly interacting with machines through language, sometimes in contexts where the user may not know they are talking to a machine (like over the phone or a text chatbot). We aim to understand how system designers and researchers might allow their systems to confirm its non-human identity. We collect over 2,500 phrasings related to the intent of \u201cAre you a robot?\u201d. This is paired with over 2,500 adversarially selected utterances where only confirming the system is non-human would be insufficient or disfluent. We compare classifiers to recognize the intent and discuss the precision/recall and model complexity tradeoffs. Such classifiers could be integrated into dialog systems to avoid undesired deception. We then explore how both a generative research model (Blender) as well as two deployed systems (Amazon Alexa, Google Assistant) handle this intent, finding that systems often fail to confirm their non-human identity. Finally, we try to understand what a good response to the intent would be, and conduct a user study to compare the important aspects when responding to this intent.", "author": "David Gros; Yu Li; Zhou Yu", "authorids": "/d/david-gros/; /y/yu-li/; /z/zhou-yu/", "bibtex": "@inproceedings{gros-etal-2021-r,\n title = \"The {R}-{U}-A-Robot Dataset: Helping Avoid Chatbot Deception by Detecting User Questions About Human or Non-Human Identity\",\n author = \"Gros, David and\n Li, Yu and\n Yu, Zhou\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.544/\",\n doi = \"10.18653/v1/2021.acl-long.544\",\n pages = \"6999--7013\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.544.pdf", "site": "https://aclanthology.org/2021.acl-long.544/", "pdf_size": 2369259, "gs_citation": 19, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=16957937960457828963&as_sdt=5,24&sciodt=0,24&hl=en", "gs_version_total": 4, "aff": "Computer Science Dept., University of California, Davis; Computer Science Dept., University of California, Davis; Computer Science Dept., Columbia University", "aff_domain": "ucdavis.edu;ucdavis.edu;columbia.edu", "email": "ucdavis.edu;ucdavis.edu;columbia.edu", "github": "", "project": "", "author_num": 3, "aff_unique_index": "0;0;1", "aff_unique_norm": "University of California, Davis;Columbia University", "aff_unique_dep": "Computer Science Dept.;Computer Science Dept.", "aff_unique_url": "https://www.ucdavis.edu;https://www.columbia.edu", "aff_unique_abbr": "UC Davis;Columbia", "aff_campus_unique_index": "0;0", "aff_campus_unique": "Davis;", "aff_country_unique_index": "0;0;0", "aff_country_unique": "United States" }, { "id": "2021.acl-long.533", "title": "The statistical advantage of automatic NLG metrics at the system level", "track": "main", "status": "Long", "award": false, "abstract": "Estimating the expected output quality of generation systems is central to NLG. This paper qualifies the notion that automatic metrics are not as good as humans in estimating system-level quality. Statistically, humans are unbiased, high variance estimators, while metrics are biased, low variance estimators. We compare these estimators by their error in pairwise prediction (which generation system is better?) using the bootstrap. Measuring this error is complicated: predictions are evaluated against noisy, human predicted labels instead of the ground truth, and metric predictions fluctuate based on the test sets they were calculated on. By applying a bias-variance-noise decomposition, we adjust this error to a noise-free, infinite test set setting. Our analysis compares the adjusted error of metrics to humans and a derived, perfect segment-level annotator, both of which are unbiased estimators dependent on the number of judgments collected. In MT, we identify two settings where metrics outperform humans due to a statistical advantage in variance: when the number of human judgments used is small, and when the quality difference between compared systems is small.", "author": "Johnny Wei; Robin Jia", "authorids": "/j/johnny-wei/; /r/robin-jia/", "bibtex": "@inproceedings{wei-jia-2021-statistical,\n title = \"The statistical advantage of automatic {NLG} metrics at the system level\",\n author = \"Wei, Johnny and\n Jia, Robin\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.533/\",\n doi = \"10.18653/v1/2021.acl-long.533\",\n pages = \"6840--6854\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.533.pdf", "site": "https://aclanthology.org/2021.acl-long.533/", "pdf_size": 578938, "gs_citation": 19, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=4321093582375457953&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 3, "aff": "Department of Computer Science, University of Southern California; Department of Computer Science, University of Southern California", "aff_domain": "usc.edu;usc.edu", "email": "usc.edu;usc.edu", "github": "https://github.com/johntzwei/metric-statistical-advantage", "project": "", "author_num": 2, "aff_unique_index": "0;0", "aff_unique_norm": "University of Southern California", "aff_unique_dep": "Department of Computer Science", "aff_unique_url": "https://www.usc.edu", "aff_unique_abbr": "USC", "aff_campus_unique_index": "0;0", "aff_campus_unique": "Los Angeles", "aff_country_unique_index": "0;0", "aff_country_unique": "United States" }, { "id": "2021.acl-short.126", "title": "Three Sentences Are All You Need: Local Path Enhanced Document Relation Extraction", "track": "main", "status": "Short", "award": false, "abstract": "Document-level Relation Extraction (RE) is a more challenging task than sentence RE as it often requires reasoning over multiple sentences. Yet, human annotators usually use a small number of sentences to identify the relationship between a given entity pair. In this paper, we present an embarrassingly simple but effective method to heuristically select evidence sentences for document-level RE, which can be easily combined with BiLSTM to achieve good performance on benchmark datasets, even better than fancy graph neural network based methods. We have released our code at https://github.com/AndrewZhe/Three-Sentences-Are-All-You-Need.", "author": "Quzhe Huang; Shengqi Zhu; Yansong Feng; Yuan Ye; Yuxuan Lai; Dongyan Zhao", "authorids": "/q/quzhe-huang/; /s/shengqi-zhu/; /y/yansong-feng/; /y/yuan-ye/; /y/yuxuan-lai/; /d/dongyan-zhao/", "bibtex": "@inproceedings{huang-etal-2021-three,\n title = \"Three Sentences Are All You Need: Local Path Enhanced Document Relation Extraction\",\n author = \"Huang, Quzhe and\n Zhu, Shengqi and\n Feng, Yansong and\n Ye, Yuan and\n Lai, Yuxuan and\n Zhao, Dongyan\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-short.126/\",\n doi = \"10.18653/v1/2021.acl-short.126\",\n pages = \"998--1004\"\n}", "pdf": "https://aclanthology.org/2021.acl-short.126.pdf", "site": "https://aclanthology.org/2021.acl-short.126/", "pdf_size": 472961, "gs_citation": 66, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=7411744823346015693&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 5, "aff": "Wangxuan Institute of Computer Technology, Peking University, China; The MOE Key Laboratory of Computational Linguistics, Peking University, China; Wangxuan Institute of Computer Technology, Peking University, China; Wangxuan Institute of Computer Technology, Peking University, China; Wangxuan Institute of Computer Technology, Peking University, China; Wangxuan Institute of Computer Technology, Peking University, China", "aff_domain": "pku.edu.cn;pku.edu.cn;pku.edu.cn;pku.edu.cn;pku.edu.cn;pku.edu.cn", "email": "pku.edu.cn;pku.edu.cn;pku.edu.cn;pku.edu.cn;pku.edu.cn;pku.edu.cn", "github": "https://github.com/AndrewZhe/Three-Sentences-Are-All-You-Need", "project": "", "author_num": 6, "aff_unique_index": "0;0;0;0;0;0", "aff_unique_norm": "Peking University", "aff_unique_dep": "Wangxuan Institute of Computer Technology", "aff_unique_url": "http://www.pku.edu.cn", "aff_unique_abbr": "PKU", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0;0;0;0;0;0", "aff_country_unique": "China" }, { "id": "2021.acl-long.55", "title": "TicketTalk: Toward human-level performance with end-to-end, transaction-based dialog systems", "track": "main", "status": "Long", "award": false, "abstract": "We present a data-driven, end-to-end approach to transaction-based dialog systems that performs at near-human levels in terms of verbal response quality and factual grounding accuracy. We show that two essential components of the system produce these results: a sufficiently large and diverse, in-domain labeled dataset, and a neural network-based, pre-trained model that generates both verbal responses and API call predictions. In terms of data, we introduce TicketTalk, a movie ticketing dialog dataset with 23,789 annotated conversations. The conversations range from completely open-ended and unrestricted to more structured, both in terms of their knowledge base, discourse features, and number of turns. In qualitative human evaluations, model-generated responses trained on just 10,000 TicketTalk dialogs were rated to \u201cmake sense\u201d 86.5% of the time, almost the same as human responses in the same contexts. Our simple, API-focused annotation schema results in a much easier labeling task making it faster and more cost effective. It is also the key component for being able to predict API calls accurately. We handle factual grounding by incorporating API calls in the training data, allowing our model to learn which actions to take and when. Trained on the same 10,000-dialog set, the model\u2019s API call predictions were rated to be correct 93.9% of the time in our evaluations, surpassing the ratings for the corresponding human labels. We show how API prediction and response generation scores improve as the dataset size incrementally increases from 5000 to 21,000 dialogs. Our analysis also clearly illustrates the benefits of pre-training. To facilitate future work on transaction-based dialog systems, we are publicly releasing the TicketTalk dataset at https://git.io/JL8an.", "author": "Bill Byrne; Karthik Krishnamoorthi; Saravanan Ganesh; Mihir Kale", "authorids": "/b/bill-byrne-ucsd/; /k/karthik-krishnamoorthi/; /s/saravanan-ganesh/; /m/mihir-kale/", "bibtex": "@inproceedings{byrne-etal-2021-tickettalk,\n title = \"{T}icket{T}alk: Toward human-level performance with end-to-end, transaction-based dialog systems\",\n author = \"Byrne, Bill and\n Krishnamoorthi, Karthik and\n Ganesh, Saravanan and\n Kale, Mihir\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.55/\",\n doi = \"10.18653/v1/2021.acl-long.55\",\n pages = \"671--680\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.55.pdf", "site": "https://aclanthology.org/2021.acl-long.55/", "pdf_size": 358433, "gs_citation": 26, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=9296461405253680592&as_sdt=5,24&sciodt=0,24&hl=en", "gs_version_total": 6, "aff": "Google, Mountain View, CA; Google, Mountain View, CA; Google, Mountain View, CA; Google, Mountain View, CA", "aff_domain": "google.com;google.com;google.com;google.com", "email": "google.com;google.com;google.com;google.com", "github": "", "project": "https://git.io/JL8an", "author_num": 4, "aff_unique_index": "0;0;0;0", "aff_unique_norm": "Google", "aff_unique_dep": "", "aff_unique_url": "https://www.google.com", "aff_unique_abbr": "Google", "aff_campus_unique_index": "0;0;0;0", "aff_campus_unique": "Mountain View", "aff_country_unique_index": "0;0;0;0", "aff_country_unique": "United States" }, { "id": "2021.acl-long.78", "title": "To POS Tag or Not to POS Tag: The Impact of POS Tags on Morphological Learning in Low-Resource Settings", "track": "main", "status": "Long", "award": false, "abstract": "Part-of-Speech (POS) tags are routinely included as features in many NLP tasks. However, the importance and usefulness of POS tags needs to be examined as NLP expands to low-resource languages because linguists who provide many annotated resources do not place priority on early identification and tagging of POS. This paper describes an empirical study about the effect that POS tags have on two computational morphological tasks with the Transformer architecture. Each task is tested twice on identical data except for the presence/absence of POS tags, using published data in ten high- to low-resource languages or unpublished linguistic field data in five low-resource languages. We find that the presence or absence of POS tags does not have a significant bearing on performance. In joint segmentation and glossing, the largest average difference is an .09 improvement in F1-scores by removing POS tags. In reinflection, the greatest average difference is 1.2% in accuracy for published data and 5% for unpublished and noisy field data.", "author": "Sarah Moeller; Ling Liu; Mans Hulden", "authorids": "/s/sarah-moeller/; /l/ling-liu/; /m/mans-hulden/", "bibtex": "@inproceedings{moeller-etal-2021-pos,\n title = \"To {POS} Tag or Not to {POS} Tag: The Impact of {POS} Tags on Morphological Learning in Low-Resource Settings\",\n author = \"Moeller, Sarah and\n Liu, Ling and\n Hulden, Mans\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.78/\",\n doi = \"10.18653/v1/2021.acl-long.78\",\n pages = \"966--978\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.78.pdf", "site": "https://aclanthology.org/2021.acl-long.78/", "pdf_size": 442120, "gs_citation": 10, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=4163276566187033523&as_sdt=5,31&sciodt=0,31&hl=en", "gs_version_total": 2, "aff": "University of Colorado Boulder; University of Colorado Boulder; University of Colorado Boulder", "aff_domain": "colorado.edu;colorado.edu;colorado.edu", "email": "colorado.edu;colorado.edu;colorado.edu", "github": "", "project": "", "author_num": 3, "aff_unique_index": "0;0;0", "aff_unique_norm": "University of Colorado", "aff_unique_dep": "", "aff_unique_url": "https://www.colorado.edu", "aff_unique_abbr": "CU Boulder", "aff_campus_unique_index": "0;0;0", "aff_campus_unique": "Boulder", "aff_country_unique_index": "0;0;0", "aff_country_unique": "United States" }, { "id": "2021.acl-long.128", "title": "Topic-Aware Evidence Reasoning and Stance-Aware Aggregation for Fact Verification", "track": "main", "status": "Long", "award": false, "abstract": "Fact verification is a challenging task that requires simultaneously reasoning and aggregating over multiple retrieved pieces of evidence to evaluate the truthfulness of a claim. Existing approaches typically (i) explore the semantic interaction between the claim and evidence at different granularity levels but fail to capture their topical consistency during the reasoning process, which we believe is crucial for verification; (ii) aggregate multiple pieces of evidence equally without considering their implicit stances to the claim, thereby introducing spurious information. To alleviate the above issues, we propose a novel topic-aware evidence reasoning and stance-aware aggregation model for more accurate fact verification, with the following four key properties: 1) checking topical consistency between the claim and evidence; 2) maintaining topical coherence among multiple pieces of evidence; 3) ensuring semantic similarity between the global topic information and the semantic representation of evidence; 4) aggregating evidence based on their implicit stances to the claim. Extensive experiments conducted on the two benchmark datasets demonstrate the superiority of the proposed model over several state-of-the-art approaches for fact verification. The source code can be obtained from https://github.com/jasenchn/TARSA.", "author": "Jiasheng Si; Deyu Zhou; Tongzhe Li; Xingyu Shi; Yulan He", "authorids": "/j/jiasheng-si/; /d/deyu-zhou/; /t/tongzhe-li/; /x/xingyu-shi/; /y/yulan-he/", "bibtex": "@inproceedings{si-etal-2021-topic,\n title = \"Topic-Aware Evidence Reasoning and Stance-Aware Aggregation for Fact Verification\",\n author = \"Si, Jiasheng and\n Zhou, Deyu and\n Li, Tongzhe and\n Shi, Xingyu and\n He, Yulan\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.128/\",\n doi = \"10.18653/v1/2021.acl-long.128\",\n pages = \"1612--1622\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.128.pdf", "site": "https://aclanthology.org/2021.acl-long.128/", "pdf_size": 704833, "gs_citation": 42, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=17540542691981156529&as_sdt=20000005&sciodt=0,21&hl=en", "gs_version_total": 8, "aff": "School of Computer Science and Engineering, Key Laboratory of Computer Network and Information Integration, Ministry of Education, Southeast University, China; School of Computer Science and Engineering, Key Laboratory of Computer Network and Information Integration, Ministry of Education, Southeast University, China; School of Computer Science and Engineering, Key Laboratory of Computer Network and Information Integration, Ministry of Education, Southeast University, China; School of Computer Science and Engineering, Key Laboratory of Computer Network and Information Integration, Ministry of Education, Southeast University, China; Department of Computer Science, University of Warwick, UK", "aff_domain": "seu.edu.cn;seu.edu.cn;seu.edu.cn;seu.edu.cn;warwick.ac.uk", "email": "seu.edu.cn;seu.edu.cn;seu.edu.cn;seu.edu.cn;warwick.ac.uk", "github": "https://github.com/jasenchn/TARSA", "project": "", "author_num": 5, "aff_unique_index": "0;0;0;0;1", "aff_unique_norm": "Southeast University;University of Warwick", "aff_unique_dep": "School of Computer Science and Engineering;Department of Computer Science", "aff_unique_url": "https://www.seu.edu.cn/;https://warwick.ac.uk", "aff_unique_abbr": "SEU;Warwick", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0;0;0;0;1", "aff_country_unique": "China;United Kingdom" }, { "id": "2021.acl-long.125", "title": "Topic-Driven and Knowledge-Aware Transformer for Dialogue Emotion Detection", "track": "main", "status": "Long", "award": false, "abstract": "Emotion detection in dialogues is challenging as it often requires the identification of thematic topics underlying a conversation, the relevant commonsense knowledge, and the intricate transition patterns between the affective states. In this paper, we propose a Topic-Driven Knowledge-Aware Transformer to handle the challenges above. We firstly design a topic-augmented language model (LM) with an additional layer specialized for topic detection. The topic-augmented LM is then combined with commonsense statements derived from a knowledge base based on the dialogue contextual information. Finally, a transformer-based encoder-decoder architecture fuses the topical and commonsense information, and performs the emotion label sequence prediction. The model has been experimented on four datasets in dialogue emotion detection, demonstrating its superiority empirically over the existing state-of-the-art approaches. Quantitative and qualitative results show that the model can discover topics which help in distinguishing emotion categories.", "author": "Lixing Zhu; Gabriele Pergola; Lin Gui; Deyu Zhou; Yulan He", "authorids": "/l/lixing-zhu/; /g/gabriele-pergola/; /l/lin-gui/; /d/deyu-zhou/; /y/yulan-he/", "bibtex": "@inproceedings{zhu-etal-2021-topic,\n title = \"Topic-Driven and Knowledge-Aware Transformer for Dialogue Emotion Detection\",\n author = \"Zhu, Lixing and\n Pergola, Gabriele and\n Gui, Lin and\n Zhou, Deyu and\n He, Yulan\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.125/\",\n doi = \"10.18653/v1/2021.acl-long.125\",\n pages = \"1571--1582\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.125.pdf", "site": "https://aclanthology.org/2021.acl-long.125/", "pdf_size": 2071427, "gs_citation": 172, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=7156314179910100702&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 12, "aff": "Department of Computer Science, University of Warwick, UK; Department of Computer Science, University of Warwick, UK; Department of Computer Science, University of Warwick, UK; School of Computer Science and Engineering, Key Laboratory of Computer Network and Information Integration, Ministry of Education, Southeast University, China; Department of Computer Science, University of Warwick, UK", "aff_domain": "warwick.ac.uk;warwick.ac.uk;warwick.ac.uk;seu.edu.cn;warwick.ac.uk", "email": "warwick.ac.uk;warwick.ac.uk;warwick.ac.uk;seu.edu.cn;warwick.ac.uk", "github": "", "project": "", "author_num": 5, "aff_unique_index": "0;0;0;1;0", "aff_unique_norm": "University of Warwick;Southeast University", "aff_unique_dep": "Department of Computer Science;School of Computer Science and Engineering", "aff_unique_url": "https://warwick.ac.uk;https://www.seu.edu.cn/", "aff_unique_abbr": "Warwick;SEU", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0;0;0;1;0", "aff_country_unique": "United Kingdom;China" }, { "id": "2021.acl-long.107", "title": "Towards Argument Mining for Social Good: A Survey", "track": "main", "status": "Long", "award": false, "abstract": "This survey builds an interdisciplinary picture of Argument Mining (AM), with a strong focus on its potential to address issues related to Social and Political Science. More specifically, we focus on AM challenges related to its applications to social media and in the multilingual domain, and then proceed to the widely debated notion of argument quality. We propose a novel definition of argument quality which is integrated with that of deliberative quality from the Social Science literature. Under our definition, the quality of a contribution needs to be assessed at multiple levels: the contribution itself, its preceding context, and the consequential effect on the development of the upcoming discourse. The latter has not received the deserved attention within the community. We finally define an application of AM for Social Good: (semi-)automatic moderation, a highly integrative application which (a) represents a challenging testbed for the integrated notion of quality we advocate, (b) allows the empirical quantification of argument/deliberative quality to benefit from the developments in other NLP fields (i.e. hate speech detection, fact checking, debiasing), and (c) has a clearly beneficial potential at the level of its societal thanks to its real-world application (even if extremely ambitious).", "author": "Eva Maria Vecchi; Neele Falk; Iman Jundi; Gabriella Lapesa", "authorids": "/e/eva-maria-vecchi/; /n/neele-falk/; /i/iman-jundi/; /g/gabriella-lapesa/", "bibtex": "@inproceedings{vecchi-etal-2021-towards,\n title = \"Towards Argument Mining for Social Good: A Survey\",\n author = \"Vecchi, Eva Maria and\n Falk, Neele and\n Jundi, Iman and\n Lapesa, Gabriella\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.107/\",\n doi = \"10.18653/v1/2021.acl-long.107\",\n pages = \"1338--1352\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.107.pdf", "site": "https://aclanthology.org/2021.acl-long.107/", "pdf_size": 340370, "gs_citation": 53, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=2301843612645985631&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 2, "aff": "Institute for Natural Language Processing, University of Stuttgart (Germany); Institute for Natural Language Processing, University of Stuttgart (Germany); Institute for Natural Language Processing, University of Stuttgart (Germany); Institute for Natural Language Processing, University of Stuttgart (Germany)", "aff_domain": "ims.uni-stuttgart.de;ims.uni-stuttgart.de;ims.uni-stuttgart.de;ims.uni-stuttgart.de", "email": "ims.uni-stuttgart.de;ims.uni-stuttgart.de;ims.uni-stuttgart.de;ims.uni-stuttgart.de", "github": "", "project": "", "author_num": 4, "aff_unique_index": "0;0;0;0", "aff_unique_norm": "University of Stuttgart", "aff_unique_dep": "Institute for Natural Language Processing", "aff_unique_url": "https://www.uni-stuttgart.de", "aff_unique_abbr": "", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0;0;0;0", "aff_country_unique": "Germany" }, { "id": "2021.acl-long.269", "title": "Towards Emotional Support Dialog Systems", "track": "main", "status": "Long", "award": false, "abstract": "Emotional support is a crucial ability for many conversation scenarios, including social interactions, mental health support, and customer service chats. Following reasonable procedures and using various support skills can help to effectively provide support. However, due to the lack of a well-designed task and corpora of effective emotional support conversations, research on building emotional support into dialog systems remains lacking. In this paper, we define the Emotional Support Conversation (ESC) task and propose an ESC Framework, which is grounded on the Helping Skills Theory. We construct an Emotion Support Conversation dataset (ESConv) with rich annotation (especially support strategy) in a help-seeker and supporter mode. To ensure a corpus of high-quality conversations that provide examples of effective emotional support, we take extensive effort to design training tutorials for supporters and several mechanisms for quality control during data collection. Finally, we evaluate state-of-the-art dialog models with respect to the ability to provide emotional support. Our results show the importance of support strategies in providing effective emotional support and the utility of ESConv in training more emotional support systems.", "author": "Siyang Liu; Chujie Zheng; Orianna Demasi; Sahand Sabour; Yu Li; Zhou Yu; Yong Jiang; Minlie Huang", "authorids": "/s/siyang-liu/; /c/chujie-zheng/; /o/orianna-demasi/; /s/sahand-sabour/; /y/yu-li/; /z/zhou-yu/; /y/yong-jiang/; /m/minlie-huang/", "bibtex": "@inproceedings{liu-etal-2021-towards,\n title = \"Towards Emotional Support Dialog Systems\",\n author = \"Liu, Siyang and\n Zheng, Chujie and\n Demasi, Orianna and\n Sabour, Sahand and\n Li, Yu and\n Yu, Zhou and\n Jiang, Yong and\n Huang, Minlie\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.269/\",\n doi = \"10.18653/v1/2021.acl-long.269\",\n pages = \"3469--3483\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.269.pdf", "site": "https://aclanthology.org/2021.acl-long.269/", "pdf_size": 2745468, "gs_citation": 306, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=10570784475640012249&as_sdt=400005&sciodt=0,14&hl=en", "gs_version_total": 7, "aff": "The CoAI group, DCST, Institute for Artificial Intelligence, State Key Lab of Intelligent Technology and Systems, Beijing National Research Center for Information Science and Technology, Tsinghua University, Beijing 100084, China+Tsinghua-Berkeley Shenzhen Institute, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, China; The CoAI group, DCST, Institute for Artificial Intelligence, State Key Lab of Intelligent Technology and Systems, Beijing National Research Center for Information Science and Technology, Tsinghua University, Beijing 100084, China+Tsinghua-Berkeley Shenzhen Institute, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, China; University of California, Davis; The CoAI group, DCST, Institute for Artificial Intelligence, State Key Lab of Intelligent Technology and Systems, Beijing National Research Center for Information Science and Technology, Tsinghua University, Beijing 100084, China; Columbia University; Tsinghua-Berkeley Shenzhen Institute, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, China; Tsinghua-Berkeley Shenzhen Institute, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, China; The CoAI group, DCST, Institute for Artificial Intelligence, State Key Lab of Intelligent Technology and Systems, Beijing National Research Center for Information Science and Technology, Tsinghua University, Beijing 100084, China", "aff_domain": "mails.tsinghua.edu.cn;gmail.com;tsinghua.edu.cn; ; ; ; ;", "email": "mails.tsinghua.edu.cn;gmail.com;tsinghua.edu.cn; ; ; ; ;", "github": "https://github.com/thu-coai/Emotional-Support-Conversation", "project": "", "author_num": 8, "aff_unique_index": "0+0;0+0;1;0;2;0;0;0", "aff_unique_norm": "Tsinghua University;University of California, Davis;Columbia University", "aff_unique_dep": "Institute for Artificial Intelligence;;", "aff_unique_url": "https://www.tsinghua.edu.cn;https://www.ucdavis.edu;https://www.columbia.edu", "aff_unique_abbr": "Tsinghua;UC Davis;Columbia", "aff_campus_unique_index": "0+1;0+1;2;0;1;1;0", "aff_campus_unique": "Beijing;Shenzhen;Davis;", "aff_country_unique_index": "0+0;0+0;1;0;1;0;0;0", "aff_country_unique": "China;United States" }, { "id": "2021.acl-short.64", "title": "Towards Generative Aspect-Based Sentiment Analysis", "track": "main", "status": "Short", "award": false, "abstract": "Aspect-based sentiment analysis (ABSA) has received increasing attention recently. Most existing work tackles ABSA in a discriminative manner, designing various task-specific classification networks for the prediction. Despite their effectiveness, these methods ignore the rich label semantics in ABSA problems and require extensive task-specific designs. In this paper, we propose to tackle various ABSA tasks in a unified generative framework. Two types of paradigms, namely annotation-style and extraction-style modeling, are designed to enable the training process by formulating each ABSA task as a text generation problem. We conduct experiments on four ABSA tasks across multiple benchmark datasets where our proposed generative approach achieves new state-of-the-art results in almost all cases. This also validates the strong generality of the proposed framework which can be easily adapted to arbitrary ABSA task without additional task-specific model design.", "author": "Wenxuan Zhang; Xin Li; Yang Deng; Lidong Bing; Wai Lam", "authorids": "/w/wenxuan-zhang/; /x/xin-li/; /y/yang-deng/; /l/lidong-bing/; /w/wai-lam/", "bibtex": "@inproceedings{zhang-etal-2021-towards-generative,\n title = \"Towards Generative Aspect-Based Sentiment Analysis\",\n author = \"Zhang, Wenxuan and\n Li, Xin and\n Deng, Yang and\n Bing, Lidong and\n Lam, Wai\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-short.64/\",\n doi = \"10.18653/v1/2021.acl-short.64\",\n pages = \"504--510\"\n}", "pdf": "https://aclanthology.org/2021.acl-short.64.pdf", "site": "https://aclanthology.org/2021.acl-short.64/", "pdf_size": 259864, "gs_citation": 271, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=2554136643422973478&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 5, "aff": "The Chinese University of Hong Kong; DAMO Academy, Alibaba Group; The Chinese University of Hong Kong; DAMO Academy, Alibaba Group; The Chinese University of Hong Kong", "aff_domain": "se.cuhk.edu.hk;alibaba-inc.com;se.cuhk.edu.hk;alibaba-inc.com;se.cuhk.edu.hk", "email": "se.cuhk.edu.hk;alibaba-inc.com;se.cuhk.edu.hk;alibaba-inc.com;se.cuhk.edu.hk", "github": "https://github.com/IsakZhang/Generative-ABSA", "project": "", "author_num": 5, "aff_unique_index": "0;1;0;1;0", "aff_unique_norm": "The Chinese University of Hong Kong;Alibaba Group", "aff_unique_dep": ";DAMO Academy", "aff_unique_url": "https://www.cuhk.edu.hk;https://www.alibaba-group.com", "aff_unique_abbr": "CUHK;Alibaba", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0;0;0;0;0", "aff_country_unique": "China" }, { "id": "2021.acl-long.297", "title": "Towards Propagation Uncertainty: Edge-enhanced Bayesian Graph Convolutional Networks for Rumor Detection", "track": "main", "status": "Long", "award": false, "abstract": "Detecting rumors on social media is a very critical task with significant implications to the economy, public health, etc. Previous works generally capture effective features from texts and the propagation structure. However, the uncertainty caused by unreliable relations in the propagation structure is common and inevitable due to wily rumor producers and the limited collection of spread data. Most approaches neglect it and may seriously limit the learning of features. Towards this issue, this paper makes the first attempt to explore propagation uncertainty for rumor detection. Specifically, we propose a novel Edge-enhanced Bayesian Graph Convolutional Network (EBGCN) to capture robust structural features. The model adaptively rethinks the reliability of latent relations by adopting a Bayesian approach. Besides, we design a new edge-wise consistency training framework to optimize the model by enforcing consistency on relations. Experiments on three public benchmark datasets demonstrate that the proposed model achieves better performance than baseline methods on both rumor detection and early rumor detection tasks.", "author": "Lingwei Wei; Dou Hu; Wei Zhou; Zhaojuan Yue; Songlin Hu", "authorids": "/l/lingwei-wei/; /d/dou-hu/; /w/wei-zhou/; /z/zhaojuan-yue/; /s/songlin-hu/", "bibtex": "@inproceedings{wei-etal-2021-towards,\n title = \"Towards Propagation Uncertainty: Edge-enhanced {B}ayesian Graph Convolutional Networks for Rumor Detection\",\n author = \"Wei, Lingwei and\n Hu, Dou and\n Zhou, Wei and\n Yue, Zhaojuan and\n Hu, Songlin\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.297/\",\n doi = \"10.18653/v1/2021.acl-long.297\",\n pages = \"3845--3854\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.297.pdf", "site": "https://aclanthology.org/2021.acl-long.297/", "pdf_size": 2369778, "gs_citation": 126, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=11453900104984449820&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 8, "aff": "Institute of Information Engineering, Chinese Academy of Sciences+School of Cyber Security, University of Chinese Academy of Sciences; National Computer System Engineering Research Institute of China; Institute of Information Engineering, Chinese Academy of Sciences; Computer Network Information Center, Chinese Academy of Sciences; Institute of Information Engineering, Chinese Academy of Sciences+School of Cyber Security, University of Chinese Academy of Sciences", "aff_domain": "mails.ucas.edu.cn;mails.ucas.edu.cn;iie.ac.cn;cnic.cn;iie.ac.cn", "email": "mails.ucas.edu.cn;mails.ucas.edu.cn;iie.ac.cn;cnic.cn;iie.ac.cn", "github": "", "project": "", "author_num": 5, "aff_unique_index": "0+1;2;0;0;0+1", "aff_unique_norm": "Chinese Academy of Sciences;University of Chinese Academy of Sciences;National Computer System Engineering Research Institute", "aff_unique_dep": "Institute of Information Engineering;School of Cyber Security;Computer System Engineering", "aff_unique_url": "http://www.cas.cn;http://www.ucas.ac.cn;", "aff_unique_abbr": "CAS;UCAS;", "aff_campus_unique_index": ";", "aff_campus_unique": "", "aff_country_unique_index": "0+0;0;0;0;0+0", "aff_country_unique": "China" }, { "id": "2021.acl-long.211", "title": "Towards Quantifiable Dialogue Coherence Evaluation", "track": "main", "status": "Long", "award": false, "abstract": "Automatic dialogue coherence evaluation has attracted increasing attention and is crucial for developing promising dialogue systems. However, existing metrics have two major limitations: (a) they are mostly trained in a simplified two-level setting (coherent vs. incoherent), while humans give Likert-type multi-level coherence scores, dubbed as \u201cquantifiable\u201d; (b) their predicted coherence scores cannot align with the actual human rating standards due to the absence of human guidance during training. To address these limitations, we propose Quantifiable Dialogue Coherence Evaluation (QuantiDCE), a novel framework aiming to train a quantifiable dialogue coherence metric that can reflect the actual human rating standards. Specifically, QuantiDCE includes two training stages, Multi-Level Ranking (MLR) pre-training and Knowledge Distillation (KD) fine-tuning. During MLR pre-training, a new MLR loss is proposed for enabling the model to learn the coarse judgement of coherence degrees. Then, during KD fine-tuning, the pretrained model is further finetuned to learn the actual human rating standards with only very few human-annotated data. To advocate the generalizability even with limited fine-tuning data, a novel KD regularization is introduced to retain the knowledge learned at the pre-training stage. Experimental results show that the model trained by QuantiDCE presents stronger correlations with human judgements than the other state-of-the-art metrics.", "author": "Zheng Ye; Liucun Lu; Lishan Huang; Liang Lin; Xiaodan Liang", "authorids": "/z/zheng-ye/; /l/liucun-lu/; /l/lishan-huang/; /l/liang-lin/; /x/xiaodan-liang/", "bibtex": "@inproceedings{ye-etal-2021-towards-quantifiable,\n title = \"Towards Quantifiable Dialogue Coherence Evaluation\",\n author = \"Ye, Zheng and\n Lu, Liucun and\n Huang, Lishan and\n Lin, Liang and\n Liang, Xiaodan\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.211/\",\n doi = \"10.18653/v1/2021.acl-long.211\",\n pages = \"2718--2729\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.211.pdf", "site": "https://aclanthology.org/2021.acl-long.211/", "pdf_size": 1444448, "gs_citation": 26, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=3570490235304460113&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 5, "aff": "Shenzhen Campus of Sun Yat-sen University; Shenzhen Campus of Sun Yat-sen University; Sun Yat-Sen University; Sun Yat-Sen University+Dark Matter AI Inc.; Shenzhen Campus of Sun Yat-sen University", "aff_domain": "mail2.sysu.edu.cn;mail2.sysu.edu.cn;mail2.sysu.edu.cn;ieee.org;gmail.com", "email": "mail2.sysu.edu.cn;mail2.sysu.edu.cn;mail2.sysu.edu.cn;ieee.org;gmail.com", "github": "https://github.com/James-Yip/QuantiDCE", "project": "", "author_num": 5, "aff_unique_index": "0;0;1;1+2;0", "aff_unique_norm": "Sun Yat-sen University;Sun Yat-Sen University;Dark Matter AI Inc.", "aff_unique_dep": ";;", "aff_unique_url": "http://www.sysu.edu.cn/;http://www.sysu.edu.cn/;", "aff_unique_abbr": "SYSU;SYSU;", "aff_campus_unique_index": "0;0;;0", "aff_campus_unique": "Shenzhen;", "aff_country_unique_index": "0;0;0;0+1;0", "aff_country_unique": "China;United States" }, { "id": "2021.acl-long.195", "title": "Towards Robustness of Text-to-SQL Models against Synonym Substitution", "track": "main", "status": "Long", "award": false, "abstract": "Recently, there has been significant progress in studying neural networks to translate text descriptions into SQL queries. Despite achieving good performance on some public benchmarks, existing text-to-SQL models typically rely on the lexical matching between words in natural language (NL) questions and tokens in table schemas, which may render the models vulnerable to attacks that break the schema linking mechanism. In this work, we investigate the robustness of text-to-SQL models to synonym substitution. In particular, we introduce Spider-Syn, a human-curated dataset based on the Spider benchmark for text-to-SQL translation. NL questions in Spider-Syn are modified from Spider, by replacing their schema-related words with manually selected synonyms that reflect real-world question paraphrases. We observe that the accuracy dramatically drops by eliminating such explicit correspondence between NL questions and table schemas, even if the synonyms are not adversarially selected to conduct worst-case attacks. Finally, we present two categories of approaches to improve the model robustness. The first category of approaches utilizes additional synonym annotations for table schemas by modifying the model input, while the second category is based on adversarial training. We demonstrate that both categories of approaches significantly outperform their counterparts without the defense, and the first category of approaches are more effective.", "author": "Yujian Gan; Xinyun Chen; Qiuping Huang; Matthew Purver; John R. Woodward; Jinxia Xie; Pengsheng Huang", "authorids": "/y/yujian-gan/; /x/xinyun-chen/; /q/qiuping-huang/; /m/matthew-purver/; /j/john-r-woodward/; /j/jinxia-xie/; /p/pengsheng-huang/", "bibtex": "@inproceedings{gan-etal-2021-towards,\n title = \"Towards Robustness of Text-to-{SQL} Models against Synonym Substitution\",\n author = \"Gan, Yujian and\n Chen, Xinyun and\n Huang, Qiuping and\n Purver, Matthew and\n Woodward, John R. and\n Xie, Jinxia and\n Huang, Pengsheng\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.195/\",\n doi = \"10.18653/v1/2021.acl-long.195\",\n pages = \"2505--2515\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.195.pdf", "site": "https://aclanthology.org/2021.acl-long.195/", "pdf_size": 413146, "gs_citation": 125, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=3340495735201090316&as_sdt=5,44&sciodt=0,44&hl=en", "gs_version_total": 9, "aff": "Queen Mary University of London; UC Berkeley; Nanning Central Sub-branch of the People\u2019s Bank of China; Queen Mary University of London; Queen Mary University of London; Guangxi University of Finance and Economics; Beijing MeiyiLab Co.,Ltd.", "aff_domain": "qmul.ac.uk;qmul.ac.uk;qmul.ac.uk;berkeley.edu;foxmail.com;hotmail.com;pku.edu.cn", "email": "qmul.ac.uk;qmul.ac.uk;qmul.ac.uk;berkeley.edu;foxmail.com;hotmail.com;pku.edu.cn", "github": "https://github.com/ygan/Spider-Syn", "project": "", "author_num": 7, "aff_unique_index": "0;1;2;0;0;3;4", "aff_unique_norm": "Queen Mary University of London;University of California, Berkeley;People\u2019s Bank of China;Guangxi University of Finance and Economics;Beijing MeiyiLab Co., Ltd.", "aff_unique_dep": ";;;;", "aff_unique_url": "https://www.qmul.ac.uk;https://www.berkeley.edu;https://www.pbc.gov.cn;http://www.gxufe.edu.cn;", "aff_unique_abbr": "QMUL;UC Berkeley;;;", "aff_campus_unique_index": "0;1;2;0;0", "aff_campus_unique": "London;Berkeley;Nanning;", "aff_country_unique_index": "0;1;2;0;0;2;2", "aff_country_unique": "United Kingdom;United States;China" }, { "id": "2021.acl-long.115", "title": "Towards Table-to-Text Generation with Numerical Reasoning", "track": "main", "status": "Long", "award": false, "abstract": "Recent neural text generation models have shown significant improvement in generating descriptive text from structured data such as table formats. One of the remaining important challenges is generating more analytical descriptions that can be inferred from facts in a data source. The use of a template-based generator and a pointer-generator is among the potential alternatives for table-to-text generators. In this paper, we propose a framework consisting of a pre-trained model and a copy mechanism. The pre-trained models are fine-tuned to produce fluent text that is enriched with numerical reasoning. However, it still lacks fidelity to the table contents. The copy mechanism is incorporated in the fine-tuning step by using general placeholders to avoid producing hallucinated phrases that are not supported by a table while preserving high fluency. In summary, our contributions are (1) a new dataset for numerical table-to-text generation using pairs of a table and a paragraph of a table description with richer inference from scientific papers, and (2) a table-to-text generation framework enriched with numerical reasoning.", "author": "Lya Hulliyyatus Suadaa; Hidetaka Kamigaito; Kotaro Funakoshi; Manabu Okumura; Hiroya Takamura", "authorids": "/l/lya-hulliyyatus-suadaa/; /h/hidetaka-kamigaito/; /k/kotaro-funakoshi/; /m/manabu-okumura/; /h/hiroya-takamura/", "bibtex": "@inproceedings{suadaa-etal-2021-towards,\n title = \"Towards Table-to-Text Generation with Numerical Reasoning\",\n author = \"Suadaa, Lya Hulliyyatus and\n Kamigaito, Hidetaka and\n Funakoshi, Kotaro and\n Okumura, Manabu and\n Takamura, Hiroya\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.115/\",\n doi = \"10.18653/v1/2021.acl-long.115\",\n pages = \"1451--1465\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.115.pdf", "site": "https://aclanthology.org/2021.acl-long.115/", "pdf_size": 1039334, "gs_citation": 72, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=10673107870449391517&as_sdt=80005&sciodt=0,11&hl=en", "gs_version_total": 4, "aff": "Tokyo Institute of Technology; Tokyo Institute of Technology; Tokyo Institute of Technology; Tokyo Institute of Technology; Tokyo Institute of Technology+National Institute of Advanced Industrial Science and Technology (AIST)", "aff_domain": "stis.ac.id;lr.pi.titech.ac.jp;lr.pi.titech.ac.jp;lr.pi.titech.ac.jp;pi.titech.ac.jp", "email": "stis.ac.id;lr.pi.titech.ac.jp;lr.pi.titech.ac.jp;lr.pi.titech.ac.jp;pi.titech.ac.jp", "github": "https://github.com/titech-nlp/numeric-nlg", "project": "", "author_num": 5, "aff_unique_index": "0;0;0;0;0+1", "aff_unique_norm": "Tokyo Institute of Technology;National Institute of Advanced Industrial Science and Technology", "aff_unique_dep": ";", "aff_unique_url": "https://www.titech.ac.jp;https://www.aist.go.jp", "aff_unique_abbr": "Titech;AIST", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0;0;0;0;0+0", "aff_country_unique": "Japan" }, { "id": "2021.acl-long.310", "title": "Towards User-Driven Neural Machine Translation", "track": "main", "status": "Long", "award": false, "abstract": "A good translation should not only translate the original content semantically, but also incarnate personal traits of the original text. For a real-world neural machine translation (NMT) system, these user traits (e.g., topic preference, stylistic characteristics and expression habits) can be preserved in user behavior (e.g., historical inputs). However, current NMT systems marginally consider the user behavior due to: 1) the difficulty of modeling user portraits in zero-shot scenarios, and 2) the lack of user-behavior annotated parallel dataset. To fill this gap, we introduce a novel framework called user-driven NMT. Specifically, a cache-based module and a user-driven contrastive learning method are proposed to offer NMT the ability to capture potential user traits from their historical inputs under a zero-shot learning fashion. Furthermore, we contribute the first Chinese-English parallel corpus annotated with user behavior called UDT-Corpus. Experimental results confirm that the proposed user-driven NMT can generate user-specific translations.", "author": "Huan Lin; Liang Yao; Baosong Yang; Dayiheng Liu; Haibo Zhang; Weihua Luo; Degen Huang; Jinsong Su", "authorids": "/h/huan-lin/; /l/liang-yao/; /b/baosong-yang/; /d/dayiheng-liu/; /h/haibo-zhang/; /w/weihua-luo/; /d/degen-huang/; /j/jinsong-su/", "bibtex": "@inproceedings{lin-etal-2021-towards,\n title = \"Towards User-Driven Neural Machine Translation\",\n author = \"Lin, Huan and\n Yao, Liang and\n Yang, Baosong and\n Liu, Dayiheng and\n Zhang, Haibo and\n Luo, Weihua and\n Huang, Degen and\n Su, Jinsong\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.310/\",\n doi = \"10.18653/v1/2021.acl-long.310\",\n pages = \"4008--4018\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.310.pdf", "site": "https://aclanthology.org/2021.acl-long.310/", "pdf_size": 596399, "gs_citation": 21, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=8529844213208577662&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 4, "aff": "School of Informatics, Xiamen University+Institute of Artificial Intelligence, Xiamen University; Alibaba Group; Alibaba Group; Alibaba Group; Alibaba Group; Alibaba Group; Dalian University of Technology; School of Informatics, Xiamen University+Institute of Artificial Intelligence, Xiamen University+Pengcheng Lab", "aff_domain": "stu.xmu.edu.cn;alibaba-inc.com;alibaba-inc.com;alibaba-inc.com;alibaba-inc.com;alibaba-inc.com;dlut.edu.cn;xmu.edu.cn", "email": "stu.xmu.edu.cn;alibaba-inc.com;alibaba-inc.com;alibaba-inc.com;alibaba-inc.com;alibaba-inc.com;dlut.edu.cn;xmu.edu.cn", "github": "https://github.com/DeepLearnXMU/User-Driven-NMT", "project": "", "author_num": 8, "aff_unique_index": "0+0;1;1;1;1;1;2;0+0+3", "aff_unique_norm": "Xiamen University;Alibaba Group;Dalian University of Technology;Pengcheng Lab", "aff_unique_dep": "School of Informatics;;;", "aff_unique_url": "https://www.xmu.edu.cn;https://www.alibaba.com;http://www.dlut.edu.cn/;", "aff_unique_abbr": "XMU;Alibaba;DUT;", "aff_campus_unique_index": ";", "aff_campus_unique": "", "aff_country_unique_index": "0+0;0;0;0;0;0;0;0+0+0", "aff_country_unique": "China" }, { "id": "2021.acl-short.90", "title": "Towards Visual Question Answering on Pathology Images", "track": "main", "status": "Short", "award": false, "abstract": "Pathology imaging is broadly used for identifying the causes and effects of diseases or injuries. Given a pathology image, being able to answer questions about the clinical findings contained in the image is very important for medical decision making. In this paper, we aim to develop a pathological visual question answering framework to analyze pathology images and answer medical questions related to these images. To build such a framework, we create PathVQA, a VQA dataset with 32,795 questions asked from 4,998 pathology images. We also propose a three-level optimization framework which performs self-supervised pretraining and VQA finetuning end-to-end to learn powerful visual and textual representations jointly and automatically identifies and excludes noisy self-supervised examples from pretraining. We perform experiments on our created PathVQA dataset and the results demonstrate the effectiveness of our proposed methods. The datasets and code are available at https://github.com/UCSD-AI4H/PathVQA", "author": "Xuehai He; Zhuo Cai; Wenlan Wei; Yichen Zhang; Luntian Mou; Eric Xing; Pengtao Xie", "authorids": "/x/xuehai-he/; /z/zhuo-cai/; /w/wenlan-wei/; /y/yichen-zhang/; /l/luntian-mou/; /e/eric-xing/; /p/pengtao-xie/", "bibtex": "@inproceedings{he-etal-2021-towards,\n title = \"Towards Visual Question Answering on Pathology Images\",\n author = \"He, Xuehai and\n Cai, Zhuo and\n Wei, Wenlan and\n Zhang, Yichen and\n Mou, Luntian and\n Xing, Eric and\n Xie, Pengtao\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-short.90/\",\n doi = \"10.18653/v1/2021.acl-short.90\",\n pages = \"708--718\"\n}", "pdf": "https://aclanthology.org/2021.acl-short.90.pdf", "site": "https://aclanthology.org/2021.acl-short.90/", "pdf_size": 525410, "gs_citation": 56, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=1351731126387597079&as_sdt=5,31&sciodt=0,31&hl=en", "gs_version_total": 5, "aff": "UC San Diego; Tsinghua University; Wuhan University; Beijing University of Technology; MBZUAI and CMU; MBZUAI and CMU; UC San Diego", "aff_domain": "eng.ucsd.edu; ; ; ; ; ;eng.ucsd.edu", "email": "eng.ucsd.edu; ; ; ; ; ;eng.ucsd.edu", "github": "https://github.com/UCSD-AI4H/PathVQA", "project": "", "author_num": 7, "aff_unique_index": "0;1;2;3;4;4;0", "aff_unique_norm": "University of California, San Diego;Tsinghua University;Wuhan University;Beijing University of Technology;Mohamed Bin Zayed University of Artificial Intelligence", "aff_unique_dep": ";;;;", "aff_unique_url": "https://www.ucsd.edu;https://www.tsinghua.edu.cn;http://www.whu.edu.cn/;http://www.bjut.edu.cn;https://www.mbzuai.ac.ae", "aff_unique_abbr": "UCSD;THU;WHU;BJUT;MBZUAI", "aff_campus_unique_index": "0;0", "aff_campus_unique": "San Diego;", "aff_country_unique_index": "0;1;1;1;2;2;0", "aff_country_unique": "United States;China;United Arab Emirates" }, { "id": "2021.acl-short.130", "title": "Towards a more Robust Evaluation for Conversational Question Answering", "track": "main", "status": "Short", "award": false, "abstract": "With the explosion of chatbot applications, Conversational Question Answering (CQA) has generated a lot of interest in recent years. Among proposals, reading comprehension models which take advantage of the conversation history (previous QA) seem to answer better than those which only consider the current question. Nevertheless, we note that the CQA evaluation protocol has a major limitation. In particular, models are allowed, at each turn of the conversation, to access the ground truth answers of the previous turns. Not only does this severely prevent their applications in fully autonomous chatbots, it also leads to unsuspected biases in their behavior. In this paper, we highlight this effect and propose new tools for evaluation and training in order to guard against the noted issues. The new results that we bring come to reinforce methods of the current state of the art.", "author": "Wissam Siblini; Baris Sayil; Yacine Kessaci", "authorids": "/w/wissam-siblini/; /b/baris-sayil/; /y/yacine-kessaci/", "bibtex": "@inproceedings{siblini-etal-2021-towards,\n title = \"Towards a more Robust Evaluation for Conversational Question Answering\",\n author = \"Siblini, Wissam and\n Sayil, Baris and\n Kessaci, Yacine\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-short.130/\",\n doi = \"10.18653/v1/2021.acl-short.130\",\n pages = \"1028--1034\"\n}", "pdf": "https://aclanthology.org/2021.acl-short.130.pdf", "site": "https://aclanthology.org/2021.acl-short.130/", "pdf_size": 197694, "gs_citation": 13, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=11696477051756939398&as_sdt=5,33&sciodt=0,33&hl=en", "gs_version_total": 2, "aff": "Worldline, France; INSA Lyon, France; Worldline, France", "aff_domain": "worldline.com;insa-lyon.fr;worldline.com", "email": "worldline.com;insa-lyon.fr;worldline.com", "github": "", "project": "", "author_num": 3, "aff_unique_index": "0;1;0", "aff_unique_norm": "Worldline;INSA Lyon", "aff_unique_dep": ";", "aff_unique_url": "https://www.worldline.com;https://www.insa-lyon.fr", "aff_unique_abbr": ";INSA Lyon", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0;0;0", "aff_country_unique": "France" }, { "id": "2021.acl-short.79", "title": "Towards more equitable question answering systems: How much more data do you need?", "track": "main", "status": "Short", "award": false, "abstract": "Question answering (QA) in English has been widely explored, but multilingual datasets are relatively new, with several methods attempting to bridge the gap between high- and low-resourced languages using data augmentation through translation and cross-lingual transfer. In this project we take a step back and study which approaches allow us to take the most advantage of existing resources in order to produce QA systems in many languages. Specifically, we perform extensive analysis to measure the efficacy of few-shot approaches augmented with automatic translations and permutations of context-question-answer pairs. In addition, we make suggestions for future dataset development efforts that make better use of a fixed annotation budget, with a goal of increasing the language coverage of QA datasets and systems.", "author": "Arnab Debnath; Navid Rajabi; Fardina Fathmiul Alam; Antonios Anastasopoulos", "authorids": "/a/arnab-debnath/; /n/navid-rajabi/; /f/fardina-fathmiul-alam/; /a/antonios-anastasopoulos/", "bibtex": "@inproceedings{debnath-etal-2021-towards,\n title = \"Towards more equitable question answering systems: How much more data do you need?\",\n author = \"Debnath, Arnab and\n Rajabi, Navid and\n Alam, Fardina Fathmiul and\n Anastasopoulos, Antonios\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-short.79/\",\n doi = \"10.18653/v1/2021.acl-short.79\",\n pages = \"621--629\"\n}", "pdf": "https://aclanthology.org/2021.acl-short.79.pdf", "site": "https://aclanthology.org/2021.acl-short.79/", "pdf_size": 260436, "gs_citation": 9, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=13854695102023979775&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 7, "aff": "Department of Computer Science, George Mason University; Department of Computer Science, George Mason University; Department of Computer Science, George Mason University; Department of Computer Science, George Mason University", "aff_domain": "gmu.edu;gmu.edu;gmu.edu;gmu.edu", "email": "gmu.edu;gmu.edu;gmu.edu;gmu.edu", "github": "https://github.com/NavidRajabi/EMQA", "project": "", "author_num": 4, "aff_unique_index": "0;0;0;0", "aff_unique_norm": "George Mason University", "aff_unique_dep": "Department of Computer Science", "aff_unique_url": "https://www.gmu.edu", "aff_unique_abbr": "GMU", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0;0;0;0", "aff_country_unique": "United States" }, { "id": "2021.acl-short.57", "title": "Training Adaptive Computation for Open-Domain Question Answering with Computational Constraints", "track": "main", "status": "Short", "award": false, "abstract": "Adaptive Computation (AC) has been shown to be effective in improving the efficiency of Open-Domain Question Answering (ODQA) systems. However, the current AC approaches require tuning of all model parameters, and training state-of-the-art ODQA models requires significant computational resources that may not be available for most researchers. We propose Adaptive Passage Encoder, an AC method that can be applied to an existing ODQA model and can be trained efficiently on a single GPU. It keeps the parameters of the base ODQA model fixed, but it overrides the default layer-by-layer computation of the encoder with an AC policy that is trained to optimise the computational efficiency of the model. Our experimental results show that our method improves upon a state-of-the-art model on two datasets, and is also more accurate than previous AC methods due to the stronger base ODQA model. All source code and datasets are available at https://github.com/uclnlp/APE.", "author": "Yuxiang Wu; Pasquale Minervini; Pontus Stenetorp; Sebastian Riedel", "authorids": "/y/yuxiang-wu/; /p/pasquale-minervini/; /p/pontus-stenetorp/; /s/sebastian-riedel/", "bibtex": "@inproceedings{wu-etal-2021-training,\n title = \"Training Adaptive Computation for Open-Domain Question Answering with Computational Constraints\",\n author = \"Wu, Yuxiang and\n Minervini, Pasquale and\n Stenetorp, Pontus and\n Riedel, Sebastian\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-short.57/\",\n doi = \"10.18653/v1/2021.acl-short.57\",\n pages = \"447--453\"\n}", "pdf": "https://aclanthology.org/2021.acl-short.57.pdf", "site": "https://aclanthology.org/2021.acl-short.57/", "pdf_size": 295645, "gs_citation": 2, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=8321705260110144683&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 5, "aff": "University College London; University College London; University College London; University College London", "aff_domain": "cs.ucl.ac.uk;cs.ucl.ac.uk;cs.ucl.ac.uk;cs.ucl.ac.uk", "email": "cs.ucl.ac.uk;cs.ucl.ac.uk;cs.ucl.ac.uk;cs.ucl.ac.uk", "github": "https://github.com/uclnlp/APE", "project": "", "author_num": 4, "aff_unique_index": "0;0;0;0", "aff_unique_norm": "University College London", "aff_unique_dep": "", "aff_unique_url": "https://www.ucl.ac.uk", "aff_unique_abbr": "UCL", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0;0;0;0", "aff_country_unique": "United Kingdom" }, { "id": "2021.acl-long.446", "title": "Transfer Learning for Sequence Generation: from Single-source to Multi-source", "track": "main", "status": "Long", "award": false, "abstract": "Multi-source sequence generation (MSG) is an important kind of sequence generation tasks that takes multiple sources, including automatic post-editing, multi-source translation, multi-document summarization, etc. As MSG tasks suffer from the data scarcity problem and recent pretrained models have been proven to be effective for low-resource downstream tasks, transferring pretrained sequence-to-sequence models to MSG tasks is essential. Although directly finetuning pretrained models on MSG tasks and concatenating multiple sources into a single long sequence is regarded as a simple method to transfer pretrained models to MSG tasks, we conjecture that the direct finetuning method leads to catastrophic forgetting and solely relying on pretrained self-attention layers to capture cross-source information is not sufficient. Therefore, we propose a two-stage finetuning method to alleviate the pretrain-finetune discrepancy and introduce a novel MSG model with a fine encoder to learn better representations in MSG tasks. Experiments show that our approach achieves new state-of-the-art results on the WMT17 APE task and multi-source translation task using the WMT14 test set. When adapted to document-level translation, our framework outperforms strong baselines significantly.", "author": "Xuancheng Huang; Jingfang Xu; Maosong Sun; Yang Liu", "authorids": "/x/xuancheng-huang/; /j/jingfang-xu/; /m/maosong-sun/; /y/yang-liu-ict/", "bibtex": "@inproceedings{huang-etal-2021-transfer,\n title = \"Transfer Learning for Sequence Generation: from Single-source to Multi-source\",\n author = \"Huang, Xuancheng and\n Xu, Jingfang and\n Sun, Maosong and\n Liu, Yang\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.446/\",\n doi = \"10.18653/v1/2021.acl-long.446\",\n pages = \"5738--5750\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.446.pdf", "site": "https://aclanthology.org/2021.acl-long.446/", "pdf_size": 694929, "gs_citation": 9, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=10423323017389929334&as_sdt=40005&sciodt=0,10&hl=en", "gs_version_total": 6, "aff": "Dept. of Comp. Sci. & Tech., BNRist Center, Institute for AI, Tsinghua University; Sogou Inc.; Dept. of Comp. Sci. & Tech., BNRist Center, Institute for AI, Tsinghua University+Beijing Academy of Artificial Intelligence; Dept. of Comp. Sci. & Tech., BNRist Center, Institute for AI, Tsinghua University+Institute for AI Industry Research, Tsinghua University+Beijing Academy of Artificial Intelligence", "aff_domain": ";;;", "email": ";;;", "github": "https://github.com/THUNLP-MT/TRICE", "project": "", "author_num": 4, "aff_unique_index": "0;1;0+2;0+0+2", "aff_unique_norm": "Tsinghua University;Sogou Inc.;Beijing Academy of Artificial Intelligence", "aff_unique_dep": "Dept. of Comp. Sci. & Tech.;;", "aff_unique_url": "https://www.tsinghua.edu.cn;https://www.sogou.com;https://www.baaic.cn", "aff_unique_abbr": "THU;Sogou;BAAI", "aff_campus_unique_index": ";", "aff_campus_unique": "", "aff_country_unique_index": "0;0;0+0;0+0+0", "aff_country_unique": "China" }, { "id": "2021.acl-long.13", "title": "Transferable Dialogue Systems and User Simulators", "track": "main", "status": "Long", "award": false, "abstract": "One of the difficulties in training dialogue systems is the lack of training data. We explore the possibility of creating dialogue data through the interaction between a dialogue system and a user simulator. Our goal is to develop a modelling framework that can incorporate new dialogue scenarios through self-play between the two agents. In this framework, we first pre-train the two agents on a collection of source domain dialogues, which equips the agents to converse with each other via natural language. With further fine-tuning on a small amount of target domain data, the agents continue to interact with the aim of improving their behaviors using reinforcement learning with structured reward functions. In experiments on the MultiWOZ dataset, two practical transfer learning problems are investigated: 1) domain adaptation and 2) single-to-multiple domain transfer. We demonstrate that the proposed framework is highly effective in bootstrapping the performance of the two agents in transfer learning. We also show that our method leads to improvements in dialogue system performance on complete datasets.", "author": "Bo-Hsiang Tseng; Yinpei Dai; Florian Kreyssig; Bill Byrne", "authorids": "/b/bo-hsiang-tseng/; /y/yinpei-dai/; /f/florian-kreyssig/; /b/bill-byrne-ucsd/", "bibtex": "@inproceedings{tseng-etal-2021-transferable,\n title = \"Transferable Dialogue Systems and User Simulators\",\n author = \"Tseng, Bo-Hsiang and\n Dai, Yinpei and\n Kreyssig, Florian and\n Byrne, Bill\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.13/\",\n doi = \"10.18653/v1/2021.acl-long.13\",\n pages = \"152--166\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.13.pdf", "site": "https://aclanthology.org/2021.acl-long.13/", "pdf_size": 718645, "gs_citation": 63, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=4686946599751983923&as_sdt=5,44&sciodt=0,44&hl=en", "gs_version_total": 4, "aff": "Engineering Department, University of Cambridge, UK; Alibaba Group; Engineering Department, University of Cambridge, UK; Engineering Department, University of Cambridge, UK", "aff_domain": "cam.ac.uk;alibaba-inc.com;cam.ac.uk;cam.ac.uk", "email": "cam.ac.uk;alibaba-inc.com;cam.ac.uk;cam.ac.uk", "github": "", "project": "", "author_num": 4, "aff_unique_index": "0;1;0;0", "aff_unique_norm": "University of Cambridge;Alibaba Group", "aff_unique_dep": "Engineering Department;", "aff_unique_url": "https://www.cam.ac.uk;https://www.alibaba.com", "aff_unique_abbr": "Cambridge;Alibaba", "aff_campus_unique_index": "0;0;0", "aff_campus_unique": "Cambridge;", "aff_country_unique_index": "0;1;0;0", "aff_country_unique": "United Kingdom;China" }, { "id": "2021.acl-long.557", "title": "Transition-based Bubble Parsing: Improvements on Coordination Structure Prediction", "track": "main", "status": "Long", "award": false, "abstract": "We propose a transition-based bubble parser to perform coordination structure identification and dependency-based syntactic analysis simultaneously. Bubble representations were proposed in the formal linguistics literature decades ago; they enhance dependency trees by encoding coordination boundaries and internal relationships within coordination structures explicitly. In this paper, we introduce a transition system and neural models for parsing these bubble-enhanced structures. Experimental results on the English Penn Treebank and the English GENIA corpus show that our parsers beat previous state-of-the-art approaches on the task of coordination structure prediction, especially for the subset of sentences with complex coordination structures.", "author": "Tianze Shi; Lillian Lee", "authorids": "/t/tianze-shi/; /l/lillian-lee/", "bibtex": "@inproceedings{shi-lee-2021-transition,\n title = \"Transition-based Bubble Parsing: Improvements on Coordination Structure Prediction\",\n author = \"Shi, Tianze and\n Lee, Lillian\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.557/\",\n doi = \"10.18653/v1/2021.acl-long.557\",\n pages = \"7167--7182\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.557.pdf", "site": "https://aclanthology.org/2021.acl-long.557/", "pdf_size": 461437, "gs_citation": 1, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=9079882411286962192&as_sdt=5,47&sciodt=0,47&hl=en", "gs_version_total": 4, "aff": "Cornell University; Cornell University", "aff_domain": "cs.cornell.edu;cs.cornell.edu", "email": "cs.cornell.edu;cs.cornell.edu", "github": "github.com/tzshi/bubble-parser-acl21", "project": "", "author_num": 2, "aff_unique_index": "0;0", "aff_unique_norm": "Cornell University", "aff_unique_dep": "", "aff_unique_url": "https://www.cornell.edu", "aff_unique_abbr": "Cornell", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0;0", "aff_country_unique": "United States" }, { "id": "2021.acl-long.182", "title": "Tree-Structured Topic Modeling with Nonparametric Neural Variational Inference", "track": "main", "status": "Long", "award": false, "abstract": "Topic modeling has been widely used for discovering the latent semantic structure of documents, but most existing methods learn topics with a flat structure. Although probabilistic models can generate topic hierarchies by introducing nonparametric priors like Chinese restaurant process, such methods have data scalability issues. In this study, we develop a tree-structured topic model by leveraging nonparametric neural variational inference. Particularly, the latent components of the stick-breaking process are first learned for each document, then the affiliations of latent components are modeled by the dependency matrices between network layers. Utilizing this network structure, we can efficiently extract a tree-structured topic hierarchy with reasonable structure, low redundancy, and adaptable widths. Experiments on real-world datasets validate the effectiveness of our method.", "author": "Ziye Chen; Cheng Ding; Zusheng Zhang; Yanghui Rao; Haoran Xie", "authorids": "/z/ziye-chen/; /c/cheng-ding/; /z/zusheng-zhang/; /y/yanghui-rao/; /h/haoran-xie/", "bibtex": "@inproceedings{chen-etal-2021-tree,\n title = \"Tree-Structured Topic Modeling with Nonparametric Neural Variational Inference\",\n author = \"Chen, Ziye and\n Ding, Cheng and\n Zhang, Zusheng and\n Rao, Yanghui and\n Xie, Haoran\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.182/\",\n doi = \"10.18653/v1/2021.acl-long.182\",\n pages = \"2343--2353\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.182.pdf", "site": "https://aclanthology.org/2021.acl-long.182/", "pdf_size": 4149848, "gs_citation": 27, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=16746523841657292594&as_sdt=5,33&sciodt=0,33&hl=en", "gs_version_total": 3, "aff": "School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China; School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China; School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China; School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China; Department of Computing and Decision Sciences, Lingnan University, Hong Kong", "aff_domain": "mail2.sysu.edu.cn;mail2.sysu.edu.cn;mail2.sysu.edu.cn;mail.sysu.edu.cn;gmail.com", "email": "mail2.sysu.edu.cn;mail2.sysu.edu.cn;mail2.sysu.edu.cn;mail.sysu.edu.cn;gmail.com", "github": "", "project": "", "author_num": 5, "aff_unique_index": "0;0;0;0;1", "aff_unique_norm": "Sun Yat-sen University;Lingnan University", "aff_unique_dep": "School of Computer Science and Engineering;Department of Computing and Decision Sciences", "aff_unique_url": "http://www.sysu.edu.cn;https://www.ln.edu.hk", "aff_unique_abbr": "SYSU;Lingnan", "aff_campus_unique_index": "0;0;0;0;1", "aff_campus_unique": "Guangzhou;Hong Kong", "aff_country_unique_index": "0;0;0;0;0", "aff_country_unique": "China" }, { "id": "2021.acl-long.360", "title": "Trigger is Not Sufficient: Exploiting Frame-aware Knowledge for Implicit Event Argument Extraction", "track": "main", "status": "Long", "award": false, "abstract": "Implicit Event Argument Extraction seeks to identify arguments that play direct or implicit roles in a given event. However, most prior works focus on capturing direct relations between arguments and the event trigger. The lack of reasoning ability brings many challenges to the extraction of implicit arguments. In this work, we present a Frame-aware Event Argument Extraction (FEAE) learning framework to tackle this issue through reasoning in event frame-level scope. The proposed method leverages related arguments of the expected one as clues to guide the reasoning process. To bridge the gap between oracle knowledge used in the training phase and the imperfect related arguments in the test stage, we further introduce a curriculum knowledge distillation strategy to drive a final model that could operate without extra inputs through mimicking the behavior of a well-informed teacher model. Experimental results demonstrate FEAE obtains new state-of-the-art performance on the RAMS dataset.", "author": "Kaiwen Wei; Xian Sun; Zequn Zhang; Jingyuan Zhang; Guo Zhi; Li Jin", "authorids": "/k/kaiwen-wei/; /x/xian-sun/; /z/zequn-zhang/; /j/jingyuan-zhang/; /g/guo-zhi/; /l/li-jin/", "bibtex": "@inproceedings{wei-etal-2021-trigger,\n title = \"Trigger is Not Sufficient: Exploiting Frame-aware Knowledge for Implicit Event Argument Extraction\",\n author = \"Wei, Kaiwen and\n Sun, Xian and\n Zhang, Zequn and\n Zhang, Jingyuan and\n Zhi, Guo and\n Jin, Li\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.360/\",\n doi = \"10.18653/v1/2021.acl-long.360\",\n pages = \"4672--4682\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.360.pdf", "site": "https://aclanthology.org/2021.acl-long.360/", "pdf_size": 503659, "gs_citation": 76, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=3320526566300509202&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 4, "aff": "Key Laboratory of Network Information System Technology, Aerospace Information Research Institute, Chinese Academy of Sciences+School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences; Key Laboratory of Network Information System Technology, Aerospace Information Research Institute, Chinese Academy of Sciences+School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences; Key Laboratory of Network Information System Technology, Aerospace Information Research Institute, Chinese Academy of Sciences+School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences; Key Laboratory of Network Information System Technology, Aerospace Information Research Institute, Chinese Academy of Sciences+School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences; Key Laboratory of Network Information System Technology, Aerospace Information Research Institute, Chinese Academy of Sciences+School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences; Key Laboratory of Network Information System Technology, Aerospace Information Research Institute, Chinese Academy of Sciences+School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences", "aff_domain": "mails.ucas.ac.cn; ;mail.ie.ac.cn; ; ; ", "email": "mails.ucas.ac.cn; ;mail.ie.ac.cn; ; ; ", "github": "", "project": "", "author_num": 6, "aff_unique_index": "0+1;0+1;0+1;0+1;0+1;0+1", "aff_unique_norm": "Chinese Academy of Sciences;University of Chinese Academy of Sciences", "aff_unique_dep": "Key Laboratory of Network Information System Technology;School of Electronic, Electrical and Communication Engineering", "aff_unique_url": "http://www.cas.cn;http://www.ucas.ac.cn", "aff_unique_abbr": "CAS;UCAS", "aff_campus_unique_index": ";;;;;", "aff_campus_unique": "", "aff_country_unique_index": "0+0;0+0;0+0;0+0;0+0;0+0", "aff_country_unique": "China" }, { "id": "2021.acl-long.377", "title": "Turn the Combination Lock: Learnable Textual Backdoor Attacks via Word Substitution", "track": "main", "status": "Long", "award": false, "abstract": "Recent studies show that neural natural language processing (NLP) models are vulnerable to backdoor attacks. Injected with backdoors, models perform normally on benign examples but produce attacker-specified predictions when the backdoor is activated, presenting serious security threats to real-world applications. Since existing textual backdoor attacks pay little attention to the invisibility of backdoors, they can be easily detected and blocked. In this work, we present invisible backdoors that are activated by a learnable combination of word substitution. We show that NLP models can be injected with backdoors that lead to a nearly 100% attack success rate, whereas being highly invisible to existing defense strategies and even human inspections. The results raise a serious alarm to the security of NLP models, which requires further research to be resolved. All the data and code of this paper are released at https://github.com/thunlp/BkdAtk-LWS.", "author": "Fanchao Qi; Yuan Yao; Sophia Xu; Zhiyuan Liu; Maosong Sun", "authorids": "/f/fanchao-qi/; /y/yuan-yao/; /s/sophia-xu/; /z/zhiyuan-liu/; /m/maosong-sun/", "bibtex": "@inproceedings{qi-etal-2021-turn,\n title = \"Turn the Combination Lock: Learnable Textual Backdoor Attacks via Word Substitution\",\n author = \"Qi, Fanchao and\n Yao, Yuan and\n Xu, Sophia and\n Liu, Zhiyuan and\n Sun, Maosong\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.377/\",\n doi = \"10.18653/v1/2021.acl-long.377\",\n pages = \"4873--4883\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.377.pdf", "site": "https://aclanthology.org/2021.acl-long.377/", "pdf_size": 568430, "gs_citation": 141, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=16945140028943865991&as_sdt=5,47&sciodt=0,47&hl=en", "gs_version_total": 4, "aff": "Department of Computer Science and Technology, Tsinghua University, Beijing, China+Beijing National Research Center for Information Science and Technology+Institute for Arti\ufb01cial Intelligence, Tsinghua University, Beijing, China; Department of Computer Science and Technology, Tsinghua University, Beijing, China+Beijing National Research Center for Information Science and Technology+Institute for Arti\ufb01cial Intelligence, Tsinghua University, Beijing, China; McGill University, Canada; Department of Computer Science and Technology, Tsinghua University, Beijing, China+Beijing National Research Center for Information Science and Technology+Institute for Arti\ufb01cial Intelligence, Tsinghua University, Beijing, China; Department of Computer Science and Technology, Tsinghua University, Beijing, China+Beijing National Research Center for Information Science and Technology+Institute for Arti\ufb01cial Intelligence, Tsinghua University, Beijing, China", "aff_domain": "mails.tsinghua.edu.cn;mails.tsinghua.edu.cn;mail.mcgill.ca;tsinghua.edu.cn;tsinghua.edu.cn", "email": "mails.tsinghua.edu.cn;mails.tsinghua.edu.cn;mail.mcgill.ca;tsinghua.edu.cn;tsinghua.edu.cn", "github": "https://github.com/thunlp/BkdAtk-LWS", "project": "", "author_num": 5, "aff_unique_index": "0+1+0;0+1+0;2;0+1+0;0+1+0", "aff_unique_norm": "Tsinghua University;Beijing National Research Center for Information Science and Technology;McGill University", "aff_unique_dep": "Department of Computer Science and Technology;;", "aff_unique_url": "https://www.tsinghua.edu.cn;;https://www.mcgill.ca", "aff_unique_abbr": "THU;;McGill", "aff_campus_unique_index": "0+0;0+0;0+0;0+0", "aff_campus_unique": "Beijing;", "aff_country_unique_index": "0+0+0;0+0+0;1;0+0+0;0+0+0", "aff_country_unique": "China;Canada" }, { "id": "2021.acl-short.29", "title": "UMIC: An Unreferenced Metric for Image Captioning via Contrastive Learning", "track": "main", "status": "Short", "award": false, "abstract": "Despite the success of various text generation metrics such as BERTScore, it is still difficult to evaluate the image captions without enough reference captions due to the diversity of the descriptions. In this paper, we introduce a new metric UMIC, an Unreferenced Metric for Image Captioning which does not require reference captions to evaluate image captions. Based on Vision-and-Language BERT, we train UMIC to discriminate negative captions via contrastive learning. Also, we observe critical problems of the previous benchmark dataset (i.e., human annotations) on image captioning metric, and introduce a new collection of human annotations on the generated captions. We validate UMIC on four datasets, including our new dataset, and show that UMIC has a higher correlation than all previous metrics that require multiple references.", "author": "Hwanhee Lee; Seunghyun Yoon; Franck Dernoncourt; Trung Bui; Kyomin Jung", "authorids": "/h/hwanhee-lee/; /s/seunghyun-yoon/; /f/franck-dernoncourt/; /t/trung-bui/; /k/kyomin-jung/", "bibtex": "@inproceedings{lee-etal-2021-umic,\n title = \"{UMIC}: An Unreferenced Metric for Image Captioning via Contrastive Learning\",\n author = \"Lee, Hwanhee and\n Yoon, Seunghyun and\n Dernoncourt, Franck and\n Bui, Trung and\n Jung, Kyomin\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-short.29/\",\n doi = \"10.18653/v1/2021.acl-short.29\",\n pages = \"220--226\"\n}", "pdf": "https://aclanthology.org/2021.acl-short.29.pdf", "site": "https://aclanthology.org/2021.acl-short.29/", "pdf_size": 1432978, "gs_citation": 61, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=2615147195414703994&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 8, "aff": "Dept. of Electrical and Computer Engineering, Seoul National University, Seoul, Korea+Adobe Research, San Jose, CA, USA; Adobe Research, San Jose, CA, USA; Adobe Research, San Jose, CA, USA; Adobe Research, San Jose, CA, USA; Dept. of Electrical and Computer Engineering, Seoul National University, Seoul, Korea", "aff_domain": "snu.ac.kr;adobe.com;adobe.com;adobe.com;snu.ac.kr", "email": "snu.ac.kr;adobe.com;adobe.com;adobe.com;snu.ac.kr", "github": "https://github.com/hwanheelee1993/UMIC", "project": "", "author_num": 5, "aff_unique_index": "0+1;1;1;1;0", "aff_unique_norm": "Seoul National University;Adobe Research", "aff_unique_dep": "Dept. of Electrical and Computer Engineering;", "aff_unique_url": "https://www.snu.ac.kr;https://research.adobe.com", "aff_unique_abbr": "SNU;Adobe", "aff_campus_unique_index": "0+1;1;1;1;0", "aff_campus_unique": "Seoul;San Jose", "aff_country_unique_index": "0+1;1;1;1;0", "aff_country_unique": "Korea;United States" }, { "id": "2021.acl-long.202", "title": "UNIMO: Towards Unified-Modal Understanding and Generation via Cross-Modal Contrastive Learning", "track": "main", "status": "Long", "award": false, "abstract": "Existed pre-training methods either focus on single-modal tasks or multi-modal tasks, and cannot effectively adapt to each other. They can only utilize single-modal data (i.e., text or image) or limited multi-modal data (i.e., image-text pairs). In this work, we propose a UNIfied-MOdal pre-training architecture, namely UNIMO, which can effectively adapt to both single-modal and multi-modal understanding and generation tasks. Large scale of free text corpus and image collections are utilized to improve the capability of visual and textual understanding, and cross-modal contrastive learning (CMCL) is leveraged to align the textual and visual information into a unified semantic space, over a corpus of image-text pairs augmented with related images and texts. With the help of rich non-paired single-modal data, our model is able to learn more generalizable representations, by allowing textual knowledge and visual knowledge to enhance each other in the unified semantic space. The experimental results show that UNIMO greatly improves the performance of several single-modal and multi-modal downstream tasks. Our code and pre-trained models are public at https://github.com/PaddlePaddle/Research/tree/master/NLP/UNIMO.", "author": "Wei Li; Can Gao; Guocheng Niu; Xinyan Xiao; Hao Liu; Jiachen Liu; Hua Wu; Haifeng Wang", "authorids": "/w/wei-li/; /c/can-gao/; /g/guocheng-niu/; /x/xinyan-xiao/; /h/hao-liu/; /j/jiachen-liu/; /h/hua-wu/; /h/haifeng-wang/", "bibtex": "@inproceedings{li-etal-2021-unimo,\n title = \"{UNIMO}: Towards Unified-Modal Understanding and Generation via Cross-Modal Contrastive Learning\",\n author = \"Li, Wei and\n Gao, Can and\n Niu, Guocheng and\n Xiao, Xinyan and\n Liu, Hao and\n Liu, Jiachen and\n Wu, Hua and\n Wang, Haifeng\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.202/\",\n doi = \"10.18653/v1/2021.acl-long.202\",\n pages = \"2592--2607\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.202.pdf", "site": "https://aclanthology.org/2021.acl-long.202/", "pdf_size": 12486900, "gs_citation": 422, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=13228244630789405696&as_sdt=5,33&sciodt=0,33&hl=en", "gs_version_total": 4, "aff": "Baidu Inc., Beijing, China; Baidu Inc., Beijing, China; Baidu Inc., Beijing, China; Baidu Inc., Beijing, China; Baidu Inc., Beijing, China; Baidu Inc., Beijing, China; Baidu Inc., Beijing, China; Baidu Inc., Beijing, China", "aff_domain": "baidu.com;baidu.com;baidu.com;baidu.com;baidu.com;baidu.com;baidu.com;baidu.com", "email": "baidu.com;baidu.com;baidu.com;baidu.com;baidu.com;baidu.com;baidu.com;baidu.com", "github": "https://github.com/PaddlePaddle/Research/tree/master/NLP/UNIMO", "project": "", "author_num": 8, "aff_unique_index": "0;0;0;0;0;0;0;0", "aff_unique_norm": "Baidu Inc.", "aff_unique_dep": "", "aff_unique_url": "https://www.baidu.com", "aff_unique_abbr": "Baidu", "aff_campus_unique_index": "0;0;0;0;0;0;0;0", "aff_campus_unique": "Beijing", "aff_country_unique_index": "0;0;0;0;0;0;0;0", "aff_country_unique": "China" }, { "id": "2021.acl-long.154", "title": "UXLA: A Robust Unsupervised Data Augmentation Framework for Zero-Resource Cross-Lingual NLP", "track": "main", "status": "Long", "award": false, "abstract": "Transfer learning has yielded state-of-the-art (SoTA) results in many supervised NLP tasks. However, annotated data for every target task in every target language is rare, especially for low-resource languages. We propose UXLA, a novel unsupervised data augmentation framework for zero-resource transfer learning scenarios. In particular, UXLA aims to solve cross-lingual adaptation problems from a source language task distribution to an unknown target language task distribution, assuming no training label in the target language. At its core, UXLA performs simultaneous self-training with data augmentation and unsupervised sample selection. To show its effectiveness, we conduct extensive experiments on three diverse zero-resource cross-lingual transfer tasks. UXLA achieves SoTA results in all the tasks, outperforming the baselines by a good margin. With an in-depth framework dissection, we demonstrate the cumulative contributions of different components to its success.", "author": "M Saiful Bari; Tasnim Mohiuddin; Shafiq Joty", "authorids": "/m/m-saiful-bari/; /m/muhammad-tasnim-mohiuddin/; /s/shafiq-joty/", "bibtex": "@inproceedings{bari-etal-2021-uxla,\n title = \"{UXLA}: A Robust Unsupervised Data Augmentation Framework for Zero-Resource Cross-Lingual {NLP}\",\n author = \"Bari, M Saiful and\n Mohiuddin, Tasnim and\n Joty, Shafiq\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.154/\",\n doi = \"10.18653/v1/2021.acl-long.154\",\n pages = \"1978--1992\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.154.pdf", "site": "https://aclanthology.org/2021.acl-long.154/", "pdf_size": 887770, "gs_citation": 25, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=2558720491735282220&as_sdt=400005&sciodt=0,14&hl=en", "gs_version_total": 4, "aff": "Nanyang Technological University, Singapore+Salesforce Research Asia, Singapore; Nanyang Technological University, Singapore+Salesforce Research Asia, Singapore; Nanyang Technological University, Singapore+Salesforce Research Asia, Singapore", "aff_domain": "e.ntu.edu.sg;e.ntu.edu.sg;ntu.edu.sg", "email": "e.ntu.edu.sg;e.ntu.edu.sg;ntu.edu.sg", "github": "", "project": "", "author_num": 3, "aff_unique_index": "0+1;0+1;0+1", "aff_unique_norm": "Nanyang Technological University;Salesforce Research Asia", "aff_unique_dep": ";", "aff_unique_url": "https://www.ntu.edu.sg;https://research.salesforce.com", "aff_unique_abbr": "NTU;SRA", "aff_campus_unique_index": ";;", "aff_campus_unique": "", "aff_country_unique_index": "0+0;0+0;0+0", "aff_country_unique": "Singapore" }, { "id": "2021.acl-long.141", "title": "Ultra-Fine Entity Typing with Weak Supervision from a Masked Language Model", "track": "main", "status": "Long", "award": false, "abstract": "Recently, there is an effort to extend fine-grained entity typing by using a richer and ultra-fine set of types, and labeling noun phrases including pronouns and nominal nouns instead of just named entity mentions. A key challenge for this ultra-fine entity typing task is that human annotated data are extremely scarce, and the annotation ability of existing distant or weak supervision approaches is very limited. To remedy this problem, in this paper, we propose to obtain training data for ultra-fine entity typing by using a BERT Masked Language Model (MLM). Given a mention in a sentence, our approach constructs an input for the BERT MLM so that it predicts context dependent hypernyms of the mention, which can be used as type labels. Experimental results demonstrate that, with the help of these automatically generated labels, the performance of an ultra-fine entity typing model can be improved substantially. We also show that our approach can be applied to improve traditional fine-grained entity typing after performing simple type mapping.", "author": "Hongliang Dai; Yangqiu Song; Haixun Wang", "authorids": "/h/hongliang-dai/; /y/yangqiu-song/; /h/haixun-wang/", "bibtex": "@inproceedings{dai-etal-2021-ultra,\n title = \"Ultra-Fine Entity Typing with Weak Supervision from a Masked Language Model\",\n author = \"Dai, Hongliang and\n Song, Yangqiu and\n Wang, Haixun\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.141/\",\n doi = \"10.18653/v1/2021.acl-long.141\",\n pages = \"1790--1799\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.141.pdf", "site": "https://aclanthology.org/2021.acl-long.141/", "pdf_size": 287387, "gs_citation": 59, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=10428336997658287449&as_sdt=80005&sciodt=0,11&hl=en", "gs_version_total": 5, "aff": "Department of CSE, HKUST; Department of CSE, HKUST; Instacart", "aff_domain": "cse.ust.hk;cse.ust.hk;instacart.com", "email": "cse.ust.hk;cse.ust.hk;instacart.com", "github": "", "project": "", "author_num": 3, "aff_unique_index": "0;0;1", "aff_unique_norm": "Hong Kong University of Science and Technology;Instacart", "aff_unique_dep": "Department of Computer Science and Engineering;", "aff_unique_url": "https://www.ust.hk;https://www.instacart.com", "aff_unique_abbr": "HKUST;Instacart", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0;0;1", "aff_country_unique": "China;United States" }, { "id": "2021.acl-long.569", "title": "UnNatural Language Inference", "track": "main", "status": "Long", "award": true, "abstract": "Recent investigations into the inner-workings of state-of-the-art large-scale pre-trained Transformer-based Natural Language Understanding (NLU) models indicate that they appear to understand human-like syntax, at least to some extent. We provide novel evidence that complicates this claim: we find that state-of-the-art Natural Language Inference (NLI) models assign the same labels to permuted examples as they do to the original, i.e. they are invariant to random word-order permutations. This behavior notably differs from that of humans; we struggle to understand the meaning of ungrammatical sentences. To measure the severity of this issue, we propose a suite of metrics and investigate which properties of particular permutations lead models to be word order invariant. For example, in MNLI dataset we find almost all (98.7%) examples contain at least one permutation which elicits the gold label. Models are even able to assign gold labels to permutations that they originally failed to predict correctly. We provide a comprehensive empirical evaluation of this phenomenon, and further show that this issue exists in pre-Transformer RNN / ConvNet based encoders, as well as across multiple languages (English and Chinese). Our code and data are available at https://github.com/facebookresearch/unlu.", "author": "Koustuv Sinha; Prasanna Parthasarathi; Joelle Pineau; Adina Williams", "authorids": "/k/koustuv-sinha/; /p/prasanna-parthasarathi/; /j/joelle-pineau/; /a/adina-williams/", "bibtex": "@inproceedings{sinha-etal-2021-unnatural,\n title = \"{UnNatural} {L}anguage {I}nference\",\n author = \"Sinha, Koustuv and\n Parthasarathi, Prasanna and\n Pineau, Joelle and\n Williams, Adina\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.569/\",\n doi = \"10.18653/v1/2021.acl-long.569\",\n pages = \"7329--7346\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.569.pdf", "site": "https://aclanthology.org/2021.acl-long.569/", "pdf_size": 5498934, "gs_citation": 118, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=702390905388364447&as_sdt=5,33&sciodt=0,33&hl=en", "gs_version_total": 6, "aff": "School of Computer Science, McGill University, Canada + Montreal Institute of Learning Algorithms (Mila), Canada + Facebook AI Research (FAIR); School of Computer Science, McGill University, Canada + Montreal Institute of Learning Algorithms (Mila), Canada; School of Computer Science, McGill University, Canada + Montreal Institute of Learning Algorithms (Mila), Canada + Facebook AI Research (FAIR); Facebook AI Research (FAIR)", "aff_domain": "mail.mcgill.ca;mail.mcgill.ca;cs.mcgill.ca;fb.com", "email": "mail.mcgill.ca;mail.mcgill.ca;cs.mcgill.ca;fb.com", "github": "https://github.com/facebookresearch/unlu", "project": "", "author_num": 4, "aff_unique_index": "0+1+2;0+1;0+1+2;2", "aff_unique_norm": "McGill University;Montreal Institute of Learning Algorithms;Facebook", "aff_unique_dep": "School of Computer Science;;Facebook AI Research", "aff_unique_url": "https://www.mcgill.ca;;https://research.facebook.com", "aff_unique_abbr": "McGill;Mila;FAIR", "aff_campus_unique_index": ";;", "aff_campus_unique": "", "aff_country_unique_index": "0+0+1;0+0;0+0+1;1", "aff_country_unique": "Canada;United States" }, { "id": "2021.acl-short.6", "title": "Uncertainty and Surprisal Jointly Deliver the Punchline: Exploiting Incongruity-Based Features for Humor Recognition", "track": "main", "status": "Short", "award": false, "abstract": "Humor recognition has been widely studied as a text classification problem using data-driven approaches. However, most existing work does not examine the actual joke mechanism to understand humor. We break down any joke into two distinct components: the set-up and the punchline, and further explore the special relationship between them. Inspired by the incongruity theory of humor, we model the set-up as the part developing semantic uncertainty, and the punchline disrupting audience expectations. With increasingly powerful language models, we were able to feed the set-up along with the punchline into the GPT-2 language model, and calculate the uncertainty and surprisal values of the jokes. By conducting experiments on the SemEval 2021 Task 7 dataset, we found that these two features have better capabilities of telling jokes from non-jokes, compared with existing baselines.", "author": "Yubo Xie; Junze Li; Pearl Pu", "authorids": "/y/yubo-xie/; /j/junze-li/; /p/pearl-pu/", "bibtex": "@inproceedings{xie-etal-2021-uncertainty,\n title = \"Uncertainty and Surprisal Jointly Deliver the Punchline: Exploiting Incongruity-Based Features for Humor Recognition\",\n author = \"Xie, Yubo and\n Li, Junze and\n Pu, Pearl\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-short.6/\",\n doi = \"10.18653/v1/2021.acl-short.6\",\n pages = \"33--39\"\n}", "pdf": "https://aclanthology.org/2021.acl-short.6.pdf", "site": "https://aclanthology.org/2021.acl-short.6/", "pdf_size": 313367, "gs_citation": 22, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=11278213565285429890&as_sdt=5,33&sciodt=0,33&hl=en", "gs_version_total": 5, "aff": "School of Computer and Communication Sciences, \u00c9cole Polytechnique F\u00e9d\u00e9rale de Lausanne; School of Computer and Communication Sciences, \u00c9cole Polytechnique F\u00e9d\u00e9rale de Lausanne; School of Computer and Communication Sciences, \u00c9cole Polytechnique F\u00e9d\u00e9rale de Lausanne", "aff_domain": "epfl.ch;epfl.ch;epfl.ch", "email": "epfl.ch;epfl.ch;epfl.ch", "github": "", "project": "", "author_num": 3, "aff_unique_index": "0;0;0", "aff_unique_norm": "\u00c9cole Polytechnique F\u00e9d\u00e9rale de Lausanne", "aff_unique_dep": "School of Computer and Communication Sciences", "aff_unique_url": "https://www.epfl.ch", "aff_unique_abbr": "EPFL", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0;0;0", "aff_country_unique": "Switzerland" }, { "id": "2021.acl-long.93", "title": "Uncovering Constraint-Based Behavior in Neural Models via Targeted Fine-Tuning", "track": "main", "status": "Long", "award": false, "abstract": "A growing body of literature has focused on detailing the linguistic knowledge embedded in large, pretrained language models. Existing work has shown that non-linguistic biases in models can drive model behavior away from linguistic generalizations. We hypothesized that competing linguistic processes within a language, rather than just non-linguistic model biases, could obscure underlying linguistic knowledge. We tested this claim by exploring a single phenomenon in four languages: English, Chinese, Spanish, and Italian. While human behavior has been found to be similar across languages, we find cross-linguistic variation in model behavior. We show that competing processes in a language act as constraints on model behavior and demonstrate that targeted fine-tuning can re-weight the learned constraints, uncovering otherwise dormant linguistic knowledge in models. Our results suggest that models need to learn both the linguistic constraints in a language and their relative ranking, with mismatches in either producing non-human-like behavior.", "author": "Forrest Davis; Marten van Schijndel", "authorids": "/f/forrest-davis/; /m/marten-van-schijndel/", "bibtex": "@inproceedings{davis-van-schijndel-2021-uncovering,\n title = \"Uncovering Constraint-Based Behavior in Neural Models via Targeted Fine-Tuning\",\n author = \"Davis, Forrest and\n van Schijndel, Marten\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.93/\",\n doi = \"10.18653/v1/2021.acl-long.93\",\n pages = \"1159--1171\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.93.pdf", "site": "https://aclanthology.org/2021.acl-long.93/", "pdf_size": 841702, "gs_citation": 11, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=12523697252514549434&as_sdt=5,33&sciodt=0,33&hl=en", "gs_version_total": 8, "aff": "Department of Linguistics, Cornell University; Department of Linguistics, Cornell University", "aff_domain": "cornell.edu;cornell.edu", "email": "cornell.edu;cornell.edu", "github": "", "project": "", "author_num": 2, "aff_unique_index": "0;0", "aff_unique_norm": "Cornell University", "aff_unique_dep": "Department of Linguistics", "aff_unique_url": "https://www.cornell.edu", "aff_unique_abbr": "Cornell", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0;0", "aff_country_unique": "United States" }, { "id": "2021.acl-long.50", "title": "Understanding and Countering Stereotypes: A Computational Approach to the Stereotype Content Model", "track": "main", "status": "Long", "award": false, "abstract": "Stereotypical language expresses widely-held beliefs about different social categories. Many stereotypes are overtly negative, while others may appear positive on the surface, but still lead to negative consequences. In this work, we present a computational approach to interpreting stereotypes in text through the Stereotype Content Model (SCM), a comprehensive causal theory from social psychology. The SCM proposes that stereotypes can be understood along two primary dimensions: warmth and competence. We present a method for defining warmth and competence axes in semantic embedding space, and show that the four quadrants defined by this subspace accurately represent the warmth and competence concepts, according to annotated lexicons. We then apply our computational SCM model to textual stereotype data and show that it compares favourably with survey-based studies in the psychological literature. Furthermore, we explore various strategies to counter stereotypical beliefs with anti-stereotypes. It is known that countering stereotypes with anti-stereotypical examples is one of the most effective ways to reduce biased thinking, yet the problem of generating anti-stereotypes has not been previously studied. Thus, a better understanding of how to generate realistic and effective anti-stereotypes can contribute to addressing pressing societal concerns of stereotyping, prejudice, and discrimination.", "author": "Kathleen C. Fraser; Isar Nejadgholi; Svetlana Kiritchenko", "authorids": "/k/kathleen-c-fraser/; /i/isar-nejadgholi/; /s/svetlana-kiritchenko/", "bibtex": "@inproceedings{fraser-etal-2021-understanding,\n title = \"Understanding and Countering Stereotypes: A Computational Approach to the Stereotype Content Model\",\n author = \"Fraser, Kathleen C. and\n Nejadgholi, Isar and\n Kiritchenko, Svetlana\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.50/\",\n doi = \"10.18653/v1/2021.acl-long.50\",\n pages = \"600--616\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.50.pdf", "site": "https://aclanthology.org/2021.acl-long.50/", "pdf_size": 501664, "gs_citation": 44, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=7897007526333256414&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 9, "aff": "National Research Council Canada; National Research Council Canada; National Research Council Canada", "aff_domain": "nrc-cnrc.gc.ca;nrc-cnrc.gc.ca;nrc-cnrc.gc.ca", "email": "nrc-cnrc.gc.ca;nrc-cnrc.gc.ca;nrc-cnrc.gc.ca", "github": "", "project": "", "author_num": 3, "aff_unique_index": "0;0;0", "aff_unique_norm": "National Research Council Canada", "aff_unique_dep": "", "aff_unique_url": "https://www.nrc-cnrc.gc.ca", "aff_unique_abbr": "NRC-CNRC", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0;0;0", "aff_country_unique": "Canada" }, { "id": "2021.acl-long.22", "title": "Understanding the Properties of Minimum Bayes Risk Decoding in Neural Machine Translation", "track": "main", "status": "Long", "award": false, "abstract": "Neural Machine Translation (NMT) currently exhibits biases such as producing translations that are too short and overgenerating frequent words, and shows poor robustness to copy noise in training data or domain shift. Recent work has tied these shortcomings to beam search \u2013 the de facto standard inference algorithm in NMT \u2013 and Eikema & Aziz (2020) propose to use Minimum Bayes Risk (MBR) decoding on unbiased samples instead. In this paper, we empirically investigate the properties of MBR decoding on a number of previously reported biases and failure cases of beam search. We find that MBR still exhibits a length and token frequency bias, owing to the MT metrics used as utility functions, but that MBR also increases robustness against copy noise in the training data and domain shift.", "author": "Mathias M\u00fcller; Rico Sennrich", "authorids": "/m/mathias-muller/; /r/rico-sennrich/", "bibtex": "@inproceedings{muller-sennrich-2021-understanding,\n title = \"Understanding the Properties of Minimum {B}ayes Risk Decoding in Neural Machine Translation\",\n author = {M{\\\"u}ller, Mathias and\n Sennrich, Rico},\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.22/\",\n doi = \"10.18653/v1/2021.acl-long.22\",\n pages = \"259--272\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.22.pdf", "site": "https://aclanthology.org/2021.acl-long.22/", "pdf_size": 9232488, "gs_citation": 54, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=6330993087878200625&as_sdt=5,33&sciodt=0,33&hl=en", "gs_version_total": 8, "aff": "Department of Computational Linguistics, University of Zurich; Department of Computational Linguistics, University of Zurich + School of Informatics, University of Edinburgh", "aff_domain": "; ", "email": "; ", "github": "https://github.com/ZurichNLP/understanding-mbr", "project": "", "author_num": 2, "aff_unique_index": "0;0+1", "aff_unique_norm": "University of Zurich;University of Edinburgh", "aff_unique_dep": "Department of Computational Linguistics;School of Informatics", "aff_unique_url": "https://www.unizh.ch;https://www.ed.ac.uk", "aff_unique_abbr": "UZH;Edinburgh", "aff_campus_unique_index": "1", "aff_campus_unique": ";Edinburgh", "aff_country_unique_index": "0;0+1", "aff_country_unique": "Switzerland;United Kingdom" }, { "id": "2021.acl-long.19", "title": "UniRE: A Unified Label Space for Entity Relation Extraction", "track": "main", "status": "Long", "award": false, "abstract": "Many joint entity relation extraction models setup two separated label spaces for the two sub-tasks (i.e., entity detection and relation classification). We argue that this setting may hinder the information interaction between entities and relations. In this work, we propose to eliminate the different treatment on the two sub-tasks\u2019 label spaces. The input of our model is a table containing all word pairs from a sentence. Entities and relations are represented by squares and rectangles in the table. We apply a unified classifier to predict each cell\u2019s label, which unifies the learning of two sub-tasks. For testing, an effective (yet fast) approximate decoder is proposed for finding squares and rectangles from tables. Experiments on three benchmarks (ACE04, ACE05, SciERC) show that, using only half the number of parameters, our model achieves competitive accuracy with the best extractor, and is faster.", "author": "Yijun Wang; Changzhi Sun; Yuanbin Wu; Hao Zhou; Lei Li; Junchi Yan", "authorids": "/y/yijun-wang/; /c/changzhi-sun/; /y/yuanbin-wu/; /h/hao-zhou/; /l/lei-li/; /j/junchi-yan/", "bibtex": "@inproceedings{wang-etal-2021-unire,\n title = \"{U}ni{RE}: A Unified Label Space for Entity Relation Extraction\",\n author = \"Wang, Yijun and\n Sun, Changzhi and\n Wu, Yuanbin and\n Zhou, Hao and\n Li, Lei and\n Yan, Junchi\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.19/\",\n doi = \"10.18653/v1/2021.acl-long.19\",\n pages = \"220--231\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.19.pdf", "site": "https://aclanthology.org/2021.acl-long.19/", "pdf_size": 1624147, "gs_citation": 148, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=5857868772356407483&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 5, "aff": "Department of Computer Science and Engineering, Shanghai Jiao Tong University+MoE Key Lab of Artificial Intelligence, AI Institute, Shanghai Jiao Tong University; ByteDance AI Lab; School of Computer Science and Technology, East China Normal University; ByteDance AI Lab; ByteDance AI Lab; Department of Computer Science and Engineering, Shanghai Jiao Tong University+MoE Key Lab of Artificial Intelligence, AI Institute, Shanghai Jiao Tong University", "aff_domain": "sjtu.edu.cn;sjtu.edu.cn;cs.ecnu.edu.cn;bytedance.com;bytedance.com;bytedance.com", "email": "sjtu.edu.cn;sjtu.edu.cn;cs.ecnu.edu.cn;bytedance.com;bytedance.com;bytedance.com", "github": "", "project": "", "author_num": 6, "aff_unique_index": "0+0;1;2;1;1;0+0", "aff_unique_norm": "Shanghai Jiao Tong University;ByteDance;East China Normal University", "aff_unique_dep": "Department of Computer Science and Engineering;AI Lab;School of Computer Science and Technology", "aff_unique_url": "https://www.sjtu.edu.cn;https://www.bytedance.com;http://www.ecnu.edu.cn", "aff_unique_abbr": "SJTU;ByteDance;ECNU", "aff_campus_unique_index": "1;1", "aff_campus_unique": ";Shanghai", "aff_country_unique_index": "0+0;0;0;0;0;0+0", "aff_country_unique": "China" }, { "id": "2021.acl-long.5", "title": "Unified Dual-view Cognitive Model for Interpretable Claim Verification", "track": "main", "status": "Long", "award": false, "abstract": "Recent studies constructing direct interactions between the claim and each single user response (a comment or a relevant article) to capture evidence have shown remarkable success in interpretable claim verification. Owing to different single responses convey different cognition of individual users (i.e., audiences), the captured evidence belongs to the perspective of individual cognition. However, individuals\u2019 cognition of social things is not always able to truly reflect the objective. There may be one-sided or biased semantics in their opinions on a claim. The captured evidence correspondingly contains some unobjective and biased evidence fragments, deteriorating task performance. In this paper, we propose a Dual-view model based on the views of Collective and Individual Cognition (CICD) for interpretable claim verification. From the view of the collective cognition, we not only capture the word-level semantics based on individual users, but also focus on sentence-level semantics (i.e., the overall responses) among all users and adjust the proportion between them to generate global evidence. From the view of individual cognition, we select the top-k articles with high degree of difference and interact with the claim to explore the local key evidence fragments. To weaken the bias of individual cognition-view evidence, we devise inconsistent loss to suppress the divergence between global and local evidence for strengthening the consistent shared evidence between the both. Experiments on three benchmark datasets confirm that CICD achieves state-of-the-art performance.", "author": "Lianwei Wu; Yuan Rao; Yuqian Lan; Ling Sun; Zhaoyin Qi", "authorids": "/l/lianwei-wu/; /y/yuan-rao/; /y/yuqian-lan/; /l/ling-sun/; /z/zhaoyin-qi/", "bibtex": "@inproceedings{wu-etal-2021-unified,\n title = \"Unified Dual-view Cognitive Model for Interpretable Claim Verification\",\n author = \"Wu, Lianwei and\n Rao, Yuan and\n Lan, Yuqian and\n Sun, Ling and\n Qi, Zhaoyin\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.5/\",\n doi = \"10.18653/v1/2021.acl-long.5\",\n pages = \"59--68\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.5.pdf", "site": "https://aclanthology.org/2021.acl-long.5/", "pdf_size": 628872, "gs_citation": 21, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=1109396711999989624&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 5, "aff": "Xi'an Key Lab. of Social Intelligence and Complexity Data Processing, School of Software Engineering, Xi'an Jiaotong University+Shaanxi Joint Key Laboratory for Artifact Intelligence(Sub-Lab of Xi'an Jiaotong University)+Research Institute of Xi'an Jiaotong University, Shenzhen; Xi'an Key Lab. of Social Intelligence and Complexity Data Processing, School of Software Engineering, Xi'an Jiaotong University+Shaanxi Joint Key Laboratory for Artifact Intelligence(Sub-Lab of Xi'an Jiaotong University)+Research Institute of Xi'an Jiaotong University, Shenzhen; Xi'an Key Lab. of Social Intelligence and Complexity Data Processing, School of Software Engineering, Xi'an Jiaotong University+Shaanxi Joint Key Laboratory for Artifact Intelligence(Sub-Lab of Xi'an Jiaotong University)+Research Institute of Xi'an Jiaotong University, Shenzhen; Xi'an Key Lab. of Social Intelligence and Complexity Data Processing, School of Software Engineering, Xi'an Jiaotong University+Shaanxi Joint Key Laboratory for Artifact Intelligence(Sub-Lab of Xi'an Jiaotong University)+Research Institute of Xi'an Jiaotong University, Shenzhen; Xi'an Key Lab. of Social Intelligence and Complexity Data Processing, School of Software Engineering, Xi'an Jiaotong University+Shaanxi Joint Key Laboratory for Artifact Intelligence(Sub-Lab of Xi'an Jiaotong University)+Research Institute of Xi'an Jiaotong University, Shenzhen", "aff_domain": "stu.xjtu.edu.cn;stu.xjtu.edu.cn;stu.xjtu.edu.cn;stu.xjtu.edu.cn;mail.xjtu.edu.cn", "email": "stu.xjtu.edu.cn;stu.xjtu.edu.cn;stu.xjtu.edu.cn;stu.xjtu.edu.cn;mail.xjtu.edu.cn", "github": "", "project": "", "author_num": 5, "aff_unique_index": "0+0+0;0+0+0;0+0+0;0+0+0;0+0+0", "aff_unique_norm": "Xi'an Jiaotong University", "aff_unique_dep": "School of Software Engineering", "aff_unique_url": "http://www.xjtu.edu.cn", "aff_unique_abbr": "XJTU", "aff_campus_unique_index": "0+0+1;0+0+1;0+0+1;0+0+1;0+0+1", "aff_campus_unique": "Xi'an;Shenzhen", "aff_country_unique_index": "0+0+0;0+0+0;0+0+0;0+0+0;0+0+0", "aff_country_unique": "China" }, { "id": "2021.acl-long.429", "title": "Unified Interpretation of Softmax Cross-Entropy and Negative Sampling: With Case Study for Knowledge Graph Embedding", "track": "main", "status": "Long", "award": false, "abstract": "In knowledge graph embedding, the theoretical relationship between the softmax cross-entropy and negative sampling loss functions has not been investigated. This makes it difficult to fairly compare the results of the two different loss functions. We attempted to solve this problem by using the Bregman divergence to provide a unified interpretation of the softmax cross-entropy and negative sampling loss functions. Under this interpretation, we can derive theoretical findings for fair comparison. Experimental results on the FB15k-237 and WN18RR datasets show that the theoretical findings are valid in practical settings.", "author": "Hidetaka Kamigaito; Katsuhiko Hayashi", "authorids": "/h/hidetaka-kamigaito/; /k/katsuhiko-hayashi/", "bibtex": "@inproceedings{kamigaito-hayashi-2021-unified,\n title = \"Unified Interpretation of Softmax Cross-Entropy and Negative Sampling: With Case Study for Knowledge Graph Embedding\",\n author = \"Kamigaito, Hidetaka and\n Hayashi, Katsuhiko\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.429/\",\n doi = \"10.18653/v1/2021.acl-long.429\",\n pages = \"5517--5531\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.429.pdf", "site": "https://aclanthology.org/2021.acl-long.429/", "pdf_size": 219590, "gs_citation": 13, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=6924559529174010960&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 7, "aff": "Tokyo Institute of Technology; Gunma University+RIKEN AIP", "aff_domain": "lr.pi.titech.ac.jp;gmail.com", "email": "lr.pi.titech.ac.jp;gmail.com", "github": "", "project": "", "author_num": 2, "aff_unique_index": "0;1+2", "aff_unique_norm": "Tokyo Institute of Technology;Gunma University;RIKEN", "aff_unique_dep": ";;Advanced Institute for Computational Science", "aff_unique_url": "https://www.titech.ac.jp;https://www.gunma-u.ac.jp;https://www.aip.riken.jp", "aff_unique_abbr": "Titech;Gunma U;RIKEN AIP", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0;0+0", "aff_country_unique": "Japan" }, { "id": "2021.acl-long.240", "title": "UnitedQA: A Hybrid Approach for Open Domain Question Answering", "track": "main", "status": "Long", "award": false, "abstract": "To date, most of recent work under the retrieval-reader framework for open-domain QA focuses on either extractive or generative reader exclusively. In this paper, we study a hybrid approach for leveraging the strengths of both models. We apply novel techniques to enhance both extractive and generative readers built upon recent pretrained neural language models, and find that proper training methods can provide large improvement over previous state-of-the-art models. We demonstrate that a simple hybrid approach by combining answers from both readers can efficiently take advantages of extractive and generative answer inference strategies and outperforms single models as well as homogeneous ensembles. Our approach outperforms previous state-of-the-art models by 3.3 and 2.7 points in exact match on NaturalQuestions and TriviaQA respectively.", "author": "Hao Cheng; Yelong Shen; Xiaodong Liu; Pengcheng He; Weizhu Chen; Jianfeng Gao", "authorids": "/h/hao-cheng/; /y/yelong-shen/; /x/xiaodong-liu/; /p/pengcheng-he/; /w/weizhu-chen/; /j/jianfeng-gao/", "bibtex": "@inproceedings{cheng-etal-2021-unitedqa,\n title = \"{U}nited{QA}: {A} Hybrid Approach for Open Domain Question Answering\",\n author = \"Cheng, Hao and\n Shen, Yelong and\n Liu, Xiaodong and\n He, Pengcheng and\n Chen, Weizhu and\n Gao, Jianfeng\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.240/\",\n doi = \"10.18653/v1/2021.acl-long.240\",\n pages = \"3080--3090\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.240.pdf", "site": "https://aclanthology.org/2021.acl-long.240/", "pdf_size": 366319, "gs_citation": 54, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=7176494025601217240&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 4, "aff": "Microsoft Research; Microsoft Azure AI; Microsoft Research; Microsoft Azure AI; Microsoft Azure AI; Microsoft Research", "aff_domain": "microsoft.com;microsoft.com;microsoft.com;microsoft.com;microsoft.com;microsoft.com", "email": "microsoft.com;microsoft.com;microsoft.com;microsoft.com;microsoft.com;microsoft.com", "github": "", "project": "", "author_num": 6, "aff_unique_index": "0;1;0;1;1;0", "aff_unique_norm": "Microsoft Corporation;Microsoft", "aff_unique_dep": "Microsoft Research;Azure AI", "aff_unique_url": "https://www.microsoft.com/en-us/research;https://azure.microsoft.com/en-us/ai", "aff_unique_abbr": "MSR;Microsoft Azure AI", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0;0;0;0;0;0", "aff_country_unique": "United States" }, { "id": "2021.acl-long.490", "title": "Unleash GPT-2 Power for Event Detection", "track": "main", "status": "Long", "award": false, "abstract": "Event Detection (ED) aims to recognize mentions of events (i.e., event triggers) and their types in text. Recently, several ED datasets in various domains have been proposed. However, the major limitation of these resources is the lack of enough training data for individual event types which hinders the efficient training of data-hungry deep learning models. To overcome this issue, we propose to exploit the powerful pre-trained language model GPT-2 to generate training samples for ED. To prevent the noises inevitable in automatically generated data from hampering training process, we propose to exploit a teacher-student architecture in which the teacher is supposed to learn anchor knowledge from the original data. The student is then trained on combination of the original and GPT-generated data while being led by the anchor knowledge from the teacher. Optimal transport is introduced to facilitate the anchor knowledge-based guidance between the two networks. We evaluate the proposed model on multiple ED benchmark datasets, gaining consistent improvement and establishing state-of-the-art results for ED.", "author": "Amir Pouran Ben Veyseh; Viet Lai; Franck Dernoncourt; Thien Huu Nguyen", "authorids": "/a/amir-pouran-ben-veyseh/; /v/viet-lai/; /f/franck-dernoncourt/; /t/thien-huu-nguyen/", "bibtex": "@inproceedings{pouran-ben-veyseh-etal-2021-unleash,\n title = \"Unleash {GPT}-2 Power for Event Detection\",\n author = \"Pouran Ben Veyseh, Amir and\n Lai, Viet and\n Dernoncourt, Franck and\n Nguyen, Thien Huu\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.490/\",\n doi = \"10.18653/v1/2021.acl-long.490\",\n pages = \"6271--6282\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.490.pdf", "site": "https://aclanthology.org/2021.acl-long.490/", "pdf_size": 379933, "gs_citation": 69, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=12688992821772622450&as_sdt=400005&sciodt=0,14&hl=en", "gs_version_total": 3, "aff": "Department of Computer and Information Science, University of Oregon; Department of Computer and Information Science, University of Oregon; Adobe Research; Department of Computer and Information Science, University of Oregon", "aff_domain": "cs.uoregon.edu;cs.uoregon.edu;adobe.com;cs.uoregon.edu", "email": "cs.uoregon.edu;cs.uoregon.edu;adobe.com;cs.uoregon.edu", "github": "", "project": "", "author_num": 4, "aff_unique_index": "0;0;1;0", "aff_unique_norm": "University of Oregon;Adobe", "aff_unique_dep": "Department of Computer and Information Science;Adobe Research", "aff_unique_url": "https://www.uoregon.edu;https://research.adobe.com", "aff_unique_abbr": "UO;Adobe", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0;0;0;0", "aff_country_unique": "United States" }, { "id": "2021.acl-short.127", "title": "Unsupervised Cross-Domain Prerequisite Chain Learning using Variational Graph Autoencoders", "track": "main", "status": "Short", "award": false, "abstract": "Learning prerequisite chains is an important task for one to pick up knowledge efficiently in both known and unknown domains. For example, one may be an expert in the natural language processing (NLP) domain, but want to determine the best order in which to learn new concepts in an unfamiliar Computer Vision domain (CV). Both domains share some common concepts, such as machine learning basics and deep learning models. In this paper, we solve the task of unsupervised cross-domain concept prerequisite chain learning, using an optimized variational graph autoencoder. Our model learns to transfer concept prerequisite relations from an information-rich domain (source domain) to an information-poor domain (target domain), substantially surpassing other baseline models. In addition, we expand an existing dataset by introducing two new domains\u2014-CV and Bioinformatics (BIO). The annotated data and resources as well as the code will be made publicly available.", "author": "Irene Li; Vanessa Yan; Tianxiao Li; Rihao Qu; Dragomir Radev", "authorids": "/i/irene-li/; /v/vanessa-yan/; /t/tianxiao-li/; /r/rihao-qu/; /d/dragomir-radev/", "bibtex": "@inproceedings{li-etal-2021-unsupervised-cross,\n title = \"Unsupervised Cross-Domain Prerequisite Chain Learning using Variational Graph Autoencoders\",\n author = \"Li, Irene and\n Yan, Vanessa and\n Li, Tianxiao and\n Qu, Rihao and\n Radev, Dragomir\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-short.127/\",\n doi = \"10.18653/v1/2021.acl-short.127\",\n pages = \"1005--1011\"\n}", "pdf": "https://aclanthology.org/2021.acl-short.127.pdf", "site": "https://aclanthology.org/2021.acl-short.127/", "pdf_size": 573025, "gs_citation": 18, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=11635988994340907660&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 5, "aff": "Yale University, USA; Yale University, USA; Yale University, USA; Yale University, USA; Yale University, USA", "aff_domain": "yale.edu;yale.edu;yale.edu;yale.edu;yale.edu", "email": "yale.edu;yale.edu;yale.edu;yale.edu;yale.edu", "github": "", "project": "", "author_num": 5, "aff_unique_index": "0;0;0;0;0", "aff_unique_norm": "Yale University", "aff_unique_dep": "", "aff_unique_url": "https://www.yale.edu", "aff_unique_abbr": "Yale", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0;0;0;0;0", "aff_country_unique": "United States" }, { "id": "2021.acl-short.74", "title": "Unsupervised Enrichment of Persona-grounded Dialog with Background Stories", "track": "main", "status": "Short", "award": false, "abstract": "Humans often refer to personal narratives, life experiences, and events to make a conversation more engaging and rich. While persona-grounded dialog models are able to generate responses that follow a given persona, they often miss out on stating detailed experiences or events related to a persona, often leaving conversations shallow and dull. In this work, we equip dialog models with \u2018background stories\u2019 related to a persona by leveraging fictional narratives from existing story datasets (e.g. ROCStories). Since current dialog datasets do not contain such narratives as responses, we perform an unsupervised adaptation of a retrieved story for generating a dialog response using a gradient-based rewriting technique. Our proposed method encourages the generated response to be fluent (i.e., highly likely) with the dialog history, minimally different from the retrieved story to preserve event ordering and consistent with the original persona. We demonstrate that our method can generate responses that are more diverse, and are rated more engaging and human-like by human evaluators, compared to outputs from existing dialog models.", "author": "Bodhisattwa Prasad Majumder; Taylor Berg-Kirkpatrick; Julian McAuley; Harsh Jhamtani", "authorids": "/b/bodhisattwa-prasad-majumder/; /t/taylor-berg-kirkpatrick/; /j/julian-mcauley/; /h/harsh-jhamtani/", "bibtex": "@inproceedings{majumder-etal-2021-unsupervised,\n title = \"Unsupervised Enrichment of Persona-grounded Dialog with Background Stories\",\n author = \"Majumder, Bodhisattwa Prasad and\n Berg-Kirkpatrick, Taylor and\n McAuley, Julian and\n Jhamtani, Harsh\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-short.74/\",\n doi = \"10.18653/v1/2021.acl-short.74\",\n pages = \"585--592\"\n}", "pdf": "https://aclanthology.org/2021.acl-short.74.pdf", "site": "https://aclanthology.org/2021.acl-short.74/", "pdf_size": 1012360, "gs_citation": 28, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=4101785039152668525&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 4, "aff": "Department of Computer Science and Engineering, UC San Diego; Department of Computer Science and Engineering, UC San Diego; Department of Computer Science and Engineering, UC San Diego; School of Computer Science, Carnegie Mellon University", "aff_domain": "eng.ucsd.edu;eng.ucsd.edu;eng.ucsd.edu;cs.cmu.edu", "email": "eng.ucsd.edu;eng.ucsd.edu;eng.ucsd.edu;cs.cmu.edu", "github": "", "project": "", "author_num": 4, "aff_unique_index": "0;0;0;1", "aff_unique_norm": "University of California, San Diego;Carnegie Mellon University", "aff_unique_dep": "Department of Computer Science and Engineering;School of Computer Science", "aff_unique_url": "https://www.ucsd.edu;https://www.cmu.edu", "aff_unique_abbr": "UCSD;CMU", "aff_campus_unique_index": "0;0;0;1", "aff_campus_unique": "San Diego;Pittsburgh", "aff_country_unique_index": "0;0;0;0", "aff_country_unique": "United States" }, { "id": "2021.acl-long.232", "title": "Unsupervised Extractive Summarization-Based Representations for Accurate and Explainable Collaborative Filtering", "track": "main", "status": "Long", "award": false, "abstract": "We pioneer the first extractive summarization-based collaborative filtering model called ESCOFILT. Our proposed model specifically produces extractive summaries for each item and user. Unlike other types of explanations, summary-level explanations closely resemble real-life explanations. The strength of ESCOFILT lies in the fact that it unifies representation and explanation. In other words, extractive summaries both represent and explain the items and users. Our model uniquely integrates BERT, K-Means embedding clustering, and multilayer perceptron to learn sentence embeddings, representation-explanations, and user-item interactions, respectively. We argue that our approach enhances both rating prediction accuracy and user/item explainability. Our experiments illustrate that ESCOFILT\u2019s prediction accuracy is better than the other state-of-the-art recommender models. Furthermore, we propose a comprehensive set of criteria that assesses the real-life explainability of explanations. Our explainability study demonstrates the superiority of and preference for summary-level explanations over other explanation types.", "author": "Reinald Adrian Pugoy; Hung-Yu Kao", "authorids": "/r/reinald-adrian-pugoy/; /h/hung-yu-kao/", "bibtex": "@inproceedings{pugoy-kao-2021-unsupervised,\n title = \"Unsupervised Extractive Summarization-Based Representations for Accurate and Explainable Collaborative Filtering\",\n author = \"Pugoy, Reinald Adrian and\n Kao, Hung-Yu\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.232/\",\n doi = \"10.18653/v1/2021.acl-long.232\",\n pages = \"2981--2990\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.232.pdf", "site": "https://aclanthology.org/2021.acl-long.232/", "pdf_size": 565175, "gs_citation": 33, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=15136986406604273628&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 4, "aff": "Intelligent Knowledge Management Lab, National Cheng Kung University, Tainan City, Taiwan + Faculty of Information and Communication Studies, University of the Philippines Open University, Los Ba\u00f1os, Philippines; Intelligent Knowledge Management Lab, National Cheng Kung University, Tainan City, Taiwan", "aff_domain": "up.edu.ph;mail.ncku.edu.tw", "email": "up.edu.ph;mail.ncku.edu.tw", "github": "", "project": "", "author_num": 2, "aff_unique_index": "0+1;0", "aff_unique_norm": "National Cheng Kung University;University of the Philippines Open University", "aff_unique_dep": "Intelligent Knowledge Management Lab;Faculty of Information and Communication Studies", "aff_unique_url": "https://www.ncku.edu.tw;https://www.upou.edu.ph", "aff_unique_abbr": "NCKU;UP Open University", "aff_campus_unique_index": "0+1;0", "aff_campus_unique": "Tainan City;Los Ba\u00f1os", "aff_country_unique_index": "0+1;0", "aff_country_unique": "Taiwan, China;Philippines" }, { "id": "2021.acl-long.225", "title": "Unsupervised Neural Machine Translation for Low-Resource Domains via Meta-Learning", "track": "main", "status": "Long", "award": false, "abstract": "Unsupervised machine translation, which utilizes unpaired monolingual corpora as training data, has achieved comparable performance against supervised machine translation. However, it still suffers from data-scarce domains. To address this issue, this paper presents a novel meta-learning algorithm for unsupervised neural machine translation (UNMT) that trains the model to adapt to another domain by utilizing only a small amount of training data. We assume that domain-general knowledge is a significant factor in handling data-scarce domains. Hence, we extend the meta-learning algorithm, which utilizes knowledge learned from high-resource domains, to boost the performance of low-resource UNMT. Our model surpasses a transfer learning-based approach by up to 2-3 BLEU scores. Extensive experimental results show that our proposed algorithm is pertinent for fast adaptation and consistently outperforms other baselines.", "author": "Cheonbok Park; Yunwon Tae; TaeHee Kim; Soyoung Yang; Mohammad Azam Khan; Lucy Park; Jaegul Choo", "authorids": "/c/cheonbok-park/; /y/yunwon-tae/; /t/taehee-kim/; /s/soyoung-yang/; /m/mohammad-azam-khan/; /l/lucy-park/; /j/jaegul-choo/", "bibtex": "@inproceedings{park-etal-2021-unsupervised,\n title = \"Unsupervised Neural Machine Translation for Low-Resource Domains via Meta-Learning\",\n author = \"Park, Cheonbok and\n Tae, Yunwon and\n Kim, TaeHee and\n Yang, Soyoung and\n Khan, Mohammad Azam and\n Park, Lucy and\n Choo, Jaegul\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.225/\",\n doi = \"10.18653/v1/2021.acl-long.225\",\n pages = \"2888--2901\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.225.pdf", "site": "https://aclanthology.org/2021.acl-long.225/", "pdf_size": 988806, "gs_citation": 19, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=17891478666890677515&as_sdt=5,33&sciodt=0,33&hl=en", "gs_version_total": 6, "aff": "NAVER Corp; Korea University; KAIST; DPDC; Upstage AI Research; NAVER Corp; KAIST", "aff_domain": "navercorp.com;korea.ac.kr;kaist.com;kaist.com;dpdc.org.bd;upstage.ai;kaist.com", "email": "navercorp.com;korea.ac.kr;kaist.com;kaist.com;dpdc.org.bd;upstage.ai;kaist.com", "github": "https://github.com/papago-lab/MetaGUMT", "project": "", "author_num": 7, "aff_unique_index": "0;1;2;3;4;0;2", "aff_unique_norm": "NAVER Corporation;Korea University;Korea Advanced Institute of Science and Technology;DPDC;Upstage AI Research", "aff_unique_dep": ";;;;", "aff_unique_url": "https://www.naver.com;https://www.korea.ac.kr;https://www.kaist.ac.kr;;", "aff_unique_abbr": "NAVER;KU;KAIST;;", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0;0;0;0;0", "aff_country_unique": "South Korea;" }, { "id": "2021.acl-long.85", "title": "Unsupervised Out-of-Domain Detection via Pre-trained Transformers", "track": "main", "status": "Long", "award": false, "abstract": "Deployed real-world machine learning applications are often subject to uncontrolled and even potentially malicious inputs. Those out-of-domain inputs can lead to unpredictable outputs and sometimes catastrophic safety issues. Prior studies on out-of-domain detection require in-domain task labels and are limited to supervised classification scenarios. Our work tackles the problem of detecting out-of-domain samples with only unsupervised in-domain data. We utilize the latent representations of pre-trained transformers and propose a simple yet effective method to transform features across all layers to construct out-of-domain detectors efficiently. Two domain-specific fine-tuning approaches are further proposed to boost detection accuracy. Our empirical evaluations of related methods on two datasets validate that our method greatly improves out-of-domain detection ability in a more general scenario.", "author": "Keyang Xu; Tongzheng Ren; Shikun Zhang; Yihao Feng; Caiming Xiong", "authorids": "/k/keyang-xu/; /t/tongzheng-ren/; /s/shikun-zhang/; /y/yihao-feng/; /c/caiming-xiong/", "bibtex": "@inproceedings{xu-etal-2021-unsupervised,\n title = \"Unsupervised Out-of-Domain Detection via Pre-trained Transformers\",\n author = \"Xu, Keyang and\n Ren, Tongzheng and\n Zhang, Shikun and\n Feng, Yihao and\n Xiong, Caiming\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.85/\",\n doi = \"10.18653/v1/2021.acl-long.85\",\n pages = \"1052--1061\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.85.pdf", "site": "https://aclanthology.org/2021.acl-long.85/", "pdf_size": 566152, "gs_citation": 46, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=10079020075965569140&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 3, "aff": "Columbia University; University of Texas at Austin; Carnegie Mellon University; University of Texas at Austin; Salesforce Research", "aff_domain": "columbia.edu;cs.utexas.edu;cs.cmu.edu;cs.utexas.edu;salesforce.com", "email": "columbia.edu;cs.utexas.edu;cs.cmu.edu;cs.utexas.edu;salesforce.com", "github": "https://github.com/rivercold/BERT-unsupervised-OOD", "project": "", "author_num": 5, "aff_unique_index": "0;1;2;1;3", "aff_unique_norm": "Columbia University;University of Texas at Austin;Carnegie Mellon University;Salesforce", "aff_unique_dep": ";;;Salesforce Research", "aff_unique_url": "https://www.columbia.edu;https://www.utexas.edu;https://www.cmu.edu;https://research.salesforce.com", "aff_unique_abbr": "Columbia;UT Austin;CMU;Salesforce", "aff_campus_unique_index": "1;1", "aff_campus_unique": ";Austin", "aff_country_unique_index": "0;0;0;0;0", "aff_country_unique": "United States" }, { "id": "2021.acl-short.117", "title": "Unsupervised Pronoun Resolution via Masked Noun-Phrase Prediction", "track": "main", "status": "Short", "award": false, "abstract": "In this work, we propose Masked Noun-Phrase Prediction (MNPP), a pre-training strategy to tackle pronoun resolution in a fully unsupervised setting. Firstly, We evaluate our pre-trained model on various pronoun resolution datasets without any finetuning. Our method outperforms all previous unsupervised methods on all datasets by large margins. Secondly, we proceed to a few-shot setting where we finetune our pre-trained model on WinoGrande-S and XS separately. Our method outperforms RoBERTa-large baseline with large margins, meanwhile, achieving a higher AUC score after further finetuning on the remaining three official splits of WinoGrande.", "author": "Ming Shen; Pratyay Banerjee; Chitta Baral", "authorids": "/m/ming-shen/; /p/pratyay-banerjee/; /c/chitta-baral/", "bibtex": "@inproceedings{shen-etal-2021-unsupervised,\n title = \"Unsupervised Pronoun Resolution via Masked Noun-Phrase Prediction\",\n author = \"Shen, Ming and\n Banerjee, Pratyay and\n Baral, Chitta\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-short.117/\",\n doi = \"10.18653/v1/2021.acl-short.117\",\n pages = \"932--941\"\n}", "pdf": "https://aclanthology.org/2021.acl-short.117.pdf", "site": "https://aclanthology.org/2021.acl-short.117/", "pdf_size": 410619, "gs_citation": 6, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=10062220650746104265&as_sdt=5,33&sciodt=0,33&hl=en", "gs_version_total": 8, "aff": "Arizona State University; Arizona State University; Arizona State University", "aff_domain": "asu.edu;asu.edu;asu.edu", "email": "asu.edu;asu.edu;asu.edu", "github": "", "project": "", "author_num": 3, "aff_unique_index": "0;0;0", "aff_unique_norm": "Arizona State University", "aff_unique_dep": "", "aff_unique_url": "https://www.asu.edu", "aff_unique_abbr": "ASU", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0;0;0", "aff_country_unique": "United States" }, { "id": "2021.acl-short.43", "title": "Using Adversarial Attacks to Reveal the Statistical Bias in Machine Reading Comprehension Models", "track": "main", "status": "Short", "award": false, "abstract": "Pre-trained language models have achieved human-level performance on many Machine Reading Comprehension (MRC) tasks, but it remains unclear whether these models truly understand language or answer questions by exploiting statistical biases in datasets. Here, we demonstrate a simple yet effective method to attack MRC models and reveal the statistical biases in these models. We apply the method to the RACE dataset, for which the answer to each MRC question is selected from 4 options. It is found that several pre-trained language models, including BERT, ALBERT, and RoBERTa, show consistent preference to some options, even when these options are irrelevant to the question. When interfered by these irrelevant options, the performance of MRC models can be reduced from human-level performance to the chance-level performance. Human readers, however, are not clearly affected by these irrelevant options. Finally, we propose an augmented training method that can greatly reduce models\u2019 statistical biases.", "author": "Jieyu Lin; Jiajie Zou; Nai Ding", "authorids": "/j/jieyu-lin/; /j/jiajie-zou/; /n/nai-ding/", "bibtex": "@inproceedings{lin-etal-2021-using,\n title = \"Using Adversarial Attacks to Reveal the Statistical Bias in Machine Reading Comprehension Models\",\n author = \"Lin, Jieyu and\n Zou, Jiajie and\n Ding, Nai\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-short.43/\",\n doi = \"10.18653/v1/2021.acl-short.43\",\n pages = \"333--342\"\n}", "pdf": "https://aclanthology.org/2021.acl-short.43.pdf", "site": "https://aclanthology.org/2021.acl-short.43/", "pdf_size": 2040470, "gs_citation": 49, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=10026937114168528336&as_sdt=5,33&sciodt=0,33&hl=en", "gs_version_total": 6, "aff": "Zhejiang Lab / Hangzhou, China + Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering and Instrument Sciences, Zhejiang University / Hangzhou, China; Zhejiang Lab / Hangzhou, China + Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering and Instrument Sciences, Zhejiang University / Hangzhou, China; Zhejiang Lab / Hangzhou, China + Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering and Instrument Sciences, Zhejiang University / Hangzhou, China", "aff_domain": "zju.edu.cn;zju.edu.cn;zju.edu.cn", "email": "zju.edu.cn;zju.edu.cn;zju.edu.cn", "github": "", "project": "", "author_num": 3, "aff_unique_index": "0+1;0+1;0+1", "aff_unique_norm": "Zhejiang Lab;Zhejiang University", "aff_unique_dep": ";College of Biomedical Engineering and Instrument Sciences", "aff_unique_url": ";http://www.zju.edu.cn", "aff_unique_abbr": ";ZJU", "aff_campus_unique_index": "0+0;0+0;0+0", "aff_campus_unique": "Hangzhou", "aff_country_unique_index": "0+0;0+0;0+0", "aff_country_unique": "China" }, { "id": "2021.acl-long.545", "title": "Using Meta-Knowledge Mined from Identifiers to Improve Intent Recognition in Conversational Systems", "track": "main", "status": "Long", "award": false, "abstract": "In this paper we explore the improvement of intent recognition in conversational systems by the use of meta-knowledge embedded in intent identifiers. Developers often include such knowledge, structure as taxonomies, in the documentation of chatbots. By using neuro-symbolic algorithms to incorporate those taxonomies into embeddings of the output space, we were able to improve accuracy in intent recognition. In datasets with intents and example utterances from 200 professional chatbots, we saw decreases in the equal error rate (EER) in more than 40% of the chatbots in comparison to the baseline of the same algorithm without the meta-knowledge. The meta-knowledge proved also to be effective in detecting out-of-scope utterances, improving the false acceptance rate (FAR) in two thirds of the chatbots, with decreases of 0.05 or more in FAR in almost 40% of the chatbots. When considering only the well-developed workspaces with a high level use of taxonomies, FAR decreased more than 0.05 in 77% of them, and more than 0.1 in 39% of the chatbots.", "author": "Claudio Pinhanez; Paulo Cavalin; Victor Henrique Alves Ribeiro; Ana Appel; Heloisa Candello; Julio Nogima; Mauro Pichiliani; Melina Guerra; Maira de Bayser; Gabriel Malfatti; Henrique Ferreira", "authorids": "/c/claudio-pinhanez/; /p/paulo-cavalin/; /v/victor-henrique-alves-ribeiro/; /a/ana-appel/; /h/heloisa-candello/; /j/julio-nogima/; /m/mauro-pichiliani/; /m/melina-guerra/; /m/maira-de-bayser/; /g/gabriel-malfatti/; /h/henrique-ferreira/", "bibtex": "@inproceedings{pinhanez-etal-2021-using,\n title = \"Using Meta-Knowledge Mined from Identifiers to Improve Intent Recognition in Conversational Systems\",\n author = \"Pinhanez, Claudio and\n Cavalin, Paulo and\n Alves Ribeiro, Victor Henrique and\n Appel, Ana and\n Candello, Heloisa and\n Nogima, Julio and\n Pichiliani, Mauro and\n Guerra, Melina and\n de Bayser, Maira and\n Malfatti, Gabriel and\n Ferreira, Henrique\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.545/\",\n doi = \"10.18653/v1/2021.acl-long.545\",\n pages = \"7014--7027\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.545.pdf", "site": "https://aclanthology.org/2021.acl-long.545/", "pdf_size": 1685097, "gs_citation": 11, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=2352355210157883610&as_sdt=5,44&sciodt=0,44&hl=en", "gs_version_total": 5, "aff": "IBM Research - Brazil; IBM Research - Brazil; IBM Research - Brazil; IBM Research - Brazil; IBM Research - Brazil; IBM Research - Brazil; IBM Research - Brazil; IBM Research - Brazil; IBM Research - Brazil; IBM Research - Brazil; IBM Research - Brazil", "aff_domain": "br.ibm.com; ; ; ; ; ; ; ; ; ; ", "email": "br.ibm.com; ; ; ; ; ; ; ; ; ; ", "github": "", "project": "", "author_num": 11, "aff_unique_index": "0;0;0;0;0;0;0;0;0;0;0", "aff_unique_norm": "IBM Research", "aff_unique_dep": "Research", "aff_unique_url": "https://www.ibm.com/research", "aff_unique_abbr": "IBM", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0;0;0;0;0;0;0;0;0;0;0", "aff_country_unique": "Brazil" }, { "id": "2021.acl-short.131", "title": "VAULT: VAriable Unified Long Text Representation for Machine Reading Comprehension", "track": "main", "status": "Short", "award": false, "abstract": "Existing models on Machine Reading Comprehension (MRC) require complex model architecture for effectively modeling long texts with paragraph representation and classification, thereby making inference computationally inefficient for production use. In this work, we propose VAULT: a light-weight and parallel-efficient paragraph representation for MRC based on contextualized representation from long document input, trained using a new Gaussian distribution-based objective that pays close attention to the partially correct instances that are close to the ground-truth. We validate our VAULT architecture showing experimental results on two benchmark MRC datasets that require long context modeling; one Wikipedia-based (Natural Questions (NQ)) and the other on TechNotes (TechQA). VAULT can achieve comparable performance on NQ with a state-of-the-art (SOTA) complex document modeling approach while being 16 times faster, demonstrating the efficiency of our proposed model. We also demonstrate that our model can also be effectively adapted to a completely different domain \u2013 TechQA \u2013 with large improvement over a model fine-tuned on a previously published large PLM.", "author": "Haoyang Wen; Anthony Ferritto; Heng Ji; Radu Florian; Avi Sil", "authorids": "/h/haoyang-wen/; /a/anthony-ferritto/; /h/heng-ji/; /r/radu-florian/; /a/avirup-sil/", "bibtex": "@inproceedings{wen-etal-2021-vault,\n title = \"{VAULT}: {VA}riable Unified Long Text Representation for Machine Reading Comprehension\",\n author = \"Wen, Haoyang and\n Ferritto, Anthony and\n Ji, Heng and\n Florian, Radu and\n Sil, Avi\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-short.131/\",\n doi = \"10.18653/v1/2021.acl-short.131\",\n pages = \"1035--1042\"\n}", "pdf": "https://aclanthology.org/2021.acl-short.131.pdf", "site": "https://aclanthology.org/2021.acl-short.131/", "pdf_size": 2228595, "gs_citation": 3, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=14012825783324214562&as_sdt=5,48&sciodt=0,48&hl=en", "gs_version_total": 7, "aff": "University of Illinois Urbana-Champaign+IBM Research AI; IBM Research AI; University of Illinois Urbana-Champaign; IBM Research AI; IBM Research AI", "aff_domain": "illinois.edu;ibm.com;illinois.edu;us.ibm.com;us.ibm.com", "email": "illinois.edu;ibm.com;illinois.edu;us.ibm.com;us.ibm.com", "github": "", "project": "", "author_num": 5, "aff_unique_index": "0+1;1;0;1;1", "aff_unique_norm": "University of Illinois at Urbana-Champaign;IBM Research", "aff_unique_dep": ";AI", "aff_unique_url": "https://illinois.edu;https://www.ibm.com/research", "aff_unique_abbr": "UIUC;IBM", "aff_campus_unique_index": "0;0", "aff_campus_unique": "Urbana-Champaign;", "aff_country_unique_index": "0+0;0;0;0;0", "aff_country_unique": "United States" }, { "id": "2021.acl-long.308", "title": "VECO: Variable and Flexible Cross-lingual Pre-training for Language Understanding and Generation", "track": "main", "status": "Long", "award": false, "abstract": "Existing work in multilingual pretraining has demonstrated the potential of cross-lingual transferability by training a unified Transformer encoder for multiple languages. However, much of this work only relies on the shared vocabulary and bilingual contexts to encourage the correlation across languages, which is loose and implicit for aligning the contextual representations between languages. In this paper, we plug a cross-attention module into the Transformer encoder to explicitly build the interdependence between languages. It can effectively avoid the degeneration of predicting masked words only conditioned on the context in its own language. More importantly, when fine-tuning on downstream tasks, the cross-attention module can be plugged in or out on-demand, thus naturally benefiting a wider range of cross-lingual tasks, from language understanding to generation. As a result, the proposed cross-lingual model delivers new state-of-the-art results on various cross-lingual understanding tasks of the XTREME benchmark, covering text classification, sequence labeling, question answering, and sentence retrieval. For cross-lingual generation tasks, it also outperforms all existing cross-lingual models and state-of-the-art Transformer variants on WMT14 English-to-German and English-to-French translation datasets, with gains of up to 1 2 BLEU.", "author": "Fuli Luo; Wei Wang; Jiahao Liu; Yijia Liu; Bin Bi; Songfang Huang; Fei Huang; Luo Si", "authorids": "/f/fuli-luo/; /w/wei-wang/; /j/jiahao-liu/; /y/yijia-liu/; /b/bin-bi/; /s/songfang-huang/; /f/fei-huang/; /l/luo-si/", "bibtex": "@inproceedings{luo-etal-2021-veco,\n title = \"{VECO}: Variable and Flexible Cross-lingual Pre-training for Language Understanding and Generation\",\n author = \"Luo, Fuli and\n Wang, Wei and\n Liu, Jiahao and\n Liu, Yijia and\n Bi, Bin and\n Huang, Songfang and\n Huang, Fei and\n Si, Luo\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.308/\",\n doi = \"10.18653/v1/2021.acl-long.308\",\n pages = \"3980--3994\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.308.pdf", "site": "https://aclanthology.org/2021.acl-long.308/", "pdf_size": 1331620, "gs_citation": 50, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=2287770652472785559&as_sdt=5,44&sciodt=0,44&hl=en", "gs_version_total": 4, "aff": "Alibaba Group; Alibaba Group; Alibaba Group; Alibaba Group; Alibaba Group; Alibaba Group; Alibaba Group; Alibaba Group", "aff_domain": "alibaba-inc.com;alibaba-inc.com;alibaba-inc.com;alibaba-inc.com;alibaba-inc.com;alibaba-inc.com;alibaba-inc.com;alibaba-inc.com", "email": "alibaba-inc.com;alibaba-inc.com;alibaba-inc.com;alibaba-inc.com;alibaba-inc.com;alibaba-inc.com;alibaba-inc.com;alibaba-inc.com", "github": "https://github.com/alibaba/AliceMind/tree/main/VECO", "project": "", "author_num": 8, "aff_unique_index": "0;0;0;0;0;0;0;0", "aff_unique_norm": "Alibaba Group", "aff_unique_dep": "", "aff_unique_url": "https://www.alibaba.com", "aff_unique_abbr": "Alibaba", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0;0;0;0;0;0;0;0", "aff_country_unique": "China" }, { "id": "2021.acl-long.284", "title": "Value-Agnostic Conversational Semantic Parsing", "track": "main", "status": "Long", "award": false, "abstract": "Conversational semantic parsers map user utterances to executable programs given dialogue histories composed of previous utterances, programs, and system responses. Existing parsers typically condition on rich representations of history that include the complete set of values and computations previously discussed. We propose a model that abstracts over values to focus prediction on type- and function-level context. This approach provides a compact encoding of dialogue histories and predicted programs, improving generalization and computational efficiency. Our model incorporates several other components, including an atomic span copy operation and structural enforcement of well-formedness constraints on predicted programs, that are particularly advantageous in the low-data regime. Trained on the SMCalFlow and TreeDST datasets, our model outperforms prior work by 7.3% and 10.6% respectively in terms of absolute accuracy. Trained on only a thousand examples from each dataset, it outperforms strong baselines by 12.4% and 6.4%. These results indicate that simple representations are key to effective generalization in conversational semantic parsing.", "author": "Emmanouil Antonios Platanios; Adam Pauls; Subhro Roy; Yuchen Zhang; Alexander Kyte; Alan Guo; Sam Thomson; Jayant Krishnamurthy; Jason Wolfe; Jacob Andreas; Dan Klein", "authorids": "/e/emmanouil-antonios-platanios/; /a/adam-pauls/; /s/subhro-roy/; /y/yuchen-zhang/; /a/alexander-kyte/; /a/alan-guo/; /s/sam-thomson/; /j/jayant-krishnamurthy/; /j/jason-wolfe/; /j/jacob-andreas/; /d/dan-klein/", "bibtex": "@inproceedings{platanios-etal-2021-value,\n title = \"Value-Agnostic Conversational Semantic Parsing\",\n author = \"Platanios, Emmanouil Antonios and\n Pauls, Adam and\n Roy, Subhro and\n Zhang, Yuchen and\n Kyte, Alexander and\n Guo, Alan and\n Thomson, Sam and\n Krishnamurthy, Jayant and\n Wolfe, Jason and\n Andreas, Jacob and\n Klein, Dan\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.284/\",\n doi = \"10.18653/v1/2021.acl-long.284\",\n pages = \"3666--3681\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.284.pdf", "site": "https://aclanthology.org/2021.acl-long.284/", "pdf_size": 632453, "gs_citation": 4, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=13420727702144007965&as_sdt=5,33&sciodt=0,33&hl=en", "gs_version_total": 5, "aff": "Microsoft Semantic Machines; ; ; ; ; ; ; ; ; ; ", "aff_domain": "microsoft.com; ; ; ; ; ; ; ; ; ; ", "email": "microsoft.com; ; ; ; ; ; ; ; ; ; ", "github": "", "project": "", "author_num": 11, "aff_unique_index": "0", "aff_unique_norm": "Microsoft", "aff_unique_dep": "Semantic Machines", "aff_unique_url": "https://www.microsoft.com", "aff_unique_abbr": "Microsoft", "aff_country_unique_index": "0", "aff_country_unique": "United States" }, { "id": "2021.acl-long.541", "title": "Verb Knowledge Injection for Multilingual Event Processing", "track": "main", "status": "Long", "award": false, "abstract": "Linguistic probing of pretrained Transformer-based language models (LMs) revealed that they encode a range of syntactic and semantic properties of a language. However, they are still prone to fall back on superficial cues and simple heuristics to solve downstream tasks, rather than leverage deeper linguistic information. In this paper, we target a specific facet of linguistic knowledge, the interplay between verb meaning and argument structure. We investigate whether injecting explicit information on verbs\u2019 semantic-syntactic behaviour improves the performance of pretrained LMs in event extraction tasks, where accurate verb processing is paramount. Concretely, we impart the verb knowledge from curated lexical resources into dedicated adapter modules (verb adapters), allowing it to complement, in downstream tasks, the language knowledge obtained during LM-pretraining. We first demonstrate that injecting verb knowledge leads to performance gains in English event extraction. We then explore the utility of verb adapters for event extraction in other languages: we investigate 1) zero-shot language transfer with multilingual Transformers and 2) transfer via (noisy automatic) translation of English verb-based lexical knowledge. Our results show that the benefits of verb knowledge injection indeed extend to other languages, even when relying on noisily translated lexical knowledge.", "author": "Olga Majewska; Ivan Vuli\u0107; Goran Glava\u0161; Edoardo Maria Ponti; Anna Korhonen", "authorids": "/o/olga-majewska/; /i/ivan-vulic/; /g/goran-glavas/; /e/edoardo-maria-ponti/; /a/anna-korhonen/", "bibtex": "@inproceedings{majewska-etal-2021-verb,\n title = \"Verb Knowledge Injection for Multilingual Event Processing\",\n author = \"Majewska, Olga and\n Vuli{\\'c}, Ivan and\n Glava{\\v{s}}, Goran and\n Ponti, Edoardo Maria and\n Korhonen, Anna\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.541/\",\n doi = \"10.18653/v1/2021.acl-long.541\",\n pages = \"6952--6969\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.541.pdf", "site": "https://aclanthology.org/2021.acl-long.541/", "pdf_size": 611565, "gs_citation": 17, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=11381343741865243524&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 6, "aff": "Language Technology Lab, TAL, University of Cambridge, UK + Mila \u2013 Quebec AI Institute, Montreal, Canada; Language Technology Lab, TAL, University of Cambridge, UK + Mila \u2013 Quebec AI Institute, Montreal, Canada; Data and Web Science Group, University of Mannheim, Germany; Language Technology Lab, TAL, University of Cambridge, UK + Mila \u2013 Quebec AI Institute, Montreal, Canada; Language Technology Lab, TAL, University of Cambridge, UK", "aff_domain": "cam.ac.uk;cam.ac.uk;informatik.uni-mannheim.de;cam.ac.uk;cam.ac.uk", "email": "cam.ac.uk;cam.ac.uk;informatik.uni-mannheim.de;cam.ac.uk;cam.ac.uk", "github": "", "project": "", "author_num": 5, "aff_unique_index": "0+1;0+1;2;0+1;0", "aff_unique_norm": "University of Cambridge;Quebec AI Institute;University of Mannheim", "aff_unique_dep": "Language Technology Lab, TAL;AI Institute;Data and Web Science Group", "aff_unique_url": "https://www.cam.ac.uk;https://mila.quebec;https://www.uni-mannheim.de", "aff_unique_abbr": "Cambridge;Mila;", "aff_campus_unique_index": "0+1;0+1;0+1;0", "aff_campus_unique": "Cambridge;Montreal;", "aff_country_unique_index": "0+1;0+1;2;0+1;0", "aff_country_unique": "United Kingdom;Canada;Germany" }, { "id": "2021.acl-long.327", "title": "Verb Metaphor Detection via Contextual Relation Learning", "track": "main", "status": "Long", "award": false, "abstract": "Correct natural language understanding requires computers to distinguish the literal and metaphorical senses of a word. Recent neu- ral models achieve progress on verb metaphor detection by viewing it as sequence labeling. In this paper, we argue that it is appropriate to view this task as relation classification between a verb and its various contexts. We propose the Metaphor-relation BERT (Mr-BERT) model, which explicitly models the relation between a verb and its grammatical, sentential and semantic contexts. We evaluate our method on the VUA, MOH-X and TroFi datasets. Our method gets competitive results compared with state-of-the-art approaches.", "author": "Wei Song; Shuhui Zhou; Ruiji Fu; Ting Liu; Lizhen Liu", "authorids": "/w/wei-song/; /s/shuhui-zhou/; /r/ruiji-fu/; /t/ting-liu/; /l/lizhen-liu/", "bibtex": "@inproceedings{song-etal-2021-verb,\n title = \"Verb Metaphor Detection via Contextual Relation Learning\",\n author = \"Song, Wei and\n Zhou, Shuhui and\n Fu, Ruiji and\n Liu, Ting and\n Liu, Lizhen\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.327/\",\n doi = \"10.18653/v1/2021.acl-long.327\",\n pages = \"4240--4251\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.327.pdf", "site": "https://aclanthology.org/2021.acl-long.327/", "pdf_size": 443431, "gs_citation": 34, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=11347003952194020976&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 3, "aff": "College of Information Engineering and Academy for Multidisciplinary Studies, Capital Normal University, Beijing, China; College of Information Engineering and Academy for Multidisciplinary Studies, Capital Normal University, Beijing, China; State Key Laboratory of Cognitive Intelligence, iFLYTEK Research, China+ iFLYTEK AI Research (Hebei), Langfang, China; Research Center for Social Computing and Information Retrieval, Harbin Institute of Technology, Harbin, China; College of Information Engineering and Academy for Multidisciplinary Studies, Capital Normal University, Beijing, China", "aff_domain": "cnu.edu.cn;cnu.edu.cn;iflytek.com;ir.hit.edu.cn;cnu.edu.cn", "email": "cnu.edu.cn;cnu.edu.cn;iflytek.com;ir.hit.edu.cn;cnu.edu.cn", "github": "", "project": "", "author_num": 5, "aff_unique_index": "0;0;1+2;3;0", "aff_unique_norm": "Capital Normal University;iFLYTEK Research;iFLYTEK AI Research;Harbin Institute of Technology", "aff_unique_dep": "College of Information Engineering and Academy for Multidisciplinary Studies;State Key Laboratory of Cognitive Intelligence;AI Research;Research Center for Social Computing and Information Retrieval", "aff_unique_url": "http://www.cnu.edu.cn;https://www.iflytek.com;https://www.iflytek.com;http://www.hit.edu.cn/", "aff_unique_abbr": "CNU;iFLYTEK;iFLYTEK;HIT", "aff_campus_unique_index": "0;0;2;3;0", "aff_campus_unique": "Beijing;;Langfang;Harbin", "aff_country_unique_index": "0;0;0+0;0;0", "aff_country_unique": "China" }, { "id": "2021.acl-short.9", "title": "Video Paragraph Captioning as a Text Summarization Task", "track": "main", "status": "Short", "award": false, "abstract": "Video paragraph captioning aims to generate a set of coherent sentences to describe a video that contains several events. Most previous methods simplify this task by using ground-truth event segments. In this work, we propose a novel framework by taking this task as a text summarization task. We first generate lots of sentence-level captions focusing on different video clips and then summarize these captions to obtain the final paragraph caption. Our method does not depend on ground-truth event segments. Experiments on two popular datasets ActivityNet Captions and YouCookII demonstrate the advantages of our new framework. On the ActivityNet dataset, our method even outperforms some previous methods using ground-truth event segment labels.", "author": "Hui Liu; Xiaojun Wan", "authorids": "/h/hui-liu/; /x/xiaojun-wan/", "bibtex": "@inproceedings{liu-wan-2021-video,\n title = \"Video Paragraph Captioning as a Text Summarization Task\",\n author = \"Liu, Hui and\n Wan, Xiaojun\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-short.9/\",\n doi = \"10.18653/v1/2021.acl-short.9\",\n pages = \"55--60\"\n}", "pdf": "https://aclanthology.org/2021.acl-short.9.pdf", "site": "https://aclanthology.org/2021.acl-short.9/", "pdf_size": 1652994, "gs_citation": 16, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=14451488847966152837&as_sdt=5,33&sciodt=0,33&hl=en", "gs_version_total": 2, "aff": "Wangxuan Institute of Computer Technology, Peking University; The MOE Key Laboratory of Computational Linguistics, Peking University", "aff_domain": "pku.edu.cn;pku.edu.cn", "email": "pku.edu.cn;pku.edu.cn", "github": "", "project": "", "author_num": 2, "aff_unique_index": "0;0", "aff_unique_norm": "Peking University", "aff_unique_dep": "Wangxuan Institute of Computer Technology", "aff_unique_url": "http://www.pku.edu.cn", "aff_unique_abbr": "PKU", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0;0", "aff_country_unique": "China" }, { "id": "2021.acl-long.389", "title": "VisualSparta: An Embarrassingly Simple Approach to Large-scale Text-to-Image Search with Weighted Bag-of-words", "track": "main", "status": "Long", "award": false, "abstract": "Text-to-image retrieval is an essential task in cross-modal information retrieval, i.e., retrieving relevant images from a large and unlabelled dataset given textual queries. In this paper, we propose VisualSparta, a novel (Visual-text Sparse Transformer Matching) model that shows significant improvement in terms of both accuracy and efficiency. VisualSparta is capable of outperforming previous state-of-the-art scalable methods in MSCOCO and Flickr30K. We also show that it achieves substantial retrieving speed advantages, i.e., for a 1 million image index, VisualSparta using CPU gets ~391X speedup compared to CPU vector search and ~5.4X speedup compared to vector search with GPU acceleration. Experiments show that this speed advantage even gets bigger for larger datasets because VisualSparta can be efficiently implemented as an inverted index. To the best of our knowledge, VisualSparta is the first transformer-based text-to-image retrieval model that can achieve real-time searching for large-scale datasets, with significant accuracy improvement compared to previous state-of-the-art methods.", "author": "Xiaopeng Lu; Tiancheng Zhao; Kyusong Lee", "authorids": "/x/xiaopeng-lu/; /t/tiancheng-zhao/; /k/kyusong-lee/", "bibtex": "@inproceedings{lu-etal-2021-visualsparta,\n title = \"{V}isual{S}parta: An Embarrassingly Simple Approach to Large-scale Text-to-Image Search with Weighted Bag-of-words\",\n author = \"Lu, Xiaopeng and\n Zhao, Tiancheng and\n Lee, Kyusong\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.389/\",\n doi = \"10.18653/v1/2021.acl-long.389\",\n pages = \"5020--5029\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.389.pdf", "site": "https://aclanthology.org/2021.acl-long.389/", "pdf_size": 3723990, "gs_citation": 29, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=11189507706857220837&as_sdt=5,44&sciodt=0,44&hl=en", "gs_version_total": 5, "aff": "Language Technologies Institute, Carnegie Mellon University; SOCO Inc; SOCO Inc", "aff_domain": "andrew.cmu.edu;soco.ai;soco.ai", "email": "andrew.cmu.edu;soco.ai;soco.ai", "github": "", "project": "", "author_num": 3, "aff_unique_index": "0;1;1", "aff_unique_norm": "Carnegie Mellon University;SOCO Inc", "aff_unique_dep": "Language Technologies Institute;", "aff_unique_url": "https://www.cmu.edu;", "aff_unique_abbr": "CMU;", "aff_campus_unique_index": "0", "aff_campus_unique": "Pittsburgh;", "aff_country_unique_index": "0;0;0", "aff_country_unique": "United States" }, { "id": "2021.acl-long.571", "title": "Vocabulary Learning via Optimal Transport for Neural Machine Translation", "track": "main", "status": "Long", "award": true, "abstract": "The choice of token vocabulary affects the performance of machine translation. This paper aims to figure out what is a good vocabulary and whether we can find the optimal vocabulary without trial training. To answer these questions, we first provide an alternative understanding of vocabulary from the perspective of information theory. It motivates us to formulate the quest of vocabularization \u2013 finding the best token dictionary with a proper size \u2013 as an optimal transport (OT) problem. We propose VOLT, a simple and efficient solution without trial training. Empirical results show that VOLT beats widely-used vocabularies in diverse scenarios, including WMT-14 English-German translation, TED bilingual translation, and TED multilingual translation. For example, VOLT achieves 70% vocabulary size reduction and 0.5 BLEU gain on English-German translation. Also, compared to BPE-search, VOLT reduces the search time from 384 GPU hours to 30 GPU hours on English-German translation. Codes are available at https://github.com/Jingjing-NLP/VOLT.", "author": "Jingjing Xu; Hao Zhou; Chun Gan; Zaixiang Zheng; Lei Li", "authorids": "/j/jingjing-xu/; /h/hao-zhou/; /c/chun-gan/; /z/zaixiang-zheng/; /l/lei-li/", "bibtex": "@inproceedings{xu-etal-2021-vocabulary,\n title = \"Vocabulary Learning via Optimal Transport for Neural Machine Translation\",\n author = \"Xu, Jingjing and\n Zhou, Hao and\n Gan, Chun and\n Zheng, Zaixiang and\n Li, Lei\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.571/\",\n doi = \"10.18653/v1/2021.acl-long.571\",\n pages = \"7361--7373\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.571.pdf", "site": "https://aclanthology.org/2021.acl-long.571/", "pdf_size": 674000, "gs_citation": 104, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=18211915954274852549&as_sdt=5,33&sciodt=0,33&hl=en", "gs_version_total": 10, "aff": "ByteDance AI Lab; ByteDance AI Lab; ByteDance AI Lab + Math Department, University of Wisconsin\u2013Madison; ByteDance AI Lab + Nanjing University; ByteDance AI Lab", "aff_domain": "bytedance.com;bytedance.com;wisc.edu;smail.nju.edu.cn;bytedance.com", "email": "bytedance.com;bytedance.com;wisc.edu;smail.nju.edu.cn;bytedance.com", "github": "https://github.com/Jingjing-NLP/VOLT", "project": "", "author_num": 5, "aff_unique_index": "0;0;0+1;0+2;0", "aff_unique_norm": "ByteDance;University of Wisconsin\u2013Madison;Nanjing University", "aff_unique_dep": "AI Lab;Math Department;", "aff_unique_url": "https://www.bytedance.com;https://www.wisc.edu;https://www.nju.edu.cn", "aff_unique_abbr": "ByteDance;UW\u2013Madison;Nanjing U", "aff_campus_unique_index": "1;", "aff_campus_unique": ";Madison", "aff_country_unique_index": "0;0;0+1;0+0;0", "aff_country_unique": "China;United States" }, { "id": "2021.acl-long.80", "title": "VoxPopuli: A Large-Scale Multilingual Speech Corpus for Representation Learning, Semi-Supervised Learning and Interpretation", "track": "main", "status": "Long", "award": false, "abstract": "We introduce VoxPopuli, a large-scale multilingual corpus providing 400K hours of unlabeled speech data in 23 languages. It is the largest open data to date for unsupervised representation learning as well as semi-supervised learning. VoxPopuli also contains 1.8K hours of transcribed speeches in 15 languages and their aligned oral interpretations into 15 target languages totaling 17.3K hours. We provide speech recognition (ASR) baselines and validate the versatility of VoxPopuli unlabeled data in semi-supervised ASR and speech-to-text translation under challenging out-of-domain settings. The corpus is available at https://github.com/facebookresearch/voxpopuli.", "author": "Changhan Wang; Morgane Riviere; Ann Lee; Anne Wu; Chaitanya Talnikar; Daniel Haziza; Mary Williamson; Juan Pino; Emmanuel Dupoux", "authorids": "/c/changhan-wang/; /m/morgane-riviere/; /a/ann-lee/; /a/anne-wu/; /c/chaitanya-talnikar/; /d/daniel-haziza/; /m/mary-williamson/; /j/juan-pino/; /e/emmanuel-dupoux/", "bibtex": "@inproceedings{wang-etal-2021-voxpopuli,\n title = \"{V}ox{P}opuli: A Large-Scale Multilingual Speech Corpus for Representation Learning, Semi-Supervised Learning and Interpretation\",\n author = \"Wang, Changhan and\n Riviere, Morgane and\n Lee, Ann and\n Wu, Anne and\n Talnikar, Chaitanya and\n Haziza, Daniel and\n Williamson, Mary and\n Pino, Juan and\n Dupoux, Emmanuel\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.80/\",\n doi = \"10.18653/v1/2021.acl-long.80\",\n pages = \"993--1003\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.80.pdf", "site": "https://aclanthology.org/2021.acl-long.80/", "pdf_size": 290528, "gs_citation": 533, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=18142184371221338974&as_sdt=400005&sciodt=0,14&hl=en", "gs_version_total": 10, "aff": "Facebook AI; Facebook AI; Facebook AI; Facebook AI; Facebook AI; Facebook AI; Facebook AI; Facebook AI; Facebook AI", "aff_domain": "fb.com;fb.com;fb.com;fb.com;fb.com;fb.com;fb.com;fb.com;fb.com", "email": "fb.com;fb.com;fb.com;fb.com;fb.com;fb.com;fb.com;fb.com;fb.com", "github": "https://github.com/facebookresearch/voxpopuli", "project": "", "author_num": 9, "aff_unique_index": "0;0;0;0;0;0;0;0;0", "aff_unique_norm": "Facebook", "aff_unique_dep": "Facebook AI", "aff_unique_url": "https://www.facebook.com", "aff_unique_abbr": "Facebook AI", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0;0;0;0;0;0;0;0;0", "aff_country_unique": "United States" }, { "id": "2021.acl-long.302", "title": "W-RST: Towards a Weighted RST-style Discourse Framework", "track": "main", "status": "Long", "award": false, "abstract": "Aiming for a better integration of data-driven and linguistically-inspired approaches, we explore whether RST Nuclearity, assigning a binary assessment of importance between text segments, can be replaced by automatically generated, real-valued scores, in what we call a Weighted-RST framework. In particular, we find that weighted discourse trees from auxiliary tasks can benefit key NLP downstream applications, compared to nuclearity-centered approaches. We further show that real-valued importance distributions partially and interestingly align with the assessment and uncertainty of human annotators.", "author": "Patrick Huber; Wen Xiao; Giuseppe Carenini", "authorids": "/p/patrick-huber/; /w/wen-xiao/; /g/giuseppe-carenini/", "bibtex": "@inproceedings{huber-etal-2021-w,\n title = \"{W}-{RST}: Towards a Weighted {RST}-style Discourse Framework\",\n author = \"Huber, Patrick and\n Xiao, Wen and\n Carenini, Giuseppe\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.302/\",\n doi = \"10.18653/v1/2021.acl-long.302\",\n pages = \"3908--3918\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.302.pdf", "site": "https://aclanthology.org/2021.acl-long.302/", "pdf_size": 1465558, "gs_citation": 5, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=13343437484828294663&as_sdt=5,33&sciodt=0,33&hl=en", "gs_version_total": 4, "aff": "Department of Computer Science, University of British Columbia; Department of Computer Science, University of British Columbia; Department of Computer Science, University of British Columbia", "aff_domain": "cs.ubc.ca;cs.ubc.ca;cs.ubc.ca", "email": "cs.ubc.ca;cs.ubc.ca;cs.ubc.ca", "github": "", "project": "", "author_num": 3, "aff_unique_index": "0;0;0", "aff_unique_norm": "University of British Columbia", "aff_unique_dep": "Department of Computer Science", "aff_unique_url": "https://www.ubc.ca", "aff_unique_abbr": "UBC", "aff_campus_unique_index": "0;0;0", "aff_campus_unique": "Vancouver", "aff_country_unique_index": "0;0;0", "aff_country_unique": "Canada" }, { "id": "2021.acl-long.381", "title": "WARP: Word-level Adversarial ReProgramming", "track": "main", "status": "Long", "award": false, "abstract": "Transfer learning from pretrained language models recently became the dominant approach for solving many NLP tasks. A common approach to transfer learning for multiple tasks that maximize parameter sharing trains one or more task-specific layers on top of the language model. In this paper, we present an alternative approach based on adversarial reprogramming, which extends earlier work on automatic prompt generation. Adversarial reprogramming attempts to learn task-specific word embeddings that, when concatenated to the input text, instruct the language model to solve the specified task. Using up to 25K trainable parameters per task, this approach outperforms all existing methods with up to 25M trainable parameters on the public leaderboard of the GLUE benchmark. Our method, initialized with task-specific human-readable prompts, also works in a few-shot setting, outperforming GPT-3 on two SuperGLUE tasks with just 32 training samples.", "author": "Karen Hambardzumyan; Hrant Khachatrian; Jonathan May", "authorids": "/k/karen-hambardzumyan/; /h/hrant-khachatrian/; /j/jonathan-may/", "bibtex": "@inproceedings{hambardzumyan-etal-2021-warp,\n title = \"{WARP}: {W}ord-level {A}dversarial {R}e{P}rogramming\",\n author = \"Hambardzumyan, Karen and\n Khachatrian, Hrant and\n May, Jonathan\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.381/\",\n doi = \"10.18653/v1/2021.acl-long.381\",\n pages = \"4921--4933\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.381.pdf", "site": "https://aclanthology.org/2021.acl-long.381/", "pdf_size": 1674867, "gs_citation": 386, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=5419022154621277948&as_sdt=5,33&sciodt=0,33&hl=en", "gs_version_total": 8, "aff": "YerevaNN; YerevaNN+Yerevan State University; Information Sciences Institute, University of Southern California", "aff_domain": "yerevann.com;yerevann.com;isi.edu", "email": "yerevann.com;yerevann.com;isi.edu", "github": "https://github.com/YerevaNN/WARP", "project": "", "author_num": 3, "aff_unique_index": "0;0+1;2", "aff_unique_norm": "YerevaNN;Yerevan State University;University of Southern California", "aff_unique_dep": ";;Information Sciences Institute", "aff_unique_url": ";https://www.yerevanstateuniversity.am;https://www.usc.edu", "aff_unique_abbr": ";YSU;USC", "aff_campus_unique_index": ";1", "aff_campus_unique": ";Los Angeles", "aff_country_unique_index": "0;0+0;1", "aff_country_unique": "Armenia;United States" }, { "id": "2021.acl-long.352", "title": "Weakly Supervised Named Entity Tagging with Learnable Logical Rules", "track": "main", "status": "Long", "award": false, "abstract": "We study the problem of building entity tagging systems by using a few rules as weak supervision. Previous methods mostly focus on disambiguating entity types based on contexts and expert-provided rules, while assuming entity spans are given. In this work, we propose a novel method TALLOR that bootstraps high-quality logical rules to train a neural tagger in a fully automated manner. Specifically, we introduce compound rules that are composed from simple rules to increase the precision of boundary detection and generate more diverse pseudo labels. We further design a dynamic label selection strategy to ensure pseudo label quality and therefore avoid overfitting the neural tagger. Experiments on three datasets demonstrate that our method outperforms other weakly supervised methods and even rivals a state-of-the-art distantly supervised tagger with a lexicon of over 2,000 terms when starting from only 20 simple rules. Our method can serve as a tool for rapidly building taggers in emerging domains and tasks. Case studies show that learned rules can potentially explain the predicted entities.", "author": "Jiacheng Li; Haibo Ding; Jingbo Shang; Julian McAuley; Zhe Feng", "authorids": "/j/jiacheng-li/; /h/haibo-ding/; /j/jingbo-shang/; /j/julian-mcauley/; /z/zhe-feng/", "bibtex": "@inproceedings{li-etal-2021-weakly,\n title = \"Weakly Supervised Named Entity Tagging with Learnable Logical Rules\",\n author = \"Li, Jiacheng and\n Ding, Haibo and\n Shang, Jingbo and\n McAuley, Julian and\n Feng, Zhe\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.352/\",\n doi = \"10.18653/v1/2021.acl-long.352\",\n pages = \"4568--4581\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.352.pdf", "site": "https://aclanthology.org/2021.acl-long.352/", "pdf_size": 800009, "gs_citation": 39, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=9306111756377263996&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 6, "aff": "University of California, San Diego1; Bosch Research North America2; University of California, San Diego1; University of California, San Diego1; Bosch Research North America2", "aff_domain": "eng.ucsd.edu;us.bosch.com;eng.ucsd.edu;eng.ucsd.edu;us.bosch.com", "email": "eng.ucsd.edu;us.bosch.com;eng.ucsd.edu;eng.ucsd.edu;us.bosch.com", "github": "", "project": "", "author_num": 5, "aff_unique_index": "0;1;0;0;1", "aff_unique_norm": "University of California, San Diego;Bosch Research North America", "aff_unique_dep": ";", "aff_unique_url": "https://ucsd.edu;https://research.bosch.com/northamerica", "aff_unique_abbr": "UCSD;", "aff_campus_unique_index": "0;0;0", "aff_campus_unique": "San Diego;", "aff_country_unique_index": "0;0;0;0;0", "aff_country_unique": "United States" }, { "id": "2021.acl-short.133", "title": "Weakly-Supervised Methods for Suicide Risk Assessment: Role of Related Domains", "track": "main", "status": "Short", "award": false, "abstract": "Social media has become a valuable resource for the study of suicidal ideation and the assessment of suicide risk. Among social media platforms, Reddit has emerged as the most promising one due to its anonymity and its focus on topic-based communities (subreddits) that can be indicative of someone\u2019s state of mind or interest regarding mental health disorders such as r/SuicideWatch, r/Anxiety, r/depression. A challenge for previous work on suicide risk assessment has been the small amount of labeled data. We propose an empirical investigation into several classes of weakly-supervised approaches, and show that using pseudo-labeling based on related issues around mental health (e.g., anxiety, depression) helps improve model performance for suicide risk assessment.", "author": "Chenghao Yang; Yudong Zhang; Smaranda Muresan", "authorids": "/c/chenghao-yang/; /y/yudong-zhang/; /s/smaranda-muresan/", "bibtex": "@inproceedings{yang-etal-2021-weakly,\n title = \"Weakly-Supervised Methods for Suicide Risk Assessment: Role of Related Domains\",\n author = \"Yang, Chenghao and\n Zhang, Yudong and\n Muresan, Smaranda\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-short.133/\",\n doi = \"10.18653/v1/2021.acl-short.133\",\n pages = \"1049--1057\"\n}", "pdf": "https://aclanthology.org/2021.acl-short.133.pdf", "site": "https://aclanthology.org/2021.acl-short.133/", "pdf_size": 399357, "gs_citation": 7, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=9694999396483255094&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 4, "aff": "Department of Computer Science, Columbia University1 + Data Science Institute, Columbia University2; Department of Computer Science, Columbia University1 + Data Science Institute, Columbia University2; Department of Computer Science, Columbia University1 + Data Science Institute, Columbia University2", "aff_domain": "gmail.com;columbia.edu;columbia.edu", "email": "gmail.com;columbia.edu;columbia.edu", "github": "https://github.com/yangalan123/WM-SRA", "project": "", "author_num": 3, "aff_unique_index": "0+0;0+0;0+0", "aff_unique_norm": "Columbia University", "aff_unique_dep": "Department of Computer Science", "aff_unique_url": "https://www.columbia.edu", "aff_unique_abbr": "Columbia", "aff_campus_unique_index": ";;", "aff_campus_unique": "", "aff_country_unique_index": "0+0;0+0;0+0", "aff_country_unique": "United States" }, { "id": "2021.acl-long.162", "title": "Weight Distillation: Transferring the Knowledge in Neural Network Parameters", "track": "main", "status": "Long", "award": false, "abstract": "Knowledge distillation has been proven to be effective in model acceleration and compression. It transfers knowledge from a large neural network to a small one by using the large neural network predictions as targets of the small neural network. But this way ignores the knowledge inside the large neural networks, e.g., parameters. Our preliminary study as well as the recent success in pre-training suggests that transferring parameters are more effective in distilling knowledge. In this paper, we propose Weight Distillation to transfer the knowledge in parameters of a large neural network to a small neural network through a parameter generator. On the WMT16 En-Ro, NIST12 Zh-En, and WMT14 En-De machine translation tasks, our experiments show that weight distillation learns a small network that is 1.88 2.94x faster than the large network but with competitive BLEU performance. When fixing the size of small networks, weight distillation outperforms knowledge distillation by 0.51 1.82 BLEU points.", "author": "Ye Lin; Yanyang Li; Ziyang Wang; Bei Li; Quan Du; Tong Xiao; Jingbo Zhu", "authorids": "/y/ye-lin/; /y/yanyang-li/; /z/ziyang-wang/; /b/bei-li/; /q/quan-du/; /t/tong-xiao/; /j/jingbo-zhu/", "bibtex": "@inproceedings{lin-etal-2021-weight,\n title = \"Weight Distillation: Transferring the Knowledge in Neural Network Parameters\",\n author = \"Lin, Ye and\n Li, Yanyang and\n Wang, Ziyang and\n Li, Bei and\n Du, Quan and\n Xiao, Tong and\n Zhu, Jingbo\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.162/\",\n doi = \"10.18653/v1/2021.acl-long.162\",\n pages = \"2076--2088\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.162.pdf", "site": "https://aclanthology.org/2021.acl-long.162/", "pdf_size": 385077, "gs_citation": 26, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=12278578291425901585&as_sdt=5,44&sciodt=0,44&hl=en", "gs_version_total": 7, "aff": "NLP Lab, School of Computer Science and Engineering, Northeastern University, Shenyang, China; The Chinese University of Hong Kong, Hong Kong, China; NLP Lab, School of Computer Science and Engineering, Northeastern University, Shenyang, China; NLP Lab, School of Computer Science and Engineering, Northeastern University, Shenyang, China; NLP Lab, School of Computer Science and Engineering, Northeastern University, Shenyang, China; NLP Lab, School of Computer Science and Engineering, Northeastern University, Shenyang, China + NiuTrans Research, Shenyang, China; NLP Lab, School of Computer Science and Engineering, Northeastern University, Shenyang, China + NiuTrans Research, Shenyang, China", "aff_domain": "outlook.com;outlook.com;stumail.neu.edu.cn;outlook.com;outlook.com;mail.neu.edu.cn;mail.neu.edu.cn", "email": "outlook.com;outlook.com;stumail.neu.edu.cn;outlook.com;outlook.com;mail.neu.edu.cn;mail.neu.edu.cn", "github": "", "project": "", "author_num": 7, "aff_unique_index": "0;1;0;0;0;0+2;0+2", "aff_unique_norm": "Northeastern University;The Chinese University of Hong Kong;NiuTrans Research", "aff_unique_dep": "School of Computer Science and Engineering;;", "aff_unique_url": "http://www.neu.edu.cn/;https://www.cuhk.edu.hk;", "aff_unique_abbr": "NEU;CUHK;", "aff_campus_unique_index": "0;1;0;0;0;0;0", "aff_campus_unique": "Shenyang;Hong Kong;", "aff_country_unique_index": "0;0;0;0;0;0+0;0+0", "aff_country_unique": "China" }, { "id": "2021.acl-long.70", "title": "What Context Features Can Transformer Language Models Use?", "track": "main", "status": "Long", "award": false, "abstract": "Transformer-based language models benefit from conditioning on contexts of hundreds to thousands of previous tokens. What aspects of these contexts contribute to accurate model prediction? We describe a series of experiments that measure usable information by selectively ablating lexical and structural information in transformer language models trained on English Wikipedia. In both mid- and long-range contexts, we find that several extremely destructive context manipulations\u2014including shuffling word order within sentences and deleting all words other than nouns\u2014remove less than 15% of the usable information. Our results suggest that long contexts, but not their detailed syntactic and propositional content, are important for the low perplexity of current transformer language models.", "author": "Joe O\u2019Connor; Jacob Andreas", "authorids": "/j/joe-oconnor/; /j/jacob-andreas/", "bibtex": "@inproceedings{oconnor-andreas-2021-context,\n title = \"What Context Features Can Transformer Language Models Use?\",\n author = \"O{'}Connor, Joe and\n Andreas, Jacob\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.70/\",\n doi = \"10.18653/v1/2021.acl-long.70\",\n pages = \"851--864\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.70.pdf", "site": "https://aclanthology.org/2021.acl-long.70/", "pdf_size": 1368233, "gs_citation": 77, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=3084165283168424574&as_sdt=5,33&sciodt=0,33&hl=en", "gs_version_total": 5, "aff": "Massachusetts Institute of Technology; Massachusetts Institute of Technology", "aff_domain": "mit.edu;mit.edu", "email": "mit.edu;mit.edu", "github": "https://github.com/lingo-mit/context-ablations", "project": "", "author_num": 2, "aff_unique_index": "0;0", "aff_unique_norm": "Massachusetts Institute of Technology", "aff_unique_dep": "", "aff_unique_url": "https://web.mit.edu", "aff_unique_abbr": "MIT", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0;0", "aff_country_unique": "United States" }, { "id": "2021.acl-long.98", "title": "What Ingredients Make for an Effective Crowdsourcing Protocol for Difficult NLU Data Collection Tasks?", "track": "main", "status": "Long", "award": false, "abstract": "Crowdsourcing is widely used to create data for common natural language understanding tasks. Despite the importance of these datasets for measuring and refining model understanding of language, there has been little focus on the crowdsourcing methods used for collecting the datasets. In this paper, we compare the efficacy of interventions that have been proposed in prior work as ways of improving data quality. We use multiple-choice question answering as a testbed and run a randomized trial by assigning crowdworkers to write questions under one of four different data collection protocols. We find that asking workers to write explanations for their examples is an ineffective stand-alone strategy for boosting NLU example difficulty. However, we find that training crowdworkers, and then using an iterative process of collecting data, sending feedback, and qualifying workers based on expert judgments is an effective means of collecting challenging data. But using crowdsourced, instead of expert judgments, to qualify workers and send feedback does not prove to be effective. We observe that the data from the iterative protocol with expert assessments is more challenging by several measures. Notably, the human\u2013model gap on the unanimous agreement portion of this data is, on average, twice as large as the gap for the baseline protocol data.", "author": "Nikita Nangia; Saku Sugawara; Harsh Trivedi; Alex Warstadt; Clara Vania; Samuel R. Bowman", "authorids": "/n/nikita-nangia/; /s/saku-sugawara/; /h/harsh-trivedi/; /a/alex-warstadt/; /c/clara-vania/; /s/samuel-bowman/", "bibtex": "@inproceedings{nangia-etal-2021-ingredients,\n title = \"What Ingredients Make for an Effective Crowdsourcing Protocol for Difficult {NLU} Data Collection Tasks?\",\n author = \"Nangia, Nikita and\n Sugawara, Saku and\n Trivedi, Harsh and\n Warstadt, Alex and\n Vania, Clara and\n Bowman, Samuel R.\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.98/\",\n doi = \"10.18653/v1/2021.acl-long.98\",\n pages = \"1221--1235\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.98.pdf", "site": "https://aclanthology.org/2021.acl-long.98/", "pdf_size": 548656, "gs_citation": 30, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=766658150479408298&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 8, "aff": "New York University; National Institute of Informatics; Stony Brook University; New York University; Amazon; New York University", "aff_domain": "nyu.edu;nii.ac.jp; ;nyu.edu;amazon.com;nyu.edu", "email": "nyu.edu;nii.ac.jp; ;nyu.edu;amazon.com;nyu.edu", "github": "", "project": "", "author_num": 6, "aff_unique_index": "0;1;2;0;3;0", "aff_unique_norm": "New York University;National Institute of Informatics;Stony Brook University;Amazon.com, Inc.", "aff_unique_dep": ";;;", "aff_unique_url": "https://www.nyu.edu;https://www.nii.ac.jp/;https://www.stonybrook.edu;https://www.amazon.com", "aff_unique_abbr": "NYU;NII;SBU;Amazon", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0;1;0;0;0;0", "aff_country_unique": "United States;Japan" }, { "id": "2021.acl-short.101", "title": "What Motivates You? Benchmarking Automatic Detection of Basic Needs from Short Posts", "track": "main", "status": "Short", "award": false, "abstract": "According to the self-determination theory, the levels of satisfaction of three basic needs (competence, autonomy and relatedness) have implications on people\u2019s everyday life and career. We benchmark the novel task of automatically detecting those needs on short posts in English, by modelling it as a ternary classification task, and as three binary classification tasks. A detailed manual analysis shows that the latter has advantages in the real-world scenario, and that our best models achieve similar performances as a trained human annotator.", "author": "Sanja Stajner; Seren Yenikent; Bilal Ghanem; Marc Franco-Salvador", "authorids": "/s/sanja-stajner/; /s/seren-yenikent/; /b/bilal-ghanem/; /m/marc-franco-salvador/", "bibtex": "@inproceedings{stajner-etal-2021-motivates,\n title = \"What Motivates You? Benchmarking Automatic Detection of Basic Needs from Short Posts\",\n author = \"Stajner, Sanja and\n Yenikent, Seren and\n Ghanem, Bilal and\n Franco-Salvador, Marc\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-short.101/\",\n doi = \"10.18653/v1/2021.acl-short.101\",\n pages = \"803--810\"\n}", "pdf": "https://aclanthology.org/2021.acl-short.101.pdf", "site": "https://aclanthology.org/2021.acl-short.101/", "pdf_size": 224845, "gs_citation": 2, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=4205359037583416786&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 3, "aff": "Symanto Research, Nuremberg, Germany; Symanto Research, Nuremberg, Germany; Symanto Research, Valencia, Spain; Symanto Research, Valencia, Spain", "aff_domain": "symanto.com;symanto.com;gmail.com;symanto.com", "email": "symanto.com;symanto.com;gmail.com;symanto.com", "github": "", "project": "", "author_num": 4, "aff_unique_index": "0;0;0;0", "aff_unique_norm": "Symanto Research", "aff_unique_dep": "", "aff_unique_url": "", "aff_unique_abbr": "", "aff_campus_unique_index": "0;0;1;1", "aff_campus_unique": "Nuremberg;Valencia", "aff_country_unique_index": "0;0;1;1", "aff_country_unique": "Germany;Spain" }, { "id": "2021.acl-long.458", "title": "What is Your Article Based On? Inferring Fine-grained Provenance", "track": "main", "status": "Long", "award": false, "abstract": "When evaluating an article and the claims it makes, a critical reader must be able to assess where the information presented comes from, and whether the various claims are mutually consistent and support the conclusion. This motivates the study of claim provenance, which seeks to trace and explain the origins of claims. In this paper, we introduce new techniques to model and reason about the provenance of multiple interacting claims, including how to capture fine-grained information about the context. Our solution hinges on first identifying the sentences that potentially contain important external information. We then develop a query generator with our novel rank-aware cross attention mechanism, which aims at generating metadata for the source article, based on the context and the signals collected from a search engine. This establishes relevant search queries, and it allows us to obtain source article candidates for each identified sentence and propose an ILP based algorithm to infer the best sources. We experiment with a newly created evaluation dataset, Politi-Prov, based on fact-checking articles from www.politifact.com; our experimental results show that our solution leads to a significant improvement over baselines.", "author": "Yi Zhang; Zachary Ives; Dan Roth", "authorids": "/y/yi-zhang/; /z/zachary-ives/; /d/dan-roth/", "bibtex": "@inproceedings{zhang-etal-2021-article,\n title = \"What is Your Article Based On? Inferring Fine-grained Provenance\",\n author = \"Zhang, Yi and\n Ives, Zachary and\n Roth, Dan\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.458/\",\n doi = \"10.18653/v1/2021.acl-long.458\",\n pages = \"5894--5903\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.458.pdf", "site": "https://aclanthology.org/2021.acl-long.458/", "pdf_size": 741623, "gs_citation": 4, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=445097628040465067&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 3, "aff": "Department of Computer and Information Science, University of Pennsylvania, Philadelphia, PA, USA; Department of Computer and Information Science, University of Pennsylvania, Philadelphia, PA, USA; Department of Computer and Information Science, University of Pennsylvania, Philadelphia, PA, USA", "aff_domain": "cis.upenn.edu;cis.upenn.edu;cis.upenn.edu", "email": "cis.upenn.edu;cis.upenn.edu;cis.upenn.edu", "github": "", "project": "http://cogcomp.org/page/publication_view/944", "author_num": 3, "aff_unique_index": "0;0;0", "aff_unique_norm": "University of Pennsylvania", "aff_unique_dep": "Department of Computer and Information Science", "aff_unique_url": "https://www.upenn.edu", "aff_unique_abbr": "UPenn", "aff_campus_unique_index": "0;0;0", "aff_campus_unique": "Philadelphia", "aff_country_unique_index": "0;0;0", "aff_country_unique": "United States" }, { "id": "2021.acl-short.24", "title": "What\u2019s in the Box? An Analysis of Undesirable Content in the Common Crawl Corpus", "track": "main", "status": "Short", "award": false, "abstract": "Whereas much of the success of the current generation of neural language models has been driven by increasingly large training corpora, relatively little research has been dedicated to analyzing these massive sources of textual data. In this exploratory analysis, we delve deeper into the Common Crawl, a colossal web corpus that is extensively used for training language models. We find that it contains a significant amount of undesirable content, including hate speech and sexually explicit content, even after filtering procedures. We discuss the potential impacts of this content on language models and conclude with future research directions and a more mindful approach to corpus collection and analysis.", "author": "Alexandra Luccioni; Joseph Viviano", "authorids": "/a/alexandra-luccioni/; /j/joseph-viviano/", "bibtex": "@inproceedings{luccioni-viviano-2021-whats,\n title = \"What`s in the Box? An Analysis of Undesirable Content in the {C}ommon {C}rawl Corpus\",\n author = \"Luccioni, Alexandra and\n Viviano, Joseph\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-short.24/\",\n doi = \"10.18653/v1/2021.acl-short.24\",\n pages = \"182--189\"\n}", "pdf": "https://aclanthology.org/2021.acl-short.24.pdf", "site": "https://aclanthology.org/2021.acl-short.24/", "pdf_size": 268156, "gs_citation": -1, "gs_cited_by_link": "", "gs_version_total": 0, "aff": "Universit \u00b4e de Montr \u00b4eal & Mila Qu \u00b4ebec AI Institute; Mila Qu \u00b4ebec AI Institute", "aff_domain": "mila.quebec;viviano.ca", "email": "mila.quebec;viviano.ca", "github": "", "project": "", "author_num": 2, "aff_unique_index": "0;1", "aff_unique_norm": "Universit\u00e9 de Montr\u00e9al;Mila Qu\u00e9bec AI Institute", "aff_unique_dep": ";AI Institute", "aff_unique_url": "https://www.umontreal.ca;https://mila.quebec", "aff_unique_abbr": "UdeM;Mila", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0;0", "aff_country_unique": "Canada" }, { "id": "2021.acl-long.90", "title": "When Do You Need Billions of Words of Pretraining Data?", "track": "main", "status": "Long", "award": false, "abstract": "NLP is currently dominated by language models like RoBERTa which are pretrained on billions of words. But what exact knowledge or skills do Transformer LMs learn from large-scale pretraining that they cannot learn from less data? To explore this question, we adopt five styles of evaluation: classifier probing, information-theoretic probing, unsupervised relative acceptability judgments, unsupervised language model knowledge probing, and fine-tuning on NLU tasks. We then draw learning curves that track the growth of these different measures of model ability with respect to pretraining data volume using the MiniBERTas, a group of RoBERTa models pretrained on 1M, 10M, 100M and 1B words. We find that these LMs require only about 10M to 100M words to learn to reliably encode most syntactic and semantic features we test. They need a much larger quantity of data in order to acquire enough commonsense knowledge and other skills required to master typical downstream NLU tasks. The results suggest that, while the ability to encode linguistic features is almost certainly necessary for language understanding, it is likely that other, unidentified, forms of knowledge are the major drivers of recent improvements in language understanding among large pretrained models.", "author": "Yian Zhang; Alex Warstadt; Xiaocheng Li; Samuel R. Bowman", "authorids": "/y/yian-zhang/; /a/alex-warstadt/; /x/xiaocheng-li/; /s/samuel-bowman/", "bibtex": "@inproceedings{zhang-etal-2021-need,\n title = \"When Do You Need Billions of Words of Pretraining Data?\",\n author = \"Zhang, Yian and\n Warstadt, Alex and\n Li, Xiaocheng and\n Bowman, Samuel R.\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.90/\",\n doi = \"10.18653/v1/2021.acl-long.90\",\n pages = \"1112--1125\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.90.pdf", "site": "https://aclanthology.org/2021.acl-long.90/", "pdf_size": 680847, "gs_citation": 156, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=15148468174906519320&as_sdt=5,33&sciodt=0,33&hl=en", "gs_version_total": 8, "aff": "Dept. of Computer Science; Dept. of Linguistics; Center for Data Science; Dept. of Computer Science + Dept. of Linguistics + Center for Data Science", "aff_domain": "nyu.edu;nyu.edu;nyu.edu;nyu.edu", "email": "nyu.edu;nyu.edu;nyu.edu;nyu.edu", "github": "", "project": "", "author_num": 4, "aff_unique_index": "0;0;1;0+0+1", "aff_unique_norm": "University Affiliation Not Specified;Center for Data Science", "aff_unique_dep": "Department of Computer Science;Data Science", "aff_unique_url": ";", "aff_unique_abbr": ";", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "", "aff_country_unique": "" }, { "id": "2021.acl-short.69", "title": "When is Char Better Than Subword: A Systematic Study of Segmentation Algorithms for Neural Machine Translation", "track": "main", "status": "Short", "award": false, "abstract": "Subword segmentation algorithms have been a de facto choice when building neural machine translation systems. However, most of them need to learn a segmentation model based on some heuristics, which may produce sub-optimal segmentation. This can be problematic in some scenarios when the target language has rich morphological changes or there is not enough data for learning compact composition rules. Translating at fully character level has the potential to alleviate the issue, but empirical performances of character-based models has not been fully explored. In this paper, we present an in-depth comparison between character-based and subword-based NMT systems under three settings: translating to typologically diverse languages, training with low resource, and adapting to unseen domains. Experiment results show strong competitiveness of character-based models. Further analyses show that compared to subword-based models, character-based models are better at handling morphological phenomena, generating rare and unknown words, and more suitable for transferring to unseen domains.", "author": "Jiahuan Li; Yutong Shen; Shujian Huang; Xinyu Dai; Jiajun Chen", "authorids": "/j/jiahuan-li/; /y/yutong-shen/; /s/shujian-huang/; /x/xinyu-dai/; /j/jiajun-chen/", "bibtex": "@inproceedings{li-etal-2021-char,\n title = \"When is Char Better Than Subword: A Systematic Study of Segmentation Algorithms for Neural Machine Translation\",\n author = \"Li, Jiahuan and\n Shen, Yutong and\n Huang, Shujian and\n Dai, Xinyu and\n Chen, Jiajun\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-short.69/\",\n doi = \"10.18653/v1/2021.acl-short.69\",\n pages = \"543--549\"\n}", "pdf": "https://aclanthology.org/2021.acl-short.69.pdf", "site": "https://aclanthology.org/2021.acl-short.69/", "pdf_size": 386636, "gs_citation": 11, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=5715857830310724013&as_sdt=5,33&sciodt=0,33&hl=en", "gs_version_total": 4, "aff": "National Key Laboratory for Novel Software Technology, Nanjing University, China; National Key Laboratory for Novel Software Technology, Nanjing University, China; National Key Laboratory for Novel Software Technology, Nanjing University, China; National Key Laboratory for Novel Software Technology, Nanjing University, China; National Key Laboratory for Novel Software Technology, Nanjing University, China", "aff_domain": "smail.nju.edu.cn;smail.nju.edu.cn;nju.edu.cn;nju.edu.cn;nju.edu.cn", "email": "smail.nju.edu.cn;smail.nju.edu.cn;nju.edu.cn;nju.edu.cn;nju.edu.cn", "github": "", "project": "", "author_num": 5, "aff_unique_index": "0;0;0;0;0", "aff_unique_norm": "Nanjing University", "aff_unique_dep": "National Key Laboratory for Novel Software Technology", "aff_unique_url": "http://www.nju.edu.cn", "aff_unique_abbr": "Nanjing U", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0;0;0;0;0", "aff_country_unique": "China" }, { "id": "2021.acl-long.304", "title": "Which Linguist Invented the Lightbulb? Presupposition Verification for Question-Answering", "track": "main", "status": "Long", "award": false, "abstract": "Many Question-Answering (QA) datasets contain unanswerable questions, but their treatment in QA systems remains primitive. Our analysis of the Natural Questions (Kwiatkowski et al. 2019) dataset reveals that a substantial portion of unanswerable questions (~21%) can be explained based on the presence of unverifiable presuppositions. Through a user preference study, we demonstrate that the oracle behavior of our proposed system\u2014which provides responses based on presupposition failure\u2014is preferred over the oracle behavior of existing QA systems. Then, we present a novel framework for implementing such a system in three steps: presupposition generation, presupposition verification, and explanation generation, reporting progress on each. Finally, we show that a simple modification of adding presuppositions and their verifiability to the input of a competitive end-to-end QA system yields modest gains in QA performance and unanswerability detection, demonstrating the promise of our approach.", "author": "Najoung Kim; Ellie Pavlick; Burcu Karagol Ayan; Deepak Ramachandran", "authorids": "/n/najoung-kim/; /e/ellie-pavlick/; /b/burcu-karagol-ayan/; /d/deepak-ramachandran/", "bibtex": "@inproceedings{kim-etal-2021-linguist,\n title = \"Which Linguist Invented the Lightbulb? Presupposition Verification for Question-Answering\",\n author = \"Kim, Najoung and\n Pavlick, Ellie and\n Karagol Ayan, Burcu and\n Ramachandran, Deepak\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.304/\",\n doi = \"10.18653/v1/2021.acl-long.304\",\n pages = \"3932--3945\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.304.pdf", "site": "https://aclanthology.org/2021.acl-long.304/", "pdf_size": 706507, "gs_citation": 39, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=11928344602004760661&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 5, "aff": "Johns Hopkins University; Brown University; Google Research; Google Research", "aff_domain": "jhu.edu;google.com;google.com;google.com", "email": "jhu.edu;google.com;google.com;google.com", "github": "", "project": "", "author_num": 4, "aff_unique_index": "0;1;2;2", "aff_unique_norm": "Johns Hopkins University;Brown University;Google", "aff_unique_dep": ";;Google Research", "aff_unique_url": "https://www.jhu.edu;https://www.brown.edu;https://research.google", "aff_unique_abbr": "JHU;Brown;Google Research", "aff_campus_unique_index": "1;1", "aff_campus_unique": ";Mountain View", "aff_country_unique_index": "0;0;0;0", "aff_country_unique": "United States" }, { "id": "2021.acl-short.28", "title": "WikiSum: Coherent Summarization Dataset for Efficient Human-Evaluation", "track": "main", "status": "Short", "award": false, "abstract": "Recent works made significant advances on summarization tasks, facilitated by summarization datasets. Several existing datasets have the form of coherent-paragraph summaries. However, these datasets were curated from academic documents that were written for experts, thus making the essential step of assessing the summarization output through human-evaluation very demanding. To overcome these limitations, we present a dataset based on article summaries appearing on the WikiHow website, composed of how-to articles and coherent-paragraph summaries written in plain language. We compare our dataset attributes to existing ones, including readability and world-knowledge, showing our dataset makes human evaluation significantly easier and thus, more effective. A human evaluation conducted on PubMed and the proposed dataset reinforces our findings.", "author": "Nachshon Cohen; Oren Kalinsky; Yftah Ziser; Alessandro Moschitti", "authorids": "/n/nachshon-cohen/; /o/oren-kalinsky/; /y/yftah-ziser/; /a/alessandro-moschitti/", "bibtex": "@inproceedings{cohen-etal-2021-wikisum,\n title = \"{W}iki{S}um: Coherent Summarization Dataset for Efficient Human-Evaluation\",\n author = \"Cohen, Nachshon and\n Kalinsky, Oren and\n Ziser, Yftah and\n Moschitti, Alessandro\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-short.28/\",\n doi = \"10.18653/v1/2021.acl-short.28\",\n pages = \"212--219\"\n}", "pdf": "https://aclanthology.org/2021.acl-short.28.pdf", "site": "https://aclanthology.org/2021.acl-short.28/", "pdf_size": 333067, "gs_citation": 22, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=6104199818209033906&as_sdt=5,33&sciodt=0,33&hl=en", "gs_version_total": 3, "aff": "Amazon; Amazon; Facebook\u2020; Amazon", "aff_domain": "gmail.com;amazon.com;fb.com;amazon.com", "email": "gmail.com;amazon.com;fb.com;amazon.com", "github": "", "project": "https://registry.opendata.aws/wikisum", "author_num": 4, "aff_unique_index": "0;0;1;0", "aff_unique_norm": "Amazon.com, Inc.;Facebook", "aff_unique_dep": ";", "aff_unique_url": "https://www.amazon.com;https://www.facebook.com", "aff_unique_abbr": "Amazon;FB", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0;0;0;0", "aff_country_unique": "United States" }, { "id": "2021.acl-long.406", "title": "Word Sense Disambiguation: Towards Interactive Context Exploitation from Both Word and Sense Perspectives", "track": "main", "status": "Long", "award": false, "abstract": "Lately proposed Word Sense Disambiguation (WSD) systems have approached the estimated upper bound of the task on standard evaluation benchmarks. However, these systems typically implement the disambiguation of words in a document almost independently, underutilizing sense and word dependency in context. In this paper, we convert the nearly isolated decisions into interrelated ones by exposing senses in context when learning sense embeddings in a similarity-based Sense Aware Context Exploitation (SACE) architecture. Meanwhile, we enhance the context embedding learning with selected sentences from the same document, rather than utilizing only the sentence where each ambiguous word appears. Experiments on both English and multilingual WSD datasets have shown the effectiveness of our approach, surpassing previous state-of-the-art by large margins (3.7% and 1.2% respectively), especially on few-shot (14.3%) and zero-shot (35.9%) scenarios.", "author": "Ming Wang; Yinglin Wang", "authorids": "/m/ming-wang/; /y/yinglin-wang/", "bibtex": "@inproceedings{wang-wang-2021-word,\n title = \"Word Sense Disambiguation: Towards Interactive Context Exploitation from Both Word and Sense Perspectives\",\n author = \"Wang, Ming and\n Wang, Yinglin\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.406/\",\n doi = \"10.18653/v1/2021.acl-long.406\",\n pages = \"5218--5229\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.406.pdf", "site": "https://aclanthology.org/2021.acl-long.406/", "pdf_size": 615147, "gs_citation": 17, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=7805522154539629063&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 2, "aff": "School of Information Management and Engineering Shanghai University of Finance and Economics, Shanghai, China; School of Information Management and Engineering Shanghai University of Finance and Economics, Shanghai, China", "aff_domain": "163.sufe.edu.cn;shufe.edu.cn", "email": "163.sufe.edu.cn;shufe.edu.cn", "github": "", "project": "", "author_num": 2, "aff_unique_index": "0;0", "aff_unique_norm": "Shanghai University of Finance and Economics", "aff_unique_dep": "School of Information Management and Engineering", "aff_unique_url": "http://www.sufe.edu.cn", "aff_unique_abbr": "SUFE", "aff_campus_unique_index": "0;0", "aff_campus_unique": "Shanghai", "aff_country_unique_index": "0;0", "aff_country_unique": "China" }, { "id": "2021.acl-long.387", "title": "Writing by Memorizing: Hierarchical Retrieval-based Medical Report Generation", "track": "main", "status": "Long", "award": false, "abstract": "Medical report generation is one of the most challenging tasks in medical image analysis. Although existing approaches have achieved promising results, they either require a predefined template database in order to retrieve sentences or ignore the hierarchical nature of medical report generation. To address these issues, we propose MedWriter that incorporates a novel hierarchical retrieval mechanism to automatically extract both report and sentence-level templates for clinically accurate report generation. MedWriter first employs the Visual-Language Retrieval (VLR) module to retrieve the most relevant reports for the given images. To guarantee the logical coherence between generated sentences, the Language-Language Retrieval (LLR) module is introduced to retrieve relevant sentences based on the previous generated description. At last, a language decoder fuses image features and features from retrieved reports and sentences to generate meaningful medical reports. We verified the effectiveness of our model by automatic evaluation and human evaluation on two datasets, i.e., Open-I and MIMIC-CXR.", "author": "Xingyi Yang; Muchao Ye; Quanzeng You; Fenglong Ma", "authorids": "/x/xingyi-yang/; /m/muchao-ye/; /q/quanzeng-you/; /f/fenglong-ma/", "bibtex": "@inproceedings{yang-etal-2021-writing,\n title = \"Writing by Memorizing: Hierarchical Retrieval-based Medical Report Generation\",\n author = \"Yang, Xingyi and\n Ye, Muchao and\n You, Quanzeng and\n Ma, Fenglong\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.387/\",\n doi = \"10.18653/v1/2021.acl-long.387\",\n pages = \"5000--5009\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.387.pdf", "site": "https://aclanthology.org/2021.acl-long.387/", "pdf_size": 913176, "gs_citation": 42, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=3408433460132315425&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 9, "aff": "UC San Diego; Penn State University; Microsoft Azure Computer Vision; Penn State University", "aff_domain": "eng.ucsd.edu;psu.edu;microsoft.com;psu.edu", "email": "eng.ucsd.edu;psu.edu;microsoft.com;psu.edu", "github": "", "project": "", "author_num": 4, "aff_unique_index": "0;1;2;1", "aff_unique_norm": "University of California, San Diego;Penn State University;Microsoft", "aff_unique_dep": ";;Azure Computer Vision", "aff_unique_url": "https://www.ucsd.edu;https://www.psu.edu;https://azure.microsoft.com/en-us/services/computer-vision/", "aff_unique_abbr": "UCSD;PSU;Microsoft Azure CV", "aff_campus_unique_index": "0", "aff_campus_unique": "San Diego;", "aff_country_unique_index": "0;0;0;0", "aff_country_unique": "United States" }, { "id": "2021.acl-short.86", "title": "X-Fact: A New Benchmark Dataset for Multilingual Fact Checking", "track": "main", "status": "Short", "award": false, "abstract": "In this work, we introduce : the largest publicly available multilingual dataset for factual verification of naturally existing real-world claims. The dataset contains short statements in 25 languages and is labeled for veracity by expert fact-checkers. The dataset includes a multilingual evaluation benchmark that measures both out-of-domain generalization, and zero-shot capabilities of the multilingual models. Using state-of-the-art multilingual transformer-based models, we develop several automated fact-checking models that, along with textual claims, make use of additional metadata and evidence from news stories retrieved using a search engine. Empirically, our best model attains an F-score of around 40%, suggesting that our dataset is a challenging benchmark for the evaluation of multilingual fact-checking models.", "author": "Ashim Gupta; Vivek Srikumar", "authorids": "/a/ashim-gupta/; /v/vivek-srikumar/", "bibtex": "@inproceedings{gupta-srikumar-2021-x,\n title = \"{X}-Fact: A New Benchmark Dataset for Multilingual Fact Checking\",\n author = \"Gupta, Ashim and\n Srikumar, Vivek\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-short.86/\",\n doi = \"10.18653/v1/2021.acl-short.86\",\n pages = \"675--682\"\n}", "pdf": "https://aclanthology.org/2021.acl-short.86.pdf", "site": "https://aclanthology.org/2021.acl-short.86/", "pdf_size": 252488, "gs_citation": 104, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=3563945074303745408&as_sdt=5,48&sciodt=0,48&hl=en", "gs_version_total": 10, "aff": "School of Computing, University of Utah; School of Computing, University of Utah", "aff_domain": "cs.utah.edu;cs.utah.edu", "email": "cs.utah.edu;cs.utah.edu", "github": "", "project": "", "author_num": 2, "aff_unique_index": "0;0", "aff_unique_norm": "University of Utah", "aff_unique_dep": "School of Computing", "aff_unique_url": "https://www.utah.edu", "aff_unique_abbr": "U of U", "aff_campus_unique_index": "0;0", "aff_campus_unique": "Utah", "aff_country_unique_index": "0;0", "aff_country_unique": "United States" }, { "id": "2021.acl-long.73", "title": "XLPT-AMR: Cross-Lingual Pre-Training via Multi-Task Learning for Zero-Shot AMR Parsing and Text Generation", "track": "main", "status": "Long", "award": false, "abstract": "Due to the scarcity of annotated data, Abstract Meaning Representation (AMR) research is relatively limited and challenging for languages other than English. Upon the availability of English AMR dataset and English-to- X parallel datasets, in this paper we propose a novel cross-lingual pre-training approach via multi-task learning (MTL) for both zeroshot AMR parsing and AMR-to-text generation. Specifically, we consider three types of relevant tasks, including AMR parsing, AMR-to-text generation, and machine translation. We hope that knowledge gained while learning for English AMR parsing and text generation can be transferred to the counterparts of other languages. With properly pretrained models, we explore four different finetuning methods, i.e., vanilla fine-tuning with a single task, one-for-all MTL fine-tuning, targeted MTL fine-tuning, and teacher-studentbased MTL fine-tuning. Experimental results on AMR parsing and text generation of multiple non-English languages demonstrate that our approach significantly outperforms a strong baseline of pre-training approach, and greatly advances the state of the art. In detail, on LDC2020T07 we have achieved 70.45%, 71.76%, and 70.80% in Smatch F1 for AMR parsing of German, Spanish, and Italian, respectively, while for AMR-to-text generation of the languages, we have obtained 25.69, 31.36, and 28.42 in BLEU respectively. We make our code available on github https://github.com/xdqkid/XLPT-AMR.", "author": "Dongqin Xu; Junhui Li; Muhua Zhu; Min Zhang; Guodong Zhou", "authorids": "/d/dongqin-xu/; /j/junhui-li/; /m/muhua-zhu/; /m/min-zhang/; /g/guodong-zhou/", "bibtex": "@inproceedings{xu-etal-2021-xlpt,\n title = \"{XLPT}-{AMR}: Cross-Lingual Pre-Training via Multi-Task Learning for Zero-Shot {AMR} Parsing and Text Generation\",\n author = \"Xu, Dongqin and\n Li, Junhui and\n Zhu, Muhua and\n Zhang, Min and\n Zhou, Guodong\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.73/\",\n doi = \"10.18653/v1/2021.acl-long.73\",\n pages = \"896--907\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.73.pdf", "site": "https://aclanthology.org/2021.acl-long.73/", "pdf_size": 514227, "gs_citation": 21, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=6902473655015924192&as_sdt=5,33&sciodt=0,33&hl=en", "gs_version_total": 3, "aff": "School of Computer Science and Technology, Soochow University, Suzhou, China; School of Computer Science and Technology, Soochow University, Suzhou, China; Tencent News, Beijing, China; School of Computer Science and Technology, Soochow University, Suzhou, China; School of Computer Science and Technology, Soochow University, Suzhou, China", "aff_domain": "live.com;suda.edu.cn;gmail.com;suda.edu.cn;suda.edu.cn", "email": "live.com;suda.edu.cn;gmail.com;suda.edu.cn;suda.edu.cn", "github": "https://github.com/xdqkid/XLPT-AMR", "project": "", "author_num": 5, "aff_unique_index": "0;0;1;0;0", "aff_unique_norm": "Soochow University;Tencent", "aff_unique_dep": "School of Computer Science and Technology;Tencent News", "aff_unique_url": "http://www.soochow.edu.cn;https://news.qq.com", "aff_unique_abbr": ";Tencent News", "aff_campus_unique_index": "0;0;1;0;0", "aff_campus_unique": "Suzhou;Beijing", "aff_country_unique_index": "0;0;0;0;0", "aff_country_unique": "China" }, { "id": "2021.acl-short.42", "title": "Zero-shot Event Extraction via Transfer Learning: Challenges and Insights", "track": "main", "status": "Short", "award": false, "abstract": "Event extraction has long been a challenging task, addressed mostly with supervised methods that require expensive annotation and are not extensible to new event ontologies. In this work, we explore the possibility of zero-shot event extraction by formulating it as a set of Textual Entailment (TE) and/or Question Answering (QA) queries (e.g. \u201cA city was attacked\u201d entails \u201cThere is an attack\u201d), exploiting pretrained TE/QA models for direct transfer. On ACE-2005 and ERE, our system achieves acceptable results, yet there is still a large gap from supervised approaches, showing that current QA and TE technologies fail in transferring to a different domain. To investigate the reasons behind the gap, we analyze the remaining key challenges, their respective impact, and possible improvement directions.", "author": "Qing Lyu; Hongming Zhang; Elior Sulem; Dan Roth", "authorids": "/q/qing-lyu/; /h/hongming-zhang/; /e/elior-sulem/; /d/dan-roth/", "bibtex": "@inproceedings{lyu-etal-2021-zero,\n title = \"Zero-shot Event Extraction via Transfer Learning: {C}hallenges and Insights\",\n author = \"Lyu, Qing and\n Zhang, Hongming and\n Sulem, Elior and\n Roth, Dan\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-short.42/\",\n doi = \"10.18653/v1/2021.acl-short.42\",\n pages = \"322--332\"\n}", "pdf": "https://aclanthology.org/2021.acl-short.42.pdf", "site": "https://aclanthology.org/2021.acl-short.42/", "pdf_size": 1456290, "gs_citation": 110, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=10383699361481514662&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 5, "aff": "Department of Computer and Information Science, UPenn; Department of Computer Science and Engineering, HKUST; Department of Computer and Information Science, UPenn; Department of Computer and Information Science, UPenn", "aff_domain": "seas.upenn.edu;cse.ust.hk;seas.upenn.edu;seas.upenn.edu", "email": "seas.upenn.edu;cse.ust.hk;seas.upenn.edu;seas.upenn.edu", "github": "", "project": "http://cogcomp.org/page/publication_view/943", "author_num": 4, "aff_unique_index": "0;1;0;0", "aff_unique_norm": "University of Pennsylvania;Hong Kong University of Science and Technology", "aff_unique_dep": "Department of Computer and Information Science;Department of Computer Science and Engineering", "aff_unique_url": "https://www.upenn.edu;https://www.hkust.edu.hk", "aff_unique_abbr": "UPenn;HKUST", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0;1;0;0", "aff_country_unique": "United States;China" }, { "id": "2021.acl-short.61", "title": "Zero-shot Fact Verification by Claim Generation", "track": "main", "status": "Short", "award": false, "abstract": "Neural models for automated fact verification have achieved promising results thanks to the availability of large, human-annotated datasets. However, for each new domain that requires fact verification, creating a dataset by manually writing claims and linking them to their supporting evidence is expensive. We develop QACG, a framework for training a robust fact verification model by using automatically generated claims that can be supported, refuted, or unverifiable from evidence from Wikipedia. QACG generates question-answer pairs from the evidence and then converts them into different types of claims. Experiments on the FEVER dataset show that our QACG framework significantly reduces the demand for human-annotated training data. In a zero-shot scenario, QACG improves a RoBERTa model\u2019s F1 from 50% to 77%, equivalent in performance to 2K+ manually-curated examples. Our QACG code is publicly available.", "author": "Liangming Pan; Wenhu Chen; Wenhan Xiong; Min-Yen Kan; William Yang Wang", "authorids": "/l/liangming-pan/; /w/wenhu-chen/; /w/wenhan-xiong/; /m/min-yen-kan/; /w/william-yang-wang/", "bibtex": "@inproceedings{pan-etal-2021-zero,\n title = \"Zero-shot Fact Verification by Claim Generation\",\n author = \"Pan, Liangming and\n Chen, Wenhu and\n Xiong, Wenhan and\n Kan, Min-Yen and\n Wang, William Yang\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-short.61/\",\n doi = \"10.18653/v1/2021.acl-short.61\",\n pages = \"476--483\"\n}", "pdf": "https://aclanthology.org/2021.acl-short.61.pdf", "site": "https://aclanthology.org/2021.acl-short.61/", "pdf_size": 811373, "gs_citation": 61, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=13353075329012233220&as_sdt=5,33&sciodt=0,33&hl=en", "gs_version_total": 8, "aff": "NUS Graduate School for Integrative Sciences and Engineering + School of Computing, National University of Singapore, Singapore; University of California, Santa Barbara, CA, USA; University of California, Santa Barbara, CA, USA; School of Computing, National University of Singapore, Singapore; University of California, Santa Barbara, CA, USA", "aff_domain": "u.nus.edu;cs.ucsb.edu;cs.ucsb.edu;comp.nus.edu.sg;cs.ucsb.edu", "email": "u.nus.edu;cs.ucsb.edu;cs.ucsb.edu;comp.nus.edu.sg;cs.ucsb.edu", "github": "https://github.com/teacherpeterpan/Zero-shot-Fact-Verification", "project": "", "author_num": 5, "aff_unique_index": "0+0;1;1;0;1", "aff_unique_norm": "National University of Singapore;University of California, Santa Barbara", "aff_unique_dep": "Graduate School for Integrative Sciences and Engineering;", "aff_unique_url": "https://www.nus.edu.sg;https://www.ucsb.edu", "aff_unique_abbr": "NUS;UCSB", "aff_campus_unique_index": ";1;1;1", "aff_campus_unique": ";Santa Barbara", "aff_country_unique_index": "0+0;1;1;0;1", "aff_country_unique": "Singapore;United States" }, { "id": "2021.acl-long.222", "title": "[RETRACTED] Breaking the Corpus Bottleneck for Context-Aware Neural Machine Translation with Cross-Task Pre-training", "track": "main", "status": "Long", "award": false, "abstract": "Context-aware neural machine translation (NMT) remains challenging due to the lack of large-scale document-level parallel corpora. To break the corpus bottleneck, in this paper we aim to improve context-aware NMT by taking the advantage of the availability of both large-scale sentence-level parallel dataset and source-side monolingual documents. To this end, we propose two pre-training tasks. One learns to translate a sentence from source language to target language on the sentence-level parallel dataset while the other learns to translate a document from deliberately noised to original on the monolingual documents. Importantly, the two pre-training tasks are jointly and simultaneously learned via the same model, thereafter fine-tuned on scale-limited parallel documents from both sentence-level and document-level perspectives. Experimental results on four translation tasks show that our approach significantly improves translation performance. One nice property of our approach is that the fine-tuned model can be used to translate both sentences and documents.", "author": "Linqing Chen; Junhui Li; Zhengxian Gong; Boxing Chen; Weihua Luo; Min Zhang; Guodong Zhou", "authorids": "/l/linqing-chen/; /j/junhui-li/; /z/zhengxian-gong/; /b/boxing-chen/; /w/weihua-luo/; /m/min-zhang/; /g/guodong-zhou/", "bibtex": "", "pdf": "https://aclanthology.org/2021.acl-long.222.pdf", "site": "https://aclanthology.org/2021.acl-long.222/", "pdf_size": 1058662, "gs_citation": 8, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=2339507605576388017&as_sdt=5,33&sciodt=0,33&hl=en", "gs_version_total": 0, "aff": "School of Computer Science and Technology, Soochow University, Suzhou, China; School of Computer Science and Technology, Soochow University, Suzhou, China; School of Computer Science and Technology, Soochow University, Suzhou, China; Alibaba DAMO Academy; Alibaba DAMO Academy; School of Computer Science and Technology, Soochow University, Suzhou, China; School of Computer Science and Technology, Soochow University, Suzhou, China", "aff_domain": "gmail.com;suda.edu.cn;suda.edu.cn;alibaba-inc.com;alibaba-inc.com;suda.edu.cn;suda.edu.cn", "email": "gmail.com;suda.edu.cn;suda.edu.cn;alibaba-inc.com;alibaba-inc.com;suda.edu.cn;suda.edu.cn", "github": "", "project": "", "author_num": 7, "aff_unique_index": "0;0;0;1;1;0;0", "aff_unique_norm": "Soochow University;Alibaba Group", "aff_unique_dep": "School of Computer Science and Technology;DAMO Academy", "aff_unique_url": "http://www.soochow.edu.cn;https://www.alibaba-group.com", "aff_unique_abbr": ";Alibaba DAMO", "aff_campus_unique_index": "0;0;0;0;0", "aff_campus_unique": "Suzhou;", "aff_country_unique_index": "0;0;0;0;0;0;0", "aff_country_unique": "China" }, { "id": "2021.acl-long.79", "title": "[RETRACTED] Prosodic segmentation for parsing spoken dialogue", "track": "main", "status": "Long", "award": false, "abstract": "Parsing spoken dialogue poses unique difficulties, including disfluencies and unmarked boundaries between sentence-like units. Previous work has shown that prosody can help with parsing disfluent speech (Tran et al. 2018), but has assumed that the input to the parser is already segmented into sentence-like units (SUs), which isn\u2019t true in existing speech applications. We investigate how prosody affects a parser that receives an entire dialogue turn as input (a turn-based model), instead of gold standard pre-segmented SUs (an SU-based model). In experiments on the English Switchboard corpus, we find that when using transcripts alone, the turn-based model has trouble segmenting SUs, leading to worse parse performance than the SU-based model. However, prosody can effectively replace gold standard SU boundaries: with prosody, the turn-based model performs as well as the SU-based model (91.38 vs. 91.06 F1 score, respectively), despite performing two tasks (SU segmentation and parsing) rather than one (parsing alone). Analysis shows that pitch and intensity features are the most important for this corpus, since they allow the model to correctly distinguish an SU boundary from a speech disfluency \u2013 a distinction that the model otherwise struggles to make.", "author": "Elizabeth Nielsen; Mark Steedman; Sharon Goldwater", "authorids": "/e/elizabeth-nielsen/; /m/mark-steedman/; /s/sharon-goldwater/", "bibtex": "", "pdf": "https://aclanthology.org/2021.acl-long.79.pdf", "site": "https://aclanthology.org/2021.acl-long.79/", "pdf_size": 319532, "gs_citation": 1, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=3281763649479835806&as_sdt=5,33&sciodt=0,33&hl=en", "gs_version_total": 4, "aff": "School of Informatics, University of Edinburgh, UK; School of Informatics, University of Edinburgh, UK; School of Informatics, University of Edinburgh, UK", "aff_domain": "sms.ed.ac.uk;inf.ed.ac.uk;inf.ed.ac.uk", "email": "sms.ed.ac.uk;inf.ed.ac.uk;inf.ed.ac.uk", "github": "", "project": "https://aclanthology.org/2021.acl-long.79", "author_num": 3, "aff_unique_index": "0;0;0", "aff_unique_norm": "University of Edinburgh", "aff_unique_dep": "School of Informatics", "aff_unique_url": "https://www.ed.ac.uk", "aff_unique_abbr": "Edinburgh", "aff_campus_unique_index": "0;0;0", "aff_campus_unique": "Edinburgh", "aff_country_unique_index": "0;0;0", "aff_country_unique": "United Kingdom" }, { "id": "2021.acl-short.38", "title": "eMLM: A New Pre-training Objective for Emotion Related Tasks", "track": "main", "status": "Short", "award": false, "abstract": "BERT has been shown to be extremely effective on a wide variety of natural language processing tasks, including sentiment analysis and emotion detection. However, the proposed pretraining objectives of BERT do not induce any sentiment or emotion-specific biases into the model. In this paper, we present Emotion Masked Language Modelling, a variation of Masked Language Modelling aimed at improving the BERT language representation model for emotion detection and sentiment analysis tasks. Using the same pre-training corpora as the original model, Wikipedia and BookCorpus, our BERT variation manages to improve the downstream performance on 4 tasks from emotion detection and sentiment analysis by an average of 1.2% F-1. Moreover, our approach shows an increased performance in our task-specific robustness tests.", "author": "Tiberiu Sosea; Cornelia Caragea", "authorids": "/t/tiberiu-sosea/; /c/cornelia-caragea/", "bibtex": "@inproceedings{sosea-caragea-2021-emlm,\n title = \"e{MLM}: A New Pre-training Objective for Emotion Related Tasks\",\n author = \"Sosea, Tiberiu and\n Caragea, Cornelia\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-short.38/\",\n doi = \"10.18653/v1/2021.acl-short.38\",\n pages = \"286--293\"\n}", "pdf": "https://aclanthology.org/2021.acl-short.38.pdf", "site": "https://aclanthology.org/2021.acl-short.38/", "pdf_size": 260432, "gs_citation": 13, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=4217529866158218649&as_sdt=5,33&sciodt=0,33&hl=en", "gs_version_total": 2, "aff": "Computer Science, University of Illinois at Chicago; Computer Science, University of Illinois at Chicago", "aff_domain": "uic.edu;uic.edu", "email": "uic.edu;uic.edu", "github": "https://github.com/tsosea2/eMLM", "project": "", "author_num": 2, "aff_unique_index": "0;0", "aff_unique_norm": "University of Illinois at Chicago", "aff_unique_dep": "Computer Science", "aff_unique_url": "https://www.uic.edu", "aff_unique_abbr": "UIC", "aff_campus_unique_index": "0;0", "aff_campus_unique": "Chicago", "aff_country_unique_index": "0;0", "aff_country_unique": "United States" }, { "id": "2021.acl-short.92", "title": "mTVR: Multilingual Moment Retrieval in Videos", "track": "main", "status": "Short", "award": false, "abstract": "We introduce mTVR, a large-scale multilingual video moment retrieval dataset, containing 218K English and Chinese queries from 21.8K TV show video clips. The dataset is collected by extending the popular TVR dataset (in English) with paired Chinese queries and subtitles. Compared to existing moment retrieval datasets, mTVR is multilingual, larger, and comes with diverse annotations. We further propose mXML, a multilingual moment retrieval model that learns and operates on data from both languages, via encoder parameter sharing and language neighborhood constraints. We demonstrate the effectiveness of mXML on the newly collected mTVR dataset, where mXML outperforms strong monolingual baselines while using fewer parameters. In addition, we also provide detailed dataset analyses and model ablations. Data and code are publicly available at https://github.com/jayleicn/mTVRetrieval", "author": "Jie Lei; Tamara Berg; Mohit Bansal", "authorids": "/j/jie-lei/; /t/tamara-berg/; /m/mohit-bansal/", "bibtex": "@inproceedings{lei-etal-2021-mtvr,\n title = \"m{TVR}: Multilingual Moment Retrieval in Videos\",\n author = \"Lei, Jie and\n Berg, Tamara and\n Bansal, Mohit\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-short.92/\",\n doi = \"10.18653/v1/2021.acl-short.92\",\n pages = \"726--734\"\n}", "pdf": "https://aclanthology.org/2021.acl-short.92.pdf", "site": "https://aclanthology.org/2021.acl-short.92/", "pdf_size": 2659817, "gs_citation": 12, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=9542503419632303104&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 5, "aff": "Department of Computer Science, University of North Carolina at Chapel Hill; Department of Computer Science, University of North Carolina at Chapel Hill; Department of Computer Science, University of North Carolina at Chapel Hill", "aff_domain": "cs.unc.edu;cs.unc.edu;cs.unc.edu", "email": "cs.unc.edu;cs.unc.edu;cs.unc.edu", "github": "https://github.com/jayleicn/mTVRetrieval", "project": "", "author_num": 3, "aff_unique_index": "0;0;0", "aff_unique_norm": "University of North Carolina at Chapel Hill", "aff_unique_dep": "Department of Computer Science", "aff_unique_url": "https://www.unc.edu", "aff_unique_abbr": "UNC Chapel Hill", "aff_campus_unique_index": "0;0;0", "aff_campus_unique": "Chapel Hill", "aff_country_unique_index": "0;0;0", "aff_country_unique": "United States" }, { "id": "2021.acl-short.87", "title": "nmT5 - Is parallel data still relevant for pre-training massively multilingual language models?", "track": "main", "status": "Short", "award": false, "abstract": "Recently, mT5 - a massively multilingual version of T5 - leveraged a unified text-to-text format to attain state-of-the-art results on a wide variety of multilingual NLP tasks. In this paper, we investigate the impact of incorporating parallel data into mT5 pre-training. We find that multi-tasking language modeling with objectives such as machine translation during pre-training is a straightforward way to improve performance on downstream multilingual and cross-lingual tasks. However, the gains start to diminish as the model capacity increases, suggesting that parallel data might not be as essential for larger models. At the same time, even at larger model sizes, we find that pre-training with parallel data still provides benefits in the limited labelled data regime", "author": "Mihir Kale; Aditya Siddhant; Rami Al-Rfou; Linting Xue; Noah Constant; Melvin Johnson", "authorids": "/m/mihir-kale/; /a/aditya-siddhant/; /r/rami-al-rfou/; /l/linting-xue/; /n/noah-constant/; /m/melvin-johnson/", "bibtex": "@inproceedings{kale-etal-2021-nmt5,\n title = \"nm{T}5 - Is parallel data still relevant for pre-training massively multilingual language models?\",\n author = \"Kale, Mihir and\n Siddhant, Aditya and\n Al-Rfou, Rami and\n Xue, Linting and\n Constant, Noah and\n Johnson, Melvin\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-short.87/\",\n doi = \"10.18653/v1/2021.acl-short.87\",\n pages = \"683--691\"\n}", "pdf": "https://aclanthology.org/2021.acl-short.87.pdf", "site": "https://aclanthology.org/2021.acl-short.87/", "pdf_size": 290447, "gs_citation": 22, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=140676248869981890&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 4, "aff": "Google Research; Google Research; Google Research; Google Research; Google Research; Google Research", "aff_domain": "google.com;google.com; ; ; ; ", "email": "google.com;google.com; ; ; ; ", "github": "", "project": "", "author_num": 6, "aff_unique_index": "0;0;0;0;0;0", "aff_unique_norm": "Google", "aff_unique_dep": "Google Research", "aff_unique_url": "https://research.google", "aff_unique_abbr": "Google Research", "aff_campus_unique_index": "0;0;0;0;0;0", "aff_campus_unique": "Mountain View", "aff_country_unique_index": "0;0;0;0;0;0", "aff_country_unique": "United States" }, { "id": "2021.acl-long.477", "title": "xMoCo: Cross Momentum Contrastive Learning for Open-Domain Question Answering", "track": "main", "status": "Long", "award": false, "abstract": "Dense passage retrieval has been shown to be an effective approach for information retrieval tasks such as open domain question answering. Under this paradigm, a dual-encoder model is learned to encode questions and passages separately into vector representations, and all the passage vectors are then pre-computed and indexed, which can be efficiently retrieved by vector space search during inference time. In this paper, we propose a new contrastive learning method called Cross Momentum Contrastive learning (xMoCo), for learning a dual-encoder model for question-passage matching. Our method efficiently maintains a large pool of negative samples like the original MoCo, and by jointly optimizing question-to-passage and passage-to-question matching tasks, enables using separate encoders for questions and passages. We evaluate our method on various open-domain question answering dataset, and the experimental results show the effectiveness of the proposed method.", "author": "Nan Yang; Furu Wei; Binxing Jiao; Daxing Jiang; Linjun Yang", "authorids": "/n/nan-yang/; /f/furu-wei/; /b/binxing-jiao/; /d/daxing-jiang/; /l/linjun-yang/", "bibtex": "@inproceedings{yang-etal-2021-xmoco,\n title = \"x{M}o{C}o: Cross Momentum Contrastive Learning for Open-Domain Question Answering\",\n author = \"Yang, Nan and\n Wei, Furu and\n Jiao, Binxing and\n Jiang, Daxing and\n Yang, Linjun\",\n editor = \"Zong, Chengqing and\n Xia, Fei and\n Li, Wenjie and\n Navigli, Roberto\",\n booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = aug,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.acl-long.477/\",\n doi = \"10.18653/v1/2021.acl-long.477\",\n pages = \"6120--6129\"\n}", "pdf": "https://aclanthology.org/2021.acl-long.477.pdf", "site": "https://aclanthology.org/2021.acl-long.477/", "pdf_size": 381687, "gs_citation": 32, "gs_cited_by_link": "https://scholar.google.com/scholar?cites=16994960440208118834&as_sdt=2005&sciodt=0,5&hl=en", "gs_version_total": 2, "aff": "Microsoft Corporation; Microsoft Corporation; Microsoft Corporation; Microsoft Corporation; Microsoft Corporation", "aff_domain": "microsoft.com;microsoft.com;microsoft.com;microsoft.com;microsoft.com", "email": "microsoft.com;microsoft.com;microsoft.com;microsoft.com;microsoft.com", "github": "", "project": "", "author_num": 5, "aff_unique_index": "0;0;0;0;0", "aff_unique_norm": "Microsoft Corporation", "aff_unique_dep": "", "aff_unique_url": "https://www.microsoft.com", "aff_unique_abbr": "Microsoft", "aff_campus_unique_index": "", "aff_campus_unique": "", "aff_country_unique_index": "0;0;0;0;0", "aff_country_unique": "United States" } ]