paper_id stringlengths 15 18 | title stringlengths 22 143 | paper_url stringlengths 41 44 | authors listlengths 1 16 | abstract large_stringlengths 439 1.79k | anthology_id stringlengths 15 18 | doi stringlengths 27 30 | award stringclasses 3
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|---|---|---|---|---|---|---|---|---|---|
2021.acl-long.201 | LayoutLMv2: Multi-modal Pre-training for Visually-rich Document Understanding | https://aclanthology.org/2021.acl-long.201/ | [
"Yang Xu",
"Yiheng Xu",
"Tengchao Lv",
"Lei Cui",
"Furu Wei",
"Guoxin Wang",
"Yijuan Lu",
"Dinei Florencio",
"Cha Zhang",
"Wanxiang Che",
"Min Zhang",
"Lidong Zhou"
] | 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... | 2021.acl-long.201 | 10.18653/v1/2021.acl-long.201 | null | 2012.14740 | title_snapshot |
2021.acl-long.202 | UNIMO: Towards Unified-Modal Understanding and Generation via Cross-Modal Contrastive Learning | https://aclanthology.org/2021.acl-long.202/ | [
"Wei Li",
"Can Gao",
"Guocheng Niu",
"Xinyan Xiao",
"Hao Liu",
"Jiachen Liu",
"Hua Wu",
"Haifeng Wang"
] | 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... | 2021.acl-long.202 | 10.18653/v1/2021.acl-long.202 | null | 2012.15409 | title_snapshot |
2021.acl-long.203 | Missing Modality Imagination Network for Emotion Recognition with Uncertain Missing Modalities | https://aclanthology.org/2021.acl-long.203/ | [
"Jinming Zhao",
"Ruichen Li",
"Qin Jin"
] | 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 ... | 2021.acl-long.203 | 10.18653/v1/2021.acl-long.203 | null | null | null |
2021.acl-long.204 | Stacked Acoustic-and-Textual Encoding: Integrating the Pre-trained Models into Speech Translation Encoders | https://aclanthology.org/2021.acl-long.204/ | [
"Chen Xu",
"Bojie Hu",
"Yanyang Li",
"Yuhao Zhang",
"Shen Huang",
"Qi Ju",
"Tong Xiao",
"Jingbo Zhu"
] | 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 represent... | 2021.acl-long.204 | 10.18653/v1/2021.acl-long.204 | null | 2105.05752 | title_snapshot |
2021.acl-long.205 | N-ary Constituent Tree Parsing with Recursive Semi-Markov Model | https://aclanthology.org/2021.acl-long.205/ | [
"Xin Xin",
"Jinlong Li",
"Zeqi Tan"
] | 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 ins... | 2021.acl-long.205 | 10.18653/v1/2021.acl-long.205 | null | null | null |
2021.acl-long.206 | Automated Concatenation of Embeddings for Structured Prediction | https://aclanthology.org/2021.acl-long.206/ | [
"Xinyu Wang",
"Yong Jiang",
"Nguyen Bach",
"Tao Wang",
"Zhongqiang Huang",
"Fei Huang",
"Kewei Tu"
] | 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 depe... | 2021.acl-long.206 | 10.18653/v1/2021.acl-long.206 | null | 2010.05006 | title_snapshot |
2021.acl-long.207 | Multi-View Cross-Lingual Structured Prediction with Minimum Supervision | https://aclanthology.org/2021.acl-long.207/ | [
"Zechuan Hu",
"Yong Jiang",
"Nguyen Bach",
"Tao Wang",
"Zhongqiang Huang",
"Fei Huang",
"Kewei Tu"
] | 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 lang... | 2021.acl-long.207 | 10.18653/v1/2021.acl-long.207 | null | null | null |
2021.acl-long.208 | The Limitations of Limited Context for Constituency Parsing | https://aclanthology.org/2021.acl-long.208/ | [
"Yuchen Li",
"Andrej Risteski"
] | 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 synta... | 2021.acl-long.208 | 10.18653/v1/2021.acl-long.208 | null | 2106.01580 | title_snapshot |
2021.acl-long.209 | Neural Bi-Lexicalized PCFG Induction | https://aclanthology.org/2021.acl-long.209/ | [
"Songlin Yang",
"Yanpeng Zhao",
"Kewei Tu"
] | 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 withou... | 2021.acl-long.209 | 10.18653/v1/2021.acl-long.209 | null | 2105.15021 | title_snapshot |
2021.acl-long.210 | Ruddit: Norms of Offensiveness for English Reddit Comments | https://aclanthology.org/2021.acl-long.210/ | [
"Rishav Hada",
"Sohi Sudhir",
"Pushkar Mishra",
"Helen Yannakoudakis",
"Saif M. Mohammad",
"Ekaterina Shutova"
] | 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 offen... | 2021.acl-long.210 | 10.18653/v1/2021.acl-long.210 | null | 2106.05664 | title_snapshot |
2021.acl-long.211 | Towards Quantifiable Dialogue Coherence Evaluation | https://aclanthology.org/2021.acl-long.211/ | [
"Zheng Ye",
"Liucun Lu",
"Lishan Huang",
"Liang Lin",
"Xiaodan Liang"
] | 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 co... | 2021.acl-long.211 | 10.18653/v1/2021.acl-long.211 | null | 2106.00507 | title_snapshot |
2021.acl-long.212 | Assessing the Representations of Idiomaticity in Vector Models with a Noun Compound Dataset Labeled at Type and Token Levels | https://aclanthology.org/2021.acl-long.212/ | [
"Marcos Garcia",
"Tiago Kramer Vieira",
"Carolina Scarton",
"Marco Idiart",
"Aline Villavicencio"
] | 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... | 2021.acl-long.212 | 10.18653/v1/2021.acl-long.212 | null | null | null |
2021.acl-long.213 | Factoring Statutory Reasoning as Language Understanding Challenges | https://aclanthology.org/2021.acl-long.213/ | [
"Nils Holzenberger",
"Benjamin Van Durme"
] | 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 th... | 2021.acl-long.213 | 10.18653/v1/2021.acl-long.213 | null | 2105.07903 | title_snapshot |
2021.acl-long.214 | Evaluating Evaluation Measures for Ordinal Classification and Ordinal Quantification | https://aclanthology.org/2021.acl-long.214/ | [
"Tetsuya Sakai"
] | 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... | 2021.acl-long.214 | 10.18653/v1/2021.acl-long.214 | null | null | null |
2021.acl-long.215 | Interpretable and Low-Resource Entity Matching via Decoupling Feature Learning from Decision Making | https://aclanthology.org/2021.acl-long.215/ | [
"Zijun Yao",
"Chengjiang Li",
"Tiansi Dong",
"Xin Lv",
"Jifan Yu",
"Lei Hou",
"Juanzi Li",
"Yichi Zhang",
"Zelin Dai"
] | 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 p... | 2021.acl-long.215 | 10.18653/v1/2021.acl-long.215 | null | 2106.04174 | title_snapshot |
2021.acl-long.216 | Locate and Label: A Two-stage Identifier for Nested Named Entity Recognition | https://aclanthology.org/2021.acl-long.216/ | [
"Yongliang Shen",
"Xinyin Ma",
"Zeqi Tan",
"Shuai Zhang",
"Wen Wang",
"Weiming Lu"
] | 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, th... | 2021.acl-long.216 | 10.18653/v1/2021.acl-long.216 | null | 2105.06804 | title_snapshot |
2021.acl-long.217 | Text2Event: Controllable Sequence-to-Structure Generation for End-to-end Event Extraction | https://aclanthology.org/2021.acl-long.217/ | [
"Yaojie Lu",
"Hongyu Lin",
"Jin Xu",
"Xianpei Han",
"Jialong Tang",
"Annan Li",
"Le Sun",
"Meng Liao",
"Shaoyi Chen"
] | 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 generati... | 2021.acl-long.217 | 10.18653/v1/2021.acl-long.217 | null | 2106.09232 | title_snapshot |
2021.acl-long.218 | A Large-Scale Chinese Multimodal NER Dataset with Speech Clues | https://aclanthology.org/2021.acl-long.218/ | [
"Dianbo Sui",
"Zhengkun Tian",
"Yubo Chen",
"Kang Liu",
"Jun Zhao"
] | 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 ac... | 2021.acl-long.218 | 10.18653/v1/2021.acl-long.218 | null | null | null |
2021.acl-long.219 | A Neural Transition-based Joint Model for Disease Named Entity Recognition and Normalization | https://aclanthology.org/2021.acl-long.219/ | [
"Zongcheng Ji",
"Tian Xia",
"Mei Han",
"Jing Xiao"
] | 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 ha... | 2021.acl-long.219 | 10.18653/v1/2021.acl-long.219 | null | null | null |
2021.acl-long.220 | OntoED: Low-resource Event Detection with Ontology Embedding | https://aclanthology.org/2021.acl-long.220/ | [
"Shumin Deng",
"Ningyu Zhang",
"Luoqiu Li",
"Chen Hui",
"Tou Huaixiao",
"Mosha Chen",
"Fei Huang",
"Huajun Chen"
] | 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 add... | 2021.acl-long.220 | 10.18653/v1/2021.acl-long.220 | null | 2105.10922 | title_snapshot |
2021.acl-long.221 | Self-Training Sampling with Monolingual Data Uncertainty for Neural Machine Translation | https://aclanthology.org/2021.acl-long.221/ | [
"Wenxiang Jiao",
"Xing Wang",
"Zhaopeng Tu",
"Shuming Shi",
"Michael Lyu",
"Irwin King"
] | 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... | 2021.acl-long.221 | 10.18653/v1/2021.acl-long.221 | null | 2106.00941 | title_snapshot |
2021.acl-long.222 | Breaking the Corpus Bottleneck for Context-Aware Neural Machine Translation with Cross-Task Pre-training | https://aclanthology.org/2021.acl-long.222/ | [
"Linqing Chen",
"Junhui Li",
"Zhengxian Gong",
"Boxing Chen",
"Weihua Luo",
"Min Zhang",
"Guodong Zhou"
] | 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-... | 2021.acl-long.222 | 10.18653/v1/2021.acl-long.222 | null | null | null |
2021.acl-long.223 | Guiding Teacher Forcing with Seer Forcing for Neural Machine Translation | https://aclanthology.org/2021.acl-long.223/ | [
"Yang Feng",
"Shuhao Gu",
"Dengji Guo",
"Zhengxin Yang",
"Chenze Shao"
] | 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... | 2021.acl-long.223 | 10.18653/v1/2021.acl-long.223 | null | 2106.06751 | title_snapshot |
2021.acl-long.224 | Cascade versus Direct Speech Translation: Do the Differences Still Make a Difference? | https://aclanthology.org/2021.acl-long.224/ | [
"Luisa Bentivogli",
"Mauro Cettolo",
"Marco Gaido",
"Alina Karakanta",
"Alberto Martinelli",
"Matteo Negri",
"Marco Turchi"
] | 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 be... | 2021.acl-long.224 | 10.18653/v1/2021.acl-long.224 | null | 2106.01045 | title_snapshot |
2021.acl-long.225 | Unsupervised Neural Machine Translation for Low-Resource Domains via Meta-Learning | https://aclanthology.org/2021.acl-long.225/ | [
"Cheonbok Park",
"Yunwon Tae",
"TaeHee Kim",
"Soyoung Yang",
"Mohammad Azam Khan",
"Lucy Park",
"Jaegul Choo"
] | 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 neur... | 2021.acl-long.225 | 10.18653/v1/2021.acl-long.225 | null | 2010.09046 | title_snapshot |
2021.acl-long.226 | Lightweight Cross-Lingual Sentence Representation Learning | https://aclanthology.org/2021.acl-long.226/ | [
"Zhuoyuan Mao",
"Prakhar Gupta",
"Chenhui Chu",
"Martin Jaggi",
"Sadao Kurohashi"
] | 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 limitat... | 2021.acl-long.226 | 10.18653/v1/2021.acl-long.226 | null | 2105.13856 | title_snapshot |
2021.acl-long.227 | ERNIE-Doc: A Retrospective Long-Document Modeling Transformer | https://aclanthology.org/2021.acl-long.227/ | [
"SiYu Ding",
"Junyuan Shang",
"Shuohuan Wang",
"Yu Sun",
"Hao Tian",
"Hua Wu",
"Haifeng Wang"
] | 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 size... | 2021.acl-long.227 | 10.18653/v1/2021.acl-long.227 | null | 2012.15688 | title_snapshot |
2021.acl-long.228 | Marginal Utility Diminishes: Exploring the Minimum Knowledge for BERT Knowledge Distillation | https://aclanthology.org/2021.acl-long.228/ | [
"Yuanxin Liu",
"Fandong Meng",
"Zheng Lin",
"Weiping Wang",
"Jie Zhou"
] | Recently, knowledge distillation (KD) has shown great success in BERT compression. Instead of only learning from the teacher’s soft label as in conventional KD, researchers find that the rich information contained in the hidden layers of BERT is conducive to the student’s performance. To better exploit the hidden knowl... | 2021.acl-long.228 | 10.18653/v1/2021.acl-long.228 | null | 2106.05691 | title_snapshot |
2021.acl-long.229 | Rational LAMOL: A Rationale-based Lifelong Learning Framework | https://aclanthology.org/2021.acl-long.229/ | [
"Kasidis Kanwatchara",
"Thanapapas Horsuwan",
"Piyawat Lertvittayakumjorn",
"Boonserm Kijsirikul",
"Peerapon Vateekul"
] | 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 Ra... | 2021.acl-long.229 | 10.18653/v1/2021.acl-long.229 | null | null | null |
2021.acl-long.230 | EnsLM: Ensemble Language Model for Data Diversity by Semantic Clustering | https://aclanthology.org/2021.acl-long.230/ | [
"Zhibin Duan",
"Hao Zhang",
"Chaojie Wang",
"Zhengjue Wang",
"Bo Chen",
"Mingyuan Zhou"
] | 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 prio... | 2021.acl-long.230 | 10.18653/v1/2021.acl-long.230 | null | null | null |
2021.acl-long.231 | LeeBERT: Learned Early Exit for BERT with cross-level optimization | https://aclanthology.org/2021.acl-long.231/ | [
"Wei Zhu"
] | 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 fe... | 2021.acl-long.231 | 10.18653/v1/2021.acl-long.231 | null | null | null |
2021.acl-long.232 | Unsupervised Extractive Summarization-Based Representations for Accurate and Explainable Collaborative Filtering | https://aclanthology.org/2021.acl-long.232/ | [
"Reinald Adrian Pugoy",
"Hung-Yu Kao"
] | 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 i... | 2021.acl-long.232 | 10.18653/v1/2021.acl-long.232 | null | null | null |
2021.acl-long.233 | PLOME: Pre-training with Misspelled Knowledge for Chinese Spelling Correction | https://aclanthology.org/2021.acl-long.233/ | [
"Shulin Liu",
"Tao Yang",
"Tianchi Yue",
"Feng Zhang",
"Di Wang"
] | 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 ... | 2021.acl-long.233 | 10.18653/v1/2021.acl-long.233 | null | null | null |
2021.acl-long.234 | Competence-based Multimodal Curriculum Learning for Medical Report Generation | https://aclanthology.org/2021.acl-long.234/ | [
"Fenglin Liu",
"Shen Ge",
"Xian Wu"
] | 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 serio... | 2021.acl-long.234 | 10.18653/v1/2021.acl-long.234 | null | 2206.14579 | title_snapshot |
2021.acl-long.235 | Learning Syntactic Dense Embedding with Correlation Graph for Automatic Readability Assessment | https://aclanthology.org/2021.acl-long.235/ | [
"Xinying Qiu",
"Yuan Chen",
"Hanwu Chen",
"Jian-Yun Nie",
"Yuming Shen",
"Dawei Lu"
] | 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 relat... | 2021.acl-long.235 | 10.18653/v1/2021.acl-long.235 | null | 2107.04268 | title_snapshot |
2021.acl-long.236 | Meta-KD: A Meta Knowledge Distillation Framework for Language Model Compression across Domains | https://aclanthology.org/2021.acl-long.236/ | [
"Haojie Pan",
"Chengyu Wang",
"Minghui Qiu",
"Yichang Zhang",
"Yaliang Li",
"Jun Huang"
] | 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 disti... | 2021.acl-long.236 | 10.18653/v1/2021.acl-long.236 | null | 2012.01266 | title_snapshot |
2021.acl-long.237 | A Semantic-based Method for Unsupervised Commonsense Question Answering | https://aclanthology.org/2021.acl-long.237/ | [
"Yilin Niu",
"Fei Huang",
"Jiaming Liang",
"Wenkai Chen",
"Xiaoyan Zhu",
"Minlie Huang"
] | 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 affe... | 2021.acl-long.237 | 10.18653/v1/2021.acl-long.237 | null | 2105.14781 | title_snapshot |
2021.acl-long.238 | Explanations for CommonsenseQA: New Dataset and Models | https://aclanthology.org/2021.acl-long.238/ | [
"Shourya Aggarwal",
"Divyanshu Mandowara",
"Vishwajeet Agrawal",
"Dinesh Khandelwal",
"Parag Singla",
"Dinesh Garg"
] | 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 ch... | 2021.acl-long.238 | 10.18653/v1/2021.acl-long.238 | null | null | null |
2021.acl-long.239 | Few-Shot Question Answering by Pretraining Span Selection | https://aclanthology.org/2021.acl-long.239/ | [
"Ori Ram",
"Yuval Kirstain",
"Jonathan Berant",
"Amir Globerson",
"Omer Levy"
] | 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, hig... | 2021.acl-long.239 | 10.18653/v1/2021.acl-long.239 | null | 2101.00438 | title_snapshot |
2021.acl-long.240 | UnitedQA: A Hybrid Approach for Open Domain Question Answering | https://aclanthology.org/2021.acl-long.240/ | [
"Hao Cheng",
"Yelong Shen",
"Xiaodong Liu",
"Pengcheng He",
"Weizhu Chen",
"Jianfeng Gao"
] | 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 upo... | 2021.acl-long.240 | 10.18653/v1/2021.acl-long.240 | null | 2101.00178 | title_snapshot |
2021.acl-long.241 | Database reasoning over text | https://aclanthology.org/2021.acl-long.241/ | [
"James Thorne",
"Majid Yazdani",
"Marzieh Saeidi",
"Fabrizio Silvestri",
"Sebastian Riedel",
"Alon Halevy"
] | 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 “List/Count all female athletes who were born in 20th century”, which require reasoning over sets of relevant facts with operations such as join,... | 2021.acl-long.241 | 10.18653/v1/2021.acl-long.241 | null | 2106.01074 | title_snapshot |
2021.acl-long.242 | Online Learning Meets Machine Translation Evaluation: Finding the Best Systems with the Least Human Effort | https://aclanthology.org/2021.acl-long.242/ | [
"Vânia Mendonça",
"Ricardo Rei",
"Luisa Coheur",
"Alberto Sardinha",
"Ana Lúcia Santos"
] | 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 chal... | 2021.acl-long.242 | 10.18653/v1/2021.acl-long.242 | null | 2105.13385 | title_snapshot |
2021.acl-long.243 | How Good is Your Tokenizer? On the Monolingual Performance of Multilingual Language Models | https://aclanthology.org/2021.acl-long.243/ | [
"Phillip Rust",
"Jonas Pfeiffer",
"Ivan Vulić",
"Sebastian Ruder",
"Iryna Gurevych"
] | 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 ... | 2021.acl-long.243 | 10.18653/v1/2021.acl-long.243 | null | 2012.15613 | title_snapshot |
2021.acl-long.244 | Evaluating morphological typology in zero-shot cross-lingual transfer | https://aclanthology.org/2021.acl-long.244/ | [
"Antonio Martínez-García",
"Toni Badia",
"Jeremy Barnes"
] | 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 ... | 2021.acl-long.244 | 10.18653/v1/2021.acl-long.244 | null | null | null |
2021.acl-long.245 | From Machine Translation to Code-Switching: Generating High-Quality Code-Switched Text | https://aclanthology.org/2021.acl-long.245/ | [
"Ishan Tarunesh",
"Syamantak Kumar",
"Preethi Jyothi"
] | 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 sente... | 2021.acl-long.245 | 10.18653/v1/2021.acl-long.245 | null | 2107.06483 | title_snapshot |
2021.acl-long.246 | Fast and Accurate Neural Machine Translation with Translation Memory | https://aclanthology.org/2021.acl-long.246/ | [
"Qiuxiang He",
"Guoping Huang",
"Qu Cui",
"Li Li",
"Lemao Liu"
] | 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... | 2021.acl-long.246 | 10.18653/v1/2021.acl-long.246 | null | null | null |
2021.acl-long.247 | Annotating Online Misogyny | https://aclanthology.org/2021.acl-long.247/ | [
"Philine Zeinert",
"Nanna Inie",
"Leon Derczynski"
] | 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 divers... | 2021.acl-long.247 | 10.18653/v1/2021.acl-long.247 | null | null | null |
2021.acl-long.248 | Few-NERD: A Few-shot Named Entity Recognition Dataset | https://aclanthology.org/2021.acl-long.248/ | [
"Ning Ding",
"Guangwei Xu",
"Yulin Chen",
"Xiaobin Wang",
"Xu Han",
"Pengjun Xie",
"Haitao Zheng",
"Zhiyuan Liu"
] | 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 empiric... | 2021.acl-long.248 | 10.18653/v1/2021.acl-long.248 | null | 2105.07464 | title_snapshot |
2021.acl-long.249 | MultiMET: A Multimodal Dataset for Metaphor Understanding | https://aclanthology.org/2021.acl-long.249/ | [
"Dongyu Zhang",
"Minghao Zhang",
"Heting Zhang",
"Liang Yang",
"Hongfei Lin"
] | 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 a... | 2021.acl-long.249 | 10.18653/v1/2021.acl-long.249 | null | null | null |
2021.acl-long.250 | Human-in-the-Loop for Data Collection: a Multi-Target Counter Narrative Dataset to Fight Online Hate Speech | https://aclanthology.org/2021.acl-long.250/ | [
"Margherita Fanton",
"Helena Bonaldi",
"Serra Sinem Tekiroğlu",
"Marco Guerini"
] | 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 ... | 2021.acl-long.250 | 10.18653/v1/2021.acl-long.250 | null | 2107.08720 | title_snapshot |
2021.acl-long.251 | Can Generative Pre-trained Language Models Serve As Knowledge Bases for Closed-book QA? | https://aclanthology.org/2021.acl-long.251/ | [
"Cunxiang Wang",
"Pai Liu",
"Yue Zhang"
] | 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 perform... | 2021.acl-long.251 | 10.18653/v1/2021.acl-long.251 | null | 2106.01561 | title_snapshot |
2021.acl-long.252 | Joint Models for Answer Verification in Question Answering Systems | https://aclanthology.org/2021.acl-long.252/ | [
"Zeyu Zhang",
"Thuy Vu",
"Alessandro Moschitti"
] | 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 inter... | 2021.acl-long.252 | 10.18653/v1/2021.acl-long.252 | null | 2107.04217 | title_snapshot |
2021.acl-long.253 | Answering Ambiguous Questions through Generative Evidence Fusion and Round-Trip Prediction | https://aclanthology.org/2021.acl-long.253/ | [
"Yifan Gao",
"Henghui Zhu",
"Patrick Ng",
"Cicero Nogueira dos Santos",
"Zhiguo Wang",
"Feng Nan",
"Dejiao Zhang",
"Ramesh Nallapati",
"Andrew O. Arnold",
"Bing Xiang"
] | 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 fou... | 2021.acl-long.253 | 10.18653/v1/2021.acl-long.253 | null | 2011.13137 | title_snapshot |
2021.acl-long.254 | TAT-QA: A Question Answering Benchmark on a Hybrid of Tabular and Textual Content in Finance | https://aclanthology.org/2021.acl-long.254/ | [
"Fengbin Zhu",
"Wenqiang Lei",
"Youcheng Huang",
"Chao Wang",
"Shuo Zhang",
"Jiancheng Lv",
"Fuli Feng",
"Tat-Seng Chua"
] | 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 con... | 2021.acl-long.254 | 10.18653/v1/2021.acl-long.254 | null | 2105.07624 | title_snapshot |
2021.acl-long.255 | Modeling Transitions of Focal Entities for Conversational Knowledge Base Question Answering | https://aclanthology.org/2021.acl-long.255/ | [
"Yunshi Lan",
"Jing Jiang"
] | 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 convers... | 2021.acl-long.255 | 10.18653/v1/2021.acl-long.255 | null | null | null |
2021.acl-long.256 | Evidence-based Factual Error Correction | https://aclanthology.org/2021.acl-long.256/ | [
"James Thorne",
"Andreas Vlachos"
] | 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 de... | 2021.acl-long.256 | 10.18653/v1/2021.acl-long.256 | null | 2012.15788 | title_snapshot |
2021.acl-long.257 | Probabilistic, Structure-Aware Algorithms for Improved Variety, Accuracy, and Coverage of AMR Alignments | https://aclanthology.org/2021.acl-long.257/ | [
"Austin Blodgett",
"Nathan Schneider"
] | 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 substruc... | 2021.acl-long.257 | 10.18653/v1/2021.acl-long.257 | null | 2106.06002 | title_snapshot |
2021.acl-long.258 | Meta-Learning to Compositionally Generalize | https://aclanthology.org/2021.acl-long.258/ | [
"Henry Conklin",
"Bailin Wang",
"Kenny Smith",
"Ivan Titov"
] | 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 p... | 2021.acl-long.258 | 10.18653/v1/2021.acl-long.258 | null | 2106.04252 | title_snapshot |
2021.acl-long.259 | Taming Pre-trained Language Models with N-gram Representations for Low-Resource Domain Adaptation | https://aclanthology.org/2021.acl-long.259/ | [
"Shizhe Diao",
"Ruijia Xu",
"Hongjin Su",
"Yilei Jiang",
"Yan Song",
"Tong Zhang"
] | 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 ... | 2021.acl-long.259 | 10.18653/v1/2021.acl-long.259 | null | null | null |
2021.acl-long.260 | ERICA: Improving Entity and Relation Understanding for Pre-trained Language Models via Contrastive Learning | https://aclanthology.org/2021.acl-long.260/ | [
"Yujia Qin",
"Yankai Lin",
"Ryuichi Takanobu",
"Zhiyuan Liu",
"Peng Li",
"Heng Ji",
"Minlie Huang",
"Maosong Sun",
"Jie Zhou"
] | 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 contras... | 2021.acl-long.260 | 10.18653/v1/2021.acl-long.260 | null | 2012.15022 | title_snapshot |
2021.acl-long.261 | Position Bias Mitigation: A Knowledge-Aware Graph Model for Emotion Cause Extraction | https://aclanthology.org/2021.acl-long.261/ | [
"Hanqi Yan",
"Lin Gui",
"Gabriele Pergola",
"Yulan He"
] | 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... | 2021.acl-long.261 | 10.18653/v1/2021.acl-long.261 | null | 2106.03518 | title_snapshot |
2021.acl-long.262 | Every Bite Is an Experience: Key Point Analysis of Business Reviews | https://aclanthology.org/2021.acl-long.262/ | [
"Roy Bar-Haim",
"Lilach Eden",
"Yoav Kantor",
"Roni Friedman",
"Noam Slonim"
] | 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 r... | 2021.acl-long.262 | 10.18653/v1/2021.acl-long.262 | null | 2106.06758 | title_snapshot |
2021.acl-long.263 | Structured Sentiment Analysis as Dependency Graph Parsing | https://aclanthology.org/2021.acl-long.263/ | [
"Jeremy Barnes",
"Robin Kurtz",
"Stephan Oepen",
"Lilja Øvrelid",
"Erik Velldal"
] | 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 ... | 2021.acl-long.263 | 10.18653/v1/2021.acl-long.263 | null | 2105.14504 | title_snapshot |
2021.acl-long.264 | Consistency Regularization for Cross-Lingual Fine-Tuning | https://aclanthology.org/2021.acl-long.264/ | [
"Bo Zheng",
"Li Dong",
"Shaohan Huang",
"Wenhui Wang",
"Zewen Chi",
"Saksham Singhal",
"Wanxiang Che",
"Ting Liu",
"Xia Song",
"Furu Wei"
] | 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 f... | 2021.acl-long.264 | 10.18653/v1/2021.acl-long.264 | null | 2106.08226 | title_snapshot |
2021.acl-long.265 | Improving Pretrained Cross-Lingual Language Models via Self-Labeled Word Alignment | https://aclanthology.org/2021.acl-long.265/ | [
"Zewen Chi",
"Li Dong",
"Bo Zheng",
"Shaohan Huang",
"Xian-Ling Mao",
"Heyan Huang",
"Furu Wei"
] | 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 ran... | 2021.acl-long.265 | 10.18653/v1/2021.acl-long.265 | null | 2106.06381 | title_snapshot |
2021.acl-long.266 | Rejuvenating Low-Frequency Words: Making the Most of Parallel Data in Non-Autoregressive Translation | https://aclanthology.org/2021.acl-long.266/ | [
"Liang Ding",
"Longyue Wang",
"Xuebo Liu",
"Derek F. Wong",
"Dacheng Tao",
"Zhaopeng Tu"
] | 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, w... | 2021.acl-long.266 | 10.18653/v1/2021.acl-long.266 | null | 2106.00903 | title_snapshot |
2021.acl-long.267 | G-Transformer for Document-Level Machine Translation | https://aclanthology.org/2021.acl-long.267/ | [
"Guangsheng Bao",
"Yue Zhang",
"Zhiyang Teng",
"Boxing Chen",
"Weihua Luo"
] | 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 ... | 2021.acl-long.267 | 10.18653/v1/2021.acl-long.267 | null | 2105.14761 | title_snapshot |
2021.acl-long.268 | Prevent the Language Model from being Overconfident in Neural Machine Translation | https://aclanthology.org/2021.acl-long.268/ | [
"Mengqi Miao",
"Fandong Meng",
"Yijin Liu",
"Xiao-Hua Zhou",
"Jie Zhou"
] | 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 ... | 2021.acl-long.268 | 10.18653/v1/2021.acl-long.268 | null | 2105.11098 | title_snapshot |
2021.acl-long.269 | Towards Emotional Support Dialog Systems | https://aclanthology.org/2021.acl-long.269/ | [
"Siyang Liu",
"Chujie Zheng",
"Orianna Demasi",
"Sahand Sabour",
"Yu Li",
"Zhou Yu",
"Yong Jiang",
"Minlie Huang"
] | 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... | 2021.acl-long.269 | 10.18653/v1/2021.acl-long.269 | null | 2106.01144 | title_snapshot |
2021.acl-long.270 | Novel Slot Detection: A Benchmark for Discovering Unknown Slot Types in the Task-Oriented Dialogue System | https://aclanthology.org/2021.acl-long.270/ | [
"Yanan Wu",
"Zhiyuan Zeng",
"Keqing He",
"Hong Xu",
"Yuanmeng Yan",
"Huixing Jiang",
"Weiran Xu"
] | 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 disc... | 2021.acl-long.270 | 10.18653/v1/2021.acl-long.270 | null | 2105.14313 | title_snapshot |
2021.acl-long.271 | GTM: A Generative Triple-wise Model for Conversational Question Generation | https://aclanthology.org/2021.acl-long.271/ | [
"Lei Shen",
"Fandong Meng",
"Jinchao Zhang",
"Yang Feng",
"Jie Zhou"
] | 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 “future” information, to guide question generation. However, ... | 2021.acl-long.271 | 10.18653/v1/2021.acl-long.271 | null | 2106.03635 | title_snapshot |
2021.acl-long.272 | Diversifying Dialog Generation via Adaptive Label Smoothing | https://aclanthology.org/2021.acl-long.272/ | [
"Yida Wang",
"Yinhe Zheng",
"Yong Jiang",
"Minlie Huang"
] | 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 context... | 2021.acl-long.272 | 10.18653/v1/2021.acl-long.272 | null | 2105.14556 | title_snapshot |
2021.acl-long.273 | Out-of-Distribution Intent Detection with Self-Supervision and Discriminative Training | https://aclanthology.org/2021.acl-long.273/ | [
"Li-Ming Zhan",
"Haowen Liang",
"Bo Liu",
"Lu Fan",
"Albert Y.S. Lam",
"Xiao-Ming Wu"
] | 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, ... | 2021.acl-long.273 | 10.18653/v1/2021.acl-long.273 | null | 2106.08616 | title_judge |
2021.acl-long.274 | Document-level Event Extraction via Heterogeneous Graph-based Interaction Model with a Tracker | https://aclanthology.org/2021.acl-long.274/ | [
"Runxin Xu",
"Tianyu Liu",
"Lei Li",
"Baobao Chang"
] | 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... | 2021.acl-long.274 | 10.18653/v1/2021.acl-long.274 | null | 2105.14924 | title_snapshot |
2021.acl-long.275 | Nested Named Entity Recognition via Explicitly Excluding the Influence of the Best Path | https://aclanthology.org/2021.acl-long.275/ | [
"Yiran Wang",
"Hiroyuki Shindo",
"Yuji Matsumoto",
"Taro Watanabe"
] | 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 di... | 2021.acl-long.275 | 10.18653/v1/2021.acl-long.275 | null | null | null |
2021.acl-long.276 | LearnDA: Learnable Knowledge-Guided Data Augmentation for Event Causality Identification | https://aclanthology.org/2021.acl-long.276/ | [
"Xinyu Zuo",
"Pengfei Cao",
"Yubo Chen",
"Kang Liu",
"Jun Zhao",
"Weihua Peng",
"Yuguang Chen"
] | 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 a... | 2021.acl-long.276 | 10.18653/v1/2021.acl-long.276 | null | 2106.01649 | title_snapshot |
2021.acl-long.277 | Revisiting the Negative Data of Distantly Supervised Relation Extraction | https://aclanthology.org/2021.acl-long.277/ | [
"Chenhao Xie",
"Jiaqing Liang",
"Jingping Liu",
"Chengsong Huang",
"Wenhao Huang",
"Yanghua Xiao"
] | 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 caus... | 2021.acl-long.277 | 10.18653/v1/2021.acl-long.277 | null | 2105.10158 | title_snapshot |
2021.acl-long.278 | Knowing the No-match: Entity Alignment with Dangling Cases | https://aclanthology.org/2021.acl-long.278/ | [
"Zequn Sun",
"Muhao Chen",
"Wei Hu"
] | 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 ... | 2021.acl-long.278 | 10.18653/v1/2021.acl-long.278 | null | 2106.02248 | title_snapshot |
2021.acl-long.279 | Superbizarre Is Not Superb: Derivational Morphology Improves BERT’s Interpretation of Complex Words | https://aclanthology.org/2021.acl-long.279/ | [
"Valentin Hofmann",
"Janet Pierrehumbert",
"Hinrich Schütze"
] | 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 d... | 2021.acl-long.279 | 10.18653/v1/2021.acl-long.279 | null | 2101.00403 | title_snapshot |
2021.acl-long.280 | BERT is to NLP what AlexNet is to CV: Can Pre-Trained Language Models Identify Analogies? | https://aclanthology.org/2021.acl-long.280/ | [
"Asahi Ushio",
"Luis Espinosa Anke",
"Steven Schockaert",
"Jose Camacho-Collados"
] | Analogies play a central role in human commonsense reasoning. The ability to recognize analogies such as “eye is to seeing what ear is to hearing”, sometimes referred to as analogical proportions, shape how we structure knowledge and understand language. Surprisingly, however, the task of identifying such analogies has... | 2021.acl-long.280 | 10.18653/v1/2021.acl-long.280 | null | 2105.04949 | title_snapshot |
2021.acl-long.281 | Exploring the Representation of Word Meanings in Context: A Case Study on Homonymy and Synonymy | https://aclanthology.org/2021.acl-long.281/ | [
"Marcos Garcia"
] | 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 con... | 2021.acl-long.281 | 10.18653/v1/2021.acl-long.281 | null | 2106.13553 | title_snapshot |
2021.acl-long.282 | Measuring Fine-Grained Domain Relevance of Terms: A Hierarchical Core-Fringe Approach | https://aclanthology.org/2021.acl-long.282/ | [
"Jie Huang",
"Kevin Chang",
"JinJun Xiong",
"Wen-mei Hwu"
] | We propose to measure fine-grained domain relevance– 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, wh... | 2021.acl-long.282 | 10.18653/v1/2021.acl-long.282 | null | 2105.13255 | title_snapshot |
2021.acl-long.283 | HERALD: An Annotation Efficient Method to Detect User Disengagement in Social Conversations | https://aclanthology.org/2021.acl-long.283/ | [
"Weixin Liang",
"Kai-Hui Liang",
"Zhou Yu"
] | 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... | 2021.acl-long.283 | 10.18653/v1/2021.acl-long.283 | null | 2106.00162 | title_snapshot |
2021.acl-long.284 | Value-Agnostic Conversational Semantic Parsing | https://aclanthology.org/2021.acl-long.284/ | [
"Emmanouil Antonios Platanios",
"Adam Pauls",
"Subhro Roy",
"Yuchen Zhang",
"Alexander Kyte",
"Alan Guo",
"Sam Thomson",
"Jayant Krishnamurthy",
"Jason Wolfe",
"Jacob Andreas",
"Dan Klein"
] | 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 p... | 2021.acl-long.284 | 10.18653/v1/2021.acl-long.284 | null | null | null |
2021.acl-long.285 | MPC-BERT: A Pre-Trained Language Model for Multi-Party Conversation Understanding | https://aclanthology.org/2021.acl-long.285/ | [
"Jia-Chen Gu",
"Chongyang Tao",
"Zhenhua Ling",
"Can Xu",
"Xiubo Geng",
"Daxin Jiang"
] | 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 ... | 2021.acl-long.285 | 10.18653/v1/2021.acl-long.285 | null | 2106.01541 | title_snapshot |
2021.acl-long.286 | Best of Both Worlds: Making High Accuracy Non-incremental Transformer-based Disfluency Detection Incremental | https://aclanthology.org/2021.acl-long.286/ | [
"Morteza Rohanian",
"Julian Hough"
] | 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... | 2021.acl-long.286 | 10.18653/v1/2021.acl-long.286 | null | null | null |
2021.acl-long.287 | NeuralWOZ: Learning to Collect Task-Oriented Dialogue via Model-Based Simulation | https://aclanthology.org/2021.acl-long.287/ | [
"Sungdong Kim",
"Minsuk Chang",
"Sang-Woo Lee"
] | 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’s goal instructions, which are the user context and task constraints in natural language, and (2) system’s API cal... | 2021.acl-long.287 | 10.18653/v1/2021.acl-long.287 | null | 2105.14454 | title_snapshot |
2021.acl-long.288 | CDRNN: Discovering Complex Dynamics in Human Language Processing | https://aclanthology.org/2021.acl-long.288/ | [
"Cory Shain"
] | 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 d... | 2021.acl-long.288 | 10.18653/v1/2021.acl-long.288 | null | null | null |
2021.acl-long.289 | Structural Guidance for Transformer Language Models | https://aclanthology.org/2021.acl-long.289/ | [
"Peng Qian",
"Tahira Naseem",
"Roger Levy",
"Ramón Fernandez Astudillo"
] | 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 resort... | 2021.acl-long.289 | 10.18653/v1/2021.acl-long.289 | null | 2108.00104 | title_snapshot |
2021.acl-long.290 | Surprisal Estimators for Human Reading Times Need Character Models | https://aclanthology.org/2021.acl-long.290/ | [
"Byung-Doh Oh",
"Christian Clark",
"William Schuler"
] | 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... | 2021.acl-long.290 | 10.18653/v1/2021.acl-long.290 | null | null | null |
2021.acl-long.291 | CogAlign: Learning to Align Textual Neural Representations to Cognitive Language Processing Signals | https://aclanthology.org/2021.acl-long.291/ | [
"Yuqi Ren",
"Deyi Xiong"
] | 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 co... | 2021.acl-long.291 | 10.18653/v1/2021.acl-long.291 | null | 2106.05544 | title_snapshot |
2021.acl-long.292 | Self-Attention Networks Can Process Bounded Hierarchical Languages | https://aclanthology.org/2021.acl-long.292/ | [
"Shunyu Yao",
"Binghui Peng",
"Christos Papadimitriou",
"Karthik Narasimhan"
] | 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 th... | 2021.acl-long.292 | 10.18653/v1/2021.acl-long.292 | null | 2105.11115 | title_snapshot |
2021.acl-long.293 | TextSETTR: Few-Shot Text Style Extraction and Tunable Targeted Restyling | https://aclanthology.org/2021.acl-long.293/ | [
"Parker Riley",
"Noah Constant",
"Mandy Guo",
"Girish Kumar",
"David Uthus",
"Zarana Parekh"
] | 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 ... | 2021.acl-long.293 | 10.18653/v1/2021.acl-long.293 | null | 2010.03802 | title_snapshot |
2021.acl-long.294 | H-Transformer-1D: Fast One-Dimensional Hierarchical Attention for Sequences | https://aclanthology.org/2021.acl-long.294/ | [
"Zhenhai Zhu",
"Radu Soricut"
] | 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... | 2021.acl-long.294 | 10.18653/v1/2021.acl-long.294 | null | 2107.11906 | title_snapshot |
2021.acl-long.295 | Making Pre-trained Language Models Better Few-shot Learners | https://aclanthology.org/2021.acl-long.295/ | [
"Tianyu Gao",
"Adam Fisch",
"Danqi Chen"
] | 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-tun... | 2021.acl-long.295 | 10.18653/v1/2021.acl-long.295 | null | 2012.15723 | title_snapshot |
2021.acl-long.296 | A Sweet Rabbit Hole by DARCY: Using Honeypots to Detect Universal Trigger’s Adversarial Attacks | https://aclanthology.org/2021.acl-long.296/ | [
"Thai Le",
"Noseong Park",
"Dongwon Lee"
] | 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. T... | 2021.acl-long.296 | 10.18653/v1/2021.acl-long.296 | null | 2011.10492 | title_snapshot |
2021.acl-long.297 | Towards Propagation Uncertainty: Edge-enhanced Bayesian Graph Convolutional Networks for Rumor Detection | https://aclanthology.org/2021.acl-long.297/ | [
"Lingwei Wei",
"Dou Hu",
"Wei Zhou",
"Zhaojuan Yue",
"Songlin Hu"
] | 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 inev... | 2021.acl-long.297 | 10.18653/v1/2021.acl-long.297 | null | 2107.11934 | title_snapshot |
2021.acl-long.298 | Label-Specific Dual Graph Neural Network for Multi-Label Text Classification | https://aclanthology.org/2021.acl-long.298/ | [
"Qianwen Ma",
"Chunyuan Yuan",
"Wei Zhou",
"Songlin Hu"
] | 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 ... | 2021.acl-long.298 | 10.18653/v1/2021.acl-long.298 | null | null | null |
2021.acl-long.299 | TAN-NTM: Topic Attention Networks for Neural Topic Modeling | https://aclanthology.org/2021.acl-long.299/ | [
"Madhur Panwar",
"Shashank Shailabh",
"Milan Aggarwal",
"Balaji Krishnamurthy"
] | 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 distributio... | 2021.acl-long.299 | 10.18653/v1/2021.acl-long.299 | null | 2012.01524 | title_snapshot |
2021.acl-long.300 | Cross-language Sentence Selection via Data Augmentation and Rationale Training | https://aclanthology.org/2021.acl-long.300/ | [
"Yanda Chen",
"Chris Kedzie",
"Suraj Nair",
"Petra Galuscakova",
"Rui Zhang",
"Douglas Oard",
"Kathleen McKeown"
] | 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 bette... | 2021.acl-long.300 | 10.18653/v1/2021.acl-long.300 | null | 2106.02293 | title_snapshot |
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