paper_id stringlengths 17 19 | title stringlengths 27 144 | paper_url stringlengths 43 45 | authors listlengths 1 37 | abstract large_stringlengths 396 1.69k | anthology_id stringlengths 17 19 | doi stringlengths 29 31 | award stringclasses 0
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2021.emnlp-main.201 | Iterative GNN-based Decoder for Question Generation | https://aclanthology.org/2021.emnlp-main.201/ | [
"Zichu Fei",
"Qi Zhang",
"Yaqian Zhou"
] | Natural question generation (QG) aims to generate questions from a passage, and generated questions are answered from the passage. Most models with state-of-the-art performance model the previously generated text at each decoding step. However, (1) they ignore the rich structure information that is hidden in the previo... | 2021.emnlp-main.201 | 10.18653/v1/2021.emnlp-main.201 | null | null | null |
2021.emnlp-main.202 | Asking Questions Like Educational Experts: Automatically Generating Question-Answer Pairs on Real-World Examination Data | https://aclanthology.org/2021.emnlp-main.202/ | [
"Fanyi Qu",
"Xin Jia",
"Yunfang Wu"
] | Generating high quality question-answer pairs is a hard but meaningful task. Although previous works have achieved great results on answer-aware question generation, it is difficult to apply them into practical application in the education field. This paper for the first time addresses the question-answer pair generati... | 2021.emnlp-main.202 | 10.18653/v1/2021.emnlp-main.202 | null | 2109.05179 | title_snapshot |
2021.emnlp-main.203 | Syntactically-Informed Unsupervised Paraphrasing with Non-Parallel Data | https://aclanthology.org/2021.emnlp-main.203/ | [
"Erguang Yang",
"Mingtong Liu",
"Deyi Xiong",
"Yujie Zhang",
"Yao Meng",
"Changjian Hu",
"Jinan Xu",
"Yufeng Chen"
] | Previous works on syntactically controlled paraphrase generation heavily rely on large-scale parallel paraphrase data that is not easily available for many languages and domains. In this paper, we take this research direction to the extreme and investigate whether it is possible to learn syntactically controlled paraph... | 2021.emnlp-main.203 | 10.18653/v1/2021.emnlp-main.203 | null | null | null |
2021.emnlp-main.204 | Exploring Task Difficulty for Few-Shot Relation Extraction | https://aclanthology.org/2021.emnlp-main.204/ | [
"Jiale Han",
"Bo Cheng",
"Wei Lu"
] | Few-shot relation extraction (FSRE) focuses on recognizing novel relations by learning with merely a handful of annotated instances. Meta-learning has been widely adopted for such a task, which trains on randomly generated few-shot tasks to learn generic data representations. Despite impressive results achieved, existi... | 2021.emnlp-main.204 | 10.18653/v1/2021.emnlp-main.204 | null | 2109.05473 | title_snapshot |
2021.emnlp-main.205 | MuVER: Improving First-Stage Entity Retrieval with Multi-View Entity Representations | https://aclanthology.org/2021.emnlp-main.205/ | [
"Xinyin Ma",
"Yong Jiang",
"Nguyen Bach",
"Tao Wang",
"Zhongqiang Huang",
"Fei Huang",
"Weiming Lu"
] | Entity retrieval, which aims at disambiguating mentions to canonical entities from massive KBs, is essential for many tasks in natural language processing. Recent progress in entity retrieval shows that the dual-encoder structure is a powerful and efficient framework to nominate candidates if entities are only identifi... | 2021.emnlp-main.205 | 10.18653/v1/2021.emnlp-main.205 | null | 2109.05716 | title_snapshot |
2021.emnlp-main.206 | Treasures Outside Contexts: Improving Event Detection via Global Statistics | https://aclanthology.org/2021.emnlp-main.206/ | [
"Rui Li",
"Wenlin Zhao",
"Cheng Yang",
"Sen Su"
] | Event detection (ED) aims at identifying event instances of specified types in given texts, which has been formalized as a sequence labeling task. As far as we know, existing neural-based ED models make decisions relying entirely on the contextual semantic features of each word in the inputted text, which we find is ea... | 2021.emnlp-main.206 | 10.18653/v1/2021.emnlp-main.206 | null | null | null |
2021.emnlp-main.207 | Uncertain Local-to-Global Networks for Document-Level Event Factuality Identification | https://aclanthology.org/2021.emnlp-main.207/ | [
"Pengfei Cao",
"Yubo Chen",
"Yuqing Yang",
"Kang Liu",
"Jun Zhao"
] | Event factuality indicates the degree of certainty about whether an event occurs in the real world. Existing studies mainly focus on identifying event factuality at sentence level, which easily leads to conflicts between different mentions of the same event. To this end, we study the problem of document-level event fac... | 2021.emnlp-main.207 | 10.18653/v1/2021.emnlp-main.207 | null | null | null |
2021.emnlp-main.208 | A Novel Global Feature-Oriented Relational Triple Extraction Model based on Table Filling | https://aclanthology.org/2021.emnlp-main.208/ | [
"Feiliang Ren",
"Longhui Zhang",
"Shujuan Yin",
"Xiaofeng Zhao",
"Shilei Liu",
"Bochao Li",
"Yaduo Liu"
] | Table filling based relational triple extraction methods are attracting growing research interests due to their promising performance and their abilities on extracting triples from complex sentences. However, this kind of methods are far from their full potential because most of them only focus on using local features ... | 2021.emnlp-main.208 | 10.18653/v1/2021.emnlp-main.208 | null | 2109.06705 | title_snapshot |
2021.emnlp-main.209 | Structure-Augmented Keyphrase Generation | https://aclanthology.org/2021.emnlp-main.209/ | [
"Jihyuk Kim",
"Myeongho Jeong",
"Seungtaek Choi",
"Seung-won Hwang"
] | This paper studies the keyphrase generation (KG) task for scenarios where structure plays an important role. For example, a scientific publication consists of a short title and a long body, where the title can be used for de-emphasizing unimportant details in the body. Similarly, for short social media posts (, tweets)... | 2021.emnlp-main.209 | 10.18653/v1/2021.emnlp-main.209 | null | null | null |
2021.emnlp-main.210 | An Empirical Study on Multiple Information Sources for Zero-Shot Fine-Grained Entity Typing | https://aclanthology.org/2021.emnlp-main.210/ | [
"Yi Chen",
"Haiyun Jiang",
"Lemao Liu",
"Shuming Shi",
"Chuang Fan",
"Min Yang",
"Ruifeng Xu"
] | Auxiliary information from multiple sources has been demonstrated to be effective in zero-shot fine-grained entity typing (ZFET). However, there lacks a comprehensive understanding about how to make better use of the existing information sources and how they affect the performance of ZFET. In this paper, we empirically... | 2021.emnlp-main.210 | 10.18653/v1/2021.emnlp-main.210 | null | null | null |
2021.emnlp-main.211 | DyLex: Incorporating Dynamic Lexicons into BERT for Sequence Labeling | https://aclanthology.org/2021.emnlp-main.211/ | [
"Baojun Wang",
"Zhao Zhang",
"Kun Xu",
"Guang-Yuan Hao",
"Yuyang Zhang",
"Lifeng Shang",
"Linlin Li",
"Xiao Chen",
"Xin Jiang",
"Qun Liu"
] | Incorporating lexical knowledge into deep learning models has been proved to be very effective for sequence labeling tasks. However, previous works commonly have difficulty dealing with large-scale dynamic lexicons which often cause excessive matching noise and problems of frequent updates. In this paper, we propose Dy... | 2021.emnlp-main.211 | 10.18653/v1/2021.emnlp-main.211 | null | 2109.08818 | title_snapshot |
2021.emnlp-main.212 | MapRE: An Effective Semantic Mapping Approach for Low-resource Relation Extraction | https://aclanthology.org/2021.emnlp-main.212/ | [
"Manqing Dong",
"Chunguang Pan",
"Zhipeng Luo"
] | Neural relation extraction models have shown promising results in recent years; however, the model performance drops dramatically given only a few training samples. Recent works try leveraging the advance in few-shot learning to solve the low resource problem, where they train label-agnostic models to directly compare ... | 2021.emnlp-main.212 | 10.18653/v1/2021.emnlp-main.212 | null | 2109.04108 | title_snapshot |
2021.emnlp-main.213 | Heterogeneous Graph Neural Networks for Keyphrase Generation | https://aclanthology.org/2021.emnlp-main.213/ | [
"Jiacheng Ye",
"Ruijian Cai",
"Tao Gui",
"Qi Zhang"
] | The encoder–decoder framework achieves state-of-the-art results in keyphrase generation (KG) tasks by predicting both present keyphrases that appear in the source document and absent keyphrases that do not. However, relying solely on the source document can result in generating uncontrollable and inaccurate absent keyp... | 2021.emnlp-main.213 | 10.18653/v1/2021.emnlp-main.213 | null | 2109.04703 | title_snapshot |
2021.emnlp-main.214 | Machine Reading Comprehension as Data Augmentation: A Case Study on Implicit Event Argument Extraction | https://aclanthology.org/2021.emnlp-main.214/ | [
"Jian Liu",
"Yufeng Chen",
"Jinan Xu"
] | Implicit event argument extraction (EAE) is a crucial document-level information extraction task that aims to identify event arguments beyond the sentence level. Despite many efforts for this task, the lack of enough training data has long impeded the study. In this paper, we take a new perspective to address the data ... | 2021.emnlp-main.214 | 10.18653/v1/2021.emnlp-main.214 | null | null | null |
2021.emnlp-main.215 | Importance Estimation from Multiple Perspectives for Keyphrase Extraction | https://aclanthology.org/2021.emnlp-main.215/ | [
"Mingyang Song",
"Liping Jing",
"Lin Xiao"
] | Keyphrase extraction is a fundamental task in Natural Language Processing, which usually contains two main parts: candidate keyphrase extraction and keyphrase importance estimation. From the view of human understanding documents, we typically measure the importance of phrase according to its syntactic accuracy, informa... | 2021.emnlp-main.215 | 10.18653/v1/2021.emnlp-main.215 | null | 2110.09749 | title_snapshot |
2021.emnlp-main.216 | Gradient Imitation Reinforcement Learning for Low Resource Relation Extraction | https://aclanthology.org/2021.emnlp-main.216/ | [
"Xuming Hu",
"Chenwei Zhang",
"Yawen Yang",
"Xiaohe Li",
"Li Lin",
"Lijie Wen",
"Philip S. Yu"
] | Low-resource Relation Extraction (LRE) aims to extract relation facts from limited labeled corpora when human annotation is scarce. Existing works either utilize self-training scheme to generate pseudo labels that will cause the gradual drift problem, or leverage meta-learning scheme which does not solicit feedback exp... | 2021.emnlp-main.216 | 10.18653/v1/2021.emnlp-main.216 | null | 2109.06415 | title_snapshot |
2021.emnlp-main.217 | Low-resource Taxonomy Enrichment with Pretrained Language Models | https://aclanthology.org/2021.emnlp-main.217/ | [
"Kunihiro Takeoka",
"Kosuke Akimoto",
"Masafumi Oyamada"
] | Taxonomies are symbolic representations of hierarchical relationships between terms or entities. While taxonomies are useful in broad applications, manually updating or maintaining them is labor-intensive and difficult to scale in practice. Conventional supervised methods for this enrichment task fail to find optimal p... | 2021.emnlp-main.217 | 10.18653/v1/2021.emnlp-main.217 | null | null | null |
2021.emnlp-main.218 | Entity Relation Extraction as Dependency Parsing in Visually Rich Documents | https://aclanthology.org/2021.emnlp-main.218/ | [
"Yue Zhang",
"Zhang Bo",
"Rui Wang",
"Junjie Cao",
"Chen Li",
"Zuyi Bao"
] | Previous works on key information extraction from visually rich documents (VRDs) mainly focus on labeling the text within each bounding box (i.e.,semantic entity), while the relations in-between are largely unexplored. In this paper, we adapt the popular dependency parsing model, the biaffine parser, to this entity rel... | 2021.emnlp-main.218 | 10.18653/v1/2021.emnlp-main.218 | null | 2110.09915 | title_snapshot |
2021.emnlp-main.219 | Synchronous Dual Network with Cross-Type Attention for Joint Entity and Relation Extraction | https://aclanthology.org/2021.emnlp-main.219/ | [
"Hui Wu",
"Xiaodong Shi"
] | Joint entity and relation extraction is challenging due to the complex interaction of interaction between named entity recognition and relation extraction. Although most existing works tend to jointly train these two tasks through a shared network, they fail to fully utilize the interdependence between entity types and... | 2021.emnlp-main.219 | 10.18653/v1/2021.emnlp-main.219 | null | null | null |
2021.emnlp-main.220 | Less is More: Pretrain a Strong Siamese Encoder for Dense Text Retrieval Using a Weak Decoder | https://aclanthology.org/2021.emnlp-main.220/ | [
"Shuqi Lu",
"Di He",
"Chenyan Xiong",
"Guolin Ke",
"Waleed Malik",
"Zhicheng Dou",
"Paul Bennett",
"Tie-Yan Liu",
"Arnold Overwijk"
] | Dense retrieval requires high-quality text sequence embeddings to support effective search in the representation space. Autoencoder-based language models are appealing in dense retrieval as they train the encoder to output high-quality embedding that can reconstruct the input texts. However, in this paper, we provide t... | 2021.emnlp-main.220 | 10.18653/v1/2021.emnlp-main.220 | null | 2102.09206 | title_judge |
2021.emnlp-main.221 | TransPrompt: Towards an Automatic Transferable Prompting Framework for Few-shot Text Classification | https://aclanthology.org/2021.emnlp-main.221/ | [
"Chengyu Wang",
"Jianing Wang",
"Minghui Qiu",
"Jun Huang",
"Ming Gao"
] | Recent studies have shown that prompts improve the performance of large pre-trained language models for few-shot text classification. Yet, it is unclear how the prompting knowledge can be transferred across similar NLP tasks for the purpose of mutual reinforcement. Based on continuous prompt embeddings, we propose Tran... | 2021.emnlp-main.221 | 10.18653/v1/2021.emnlp-main.221 | null | null | null |
2021.emnlp-main.222 | Weakly-supervised Text Classification Based on Keyword Graph | https://aclanthology.org/2021.emnlp-main.222/ | [
"Lu Zhang",
"Jiandong Ding",
"Yi Xu",
"Yingyao Liu",
"Shuigeng Zhou"
] | Weakly-supervised text classification has received much attention in recent years for it can alleviate the heavy burden of annotating massive data. Among them, keyword-driven methods are the mainstream where user-provided keywords are exploited to generate pseudo-labels for unlabeled texts. However, existing methods tr... | 2021.emnlp-main.222 | 10.18653/v1/2021.emnlp-main.222 | null | 2110.02591 | title_snapshot |
2021.emnlp-main.223 | Efficient-FedRec: Efficient Federated Learning Framework for Privacy-Preserving News Recommendation | https://aclanthology.org/2021.emnlp-main.223/ | [
"Jingwei Yi",
"Fangzhao Wu",
"Chuhan Wu",
"Ruixuan Liu",
"Guangzhong Sun",
"Xing Xie"
] | News recommendation is critical for personalized news access. Most existing news recommendation methods rely on centralized storage of users’ historical news click behavior data, which may lead to privacy concerns and hazards. Federated Learning is a privacy-preserving framework for multiple clients to collaboratively ... | 2021.emnlp-main.223 | 10.18653/v1/2021.emnlp-main.223 | null | 2109.05446 | title_snapshot |
2021.emnlp-main.224 | RocketQAv2: A Joint Training Method for Dense Passage Retrieval and Passage Re-ranking | https://aclanthology.org/2021.emnlp-main.224/ | [
"Ruiyang Ren",
"Yingqi Qu",
"Jing Liu",
"Wayne Xin Zhao",
"QiaoQiao She",
"Hua Wu",
"Haifeng Wang",
"Ji-Rong Wen"
] | In various natural language processing tasks, passage retrieval and passage re-ranking are two key procedures in finding and ranking relevant information. Since both the two procedures contribute to the final performance, it is important to jointly optimize them in order to achieve mutual improvement. In this paper, we... | 2021.emnlp-main.224 | 10.18653/v1/2021.emnlp-main.224 | null | 2110.07367 | title_snapshot |
2021.emnlp-main.225 | Dealing with Typos for BERT-based Passage Retrieval and Ranking | https://aclanthology.org/2021.emnlp-main.225/ | [
"Shengyao Zhuang",
"Guido Zuccon"
] | Passage retrieval and ranking is a key task in open-domain question answering and information retrieval. Current effective approaches mostly rely on pre-trained deep language model-based retrievers and rankers. These methods have been shown to effectively model the semantic matching between queries and passages, also i... | 2021.emnlp-main.225 | 10.18653/v1/2021.emnlp-main.225 | null | 2108.12139 | title_snapshot |
2021.emnlp-main.226 | From Alignment to Assignment: Frustratingly Simple Unsupervised Entity Alignment | https://aclanthology.org/2021.emnlp-main.226/ | [
"Xin Mao",
"Wenting Wang",
"Yuanbin Wu",
"Man Lan"
] | Cross-lingual entity alignment (EA) aims to find the equivalent entities between crosslingual KGs (Knowledge Graphs), which is a crucial step for integrating KGs. Recently, many GNN-based EA methods are proposed and show decent performance improvements on several public datasets. However, existing GNN-based EA methods ... | 2021.emnlp-main.226 | 10.18653/v1/2021.emnlp-main.226 | null | 2109.02363 | title_snapshot |
2021.emnlp-main.227 | Simple and Effective Unsupervised Redundancy Elimination to Compress Dense Vectors for Passage Retrieval | https://aclanthology.org/2021.emnlp-main.227/ | [
"Xueguang Ma",
"Minghan Li",
"Kai Sun",
"Ji Xin",
"Jimmy Lin"
] | Recent work has shown that dense passage retrieval techniques achieve better ranking accuracy in open-domain question answering compared to sparse retrieval techniques such as BM25, but at the cost of large space and memory requirements. In this paper, we analyze the redundancy present in encoded dense vectors and show... | 2021.emnlp-main.227 | 10.18653/v1/2021.emnlp-main.227 | null | null | null |
2021.emnlp-main.228 | Relation Extraction with Word Graphs from N-grams | https://aclanthology.org/2021.emnlp-main.228/ | [
"Han Qin",
"Yuanhe Tian",
"Yan Song"
] | Most recent studies for relation extraction (RE) leverage the dependency tree of the input sentence to incorporate syntax-driven contextual information to improve model performance, with little attention paid to the limitation where high-quality dependency parsers in most cases unavailable, especially for in-domain sce... | 2021.emnlp-main.228 | 10.18653/v1/2021.emnlp-main.228 | null | null | null |
2021.emnlp-main.229 | A Bayesian Framework for Information-Theoretic Probing | https://aclanthology.org/2021.emnlp-main.229/ | [
"Tiago Pimentel",
"Ryan Cotterell"
] | Pimentel et al. (2020) recently analysed probing from an information-theoretic perspective. They argue that probing should be seen as approximating a mutual information. This led to the rather unintuitive conclusion that representations encode exactly the same information about a target task as the original sentences. ... | 2021.emnlp-main.229 | 10.18653/v1/2021.emnlp-main.229 | null | 2109.03853 | title_snapshot |
2021.emnlp-main.230 | Masked Language Modeling and the Distributional Hypothesis: Order Word Matters Pre-training for Little | https://aclanthology.org/2021.emnlp-main.230/ | [
"Koustuv Sinha",
"Robin Jia",
"Dieuwke Hupkes",
"Joelle Pineau",
"Adina Williams",
"Douwe Kiela"
] | A possible explanation for the impressive performance of masked language model (MLM) pre-training is that such models have learned to represent the syntactic structures prevalent in classical NLP pipelines. In this paper, we propose a different explanation: MLMs succeed on downstream tasks almost entirely due to their ... | 2021.emnlp-main.230 | 10.18653/v1/2021.emnlp-main.230 | null | 2104.06644 | title_snapshot |
2021.emnlp-main.231 | What’s Hidden in a One-layer Randomly Weighted Transformer? | https://aclanthology.org/2021.emnlp-main.231/ | [
"Sheng Shen",
"Zhewei Yao",
"Douwe Kiela",
"Kurt Keutzer",
"Michael Mahoney"
] | We demonstrate that, hidden within one-layer randomly weighted neural networks, there exist subnetworks that can achieve impressive performance, without ever modifying the weight initializations, on machine translation tasks. To find subnetworks for one-layer randomly weighted neural networks, we apply different binary... | 2021.emnlp-main.231 | 10.18653/v1/2021.emnlp-main.231 | null | 2109.03939 | title_snapshot |
2021.emnlp-main.232 | Rethinking Denoised Auto-Encoding in Language Pre-Training | https://aclanthology.org/2021.emnlp-main.232/ | [
"Fuli Luo",
"Pengcheng Yang",
"Shicheng Li",
"Xuancheng Ren",
"Xu Sun",
"Songfang Huang",
"Fei Huang"
] | Pre-trained self-supervised models such as BERT have achieved striking success in learning sequence representations, especially for natural language processing. These models typically corrupt the given sequences with certain types of noise, such as masking, shuffling, or substitution, and then try to recover the origin... | 2021.emnlp-main.232 | 10.18653/v1/2021.emnlp-main.232 | null | null | null |
2021.emnlp-main.233 | Lifelong Explainer for Lifelong Learners | https://aclanthology.org/2021.emnlp-main.233/ | [
"Xuelin Situ",
"Sameen Maruf",
"Ingrid Zukerman",
"Cecile Paris",
"Gholamreza Haffari"
] | Lifelong Learning (LL) black-box models are dynamic in that they keep learning from new tasks and constantly update their parameters. Owing to the need to utilize information from previously seen tasks, and capture commonalities in potentially diverse data, it is hard for automatic explanation methods to explain the ou... | 2021.emnlp-main.233 | 10.18653/v1/2021.emnlp-main.233 | null | null | null |
2021.emnlp-main.234 | Linguistic Dependencies and Statistical Dependence | https://aclanthology.org/2021.emnlp-main.234/ | [
"Jacob Louis Hoover",
"Wenyu Du",
"Alessandro Sordoni",
"Timothy J. O’Donnell"
] | Are pairs of words that tend to occur together also likely to stand in a linguistic dependency? This empirical question is motivated by a long history of literature in cognitive science, psycholinguistics, and NLP. In this work we contribute an extensive analysis of the relationship between linguistic dependencies and ... | 2021.emnlp-main.234 | 10.18653/v1/2021.emnlp-main.234 | null | 2104.08685 | title_snapshot |
2021.emnlp-main.235 | Modeling Human Sentence Processing with Left-Corner Recurrent Neural Network Grammars | https://aclanthology.org/2021.emnlp-main.235/ | [
"Ryo Yoshida",
"Hiroshi Noji",
"Yohei Oseki"
] | In computational linguistics, it has been shown that hierarchical structures make language models (LMs) more human-like. However, the previous literature has been agnostic about a parsing strategy of the hierarchical models. In this paper, we investigated whether hierarchical structures make LMs more human-like, and if... | 2021.emnlp-main.235 | 10.18653/v1/2021.emnlp-main.235 | null | 2109.04939 | title_snapshot |
2021.emnlp-main.236 | A Simple and Effective Positional Encoding for Transformers | https://aclanthology.org/2021.emnlp-main.236/ | [
"Pu-Chin Chen",
"Henry Tsai",
"Srinadh Bhojanapalli",
"Hyung Won Chung",
"Yin-Wen Chang",
"Chun-Sung Ferng"
] | Transformer models are permutation equivariant. To supply the order and type information of the input tokens, position and segment embeddings are usually added to the input. Recent works proposed variations of positional encodings with relative position encodings achieving better performance. Our analysis shows that th... | 2021.emnlp-main.236 | 10.18653/v1/2021.emnlp-main.236 | null | 2104.08698 | title_snapshot |
2021.emnlp-main.237 | Explore Better Relative Position Embeddings from Encoding Perspective for Transformer Models | https://aclanthology.org/2021.emnlp-main.237/ | [
"Anlin Qu",
"Jianwei Niu",
"Shasha Mo"
] | Relative position embedding (RPE) is a successful method to explicitly and efficaciously encode position information into Transformer models. In this paper, we investigate the potential problems in Shaw-RPE and XL-RPE, which are the most representative and prevalent RPEs, and propose two novel RPEs called Low-level Fin... | 2021.emnlp-main.237 | 10.18653/v1/2021.emnlp-main.237 | null | null | null |
2021.emnlp-main.238 | Adversarial Mixing Policy for Relaxing Locally Linear Constraints in Mixup | https://aclanthology.org/2021.emnlp-main.238/ | [
"Guang Liu",
"Yuzhao Mao",
"Huang Hailong",
"Gao Weiguo",
"Li Xuan"
] | Mixup is a recent regularizer for current deep classification networks. Through training a neural network on convex combinations of pairs of examples and their labels, it imposes locally linear constraints on the model’s input space. However, such strict linear constraints often lead to under-fitting which degrades the... | 2021.emnlp-main.238 | 10.18653/v1/2021.emnlp-main.238 | null | 2109.07177 | title_snapshot |
2021.emnlp-main.239 | Is this the end of the gold standard? A straightforward reference-less grammatical error correction metric | https://aclanthology.org/2021.emnlp-main.239/ | [
"Md Asadul Islam",
"Enrico Magnani"
] | It is difficult to rank and evaluate the performance of grammatical error correction (GEC) systems, as a sentence can be rewritten in numerous correct ways. A number of GEC metrics have been used to evaluate proposed GEC systems; however, each system relies on either a comparison with one or more reference texts—in wha... | 2021.emnlp-main.239 | 10.18653/v1/2021.emnlp-main.239 | null | null | null |
2021.emnlp-main.240 | Augmenting BERT-style Models with Predictive Coding to Improve Discourse-level Representations | https://aclanthology.org/2021.emnlp-main.240/ | [
"Vladimir Araujo",
"Andrés Villa",
"Marcelo Mendoza",
"Marie-Francine Moens",
"Alvaro Soto"
] | Current language models are usually trained using a self-supervised scheme, where the main focus is learning representations at the word or sentence level. However, there has been limited progress in generating useful discourse-level representations. In this work, we propose to use ideas from predictive coding theory t... | 2021.emnlp-main.240 | 10.18653/v1/2021.emnlp-main.240 | null | 2109.04602 | title_snapshot |
2021.emnlp-main.241 | Backdoor Attacks on Pre-trained Models by Layerwise Weight Poisoning | https://aclanthology.org/2021.emnlp-main.241/ | [
"Linyang Li",
"Demin Song",
"Xiaonan Li",
"Jiehang Zeng",
"Ruotian Ma",
"Xipeng Qiu"
] | Pre-Trained Models have been widely applied and recently proved vulnerable under backdoor attacks: the released pre-trained weights can be maliciously poisoned with certain triggers. When the triggers are activated, even the fine-tuned model will predict pre-defined labels, causing a security threat. These backdoors ge... | 2021.emnlp-main.241 | 10.18653/v1/2021.emnlp-main.241 | null | 2108.13888 | title_snapshot |
2021.emnlp-main.242 | GAML-BERT: Improving BERT Early Exiting by Gradient Aligned Mutual Learning | https://aclanthology.org/2021.emnlp-main.242/ | [
"Wei Zhu",
"Xiaoling Wang",
"Yuan Ni",
"Guotong Xie"
] | In this work, we propose a novel framework, Gradient Aligned Mutual Learning BERT (GAML-BERT), for improving the early exiting of BERT. GAML-BERT’s contributions are two-fold. We conduct a set of pilot experiments, which shows that mutual knowledge distillation between a shallow exit and a deep exit leads to better per... | 2021.emnlp-main.242 | 10.18653/v1/2021.emnlp-main.242 | null | null | null |
2021.emnlp-main.243 | The Power of Scale for Parameter-Efficient Prompt Tuning | https://aclanthology.org/2021.emnlp-main.243/ | [
"Brian Lester",
"Rami Al-Rfou",
"Noah Constant"
] | In this work, we explore “prompt tuning,” a simple yet effective mechanism for learning “soft prompts” to condition frozen language models to perform specific downstream tasks. Unlike the discrete text prompts used by GPT-3, soft prompts are learned through backpropagation and can be tuned to incorporate signals from a... | 2021.emnlp-main.243 | 10.18653/v1/2021.emnlp-main.243 | null | 2104.08691 | title_snapshot |
2021.emnlp-main.244 | Scalable Font Reconstruction with Dual Latent Manifolds | https://aclanthology.org/2021.emnlp-main.244/ | [
"Nikita Srivatsan",
"Si Wu",
"Jonathan Barron",
"Taylor Berg-Kirkpatrick"
] | We propose a deep generative model that performs typography analysis and font reconstruction by learning disentangled manifolds of both font style and character shape. Our approach enables us to massively scale up the number of character types we can effectively model compared to previous methods. Specifically, we infe... | 2021.emnlp-main.244 | 10.18653/v1/2021.emnlp-main.244 | null | 2109.06627 | title_snapshot |
2021.emnlp-main.245 | Neuro-Symbolic Approaches for Text-Based Policy Learning | https://aclanthology.org/2021.emnlp-main.245/ | [
"Subhajit Chaudhury",
"Prithviraj Sen",
"Masaki Ono",
"Daiki Kimura",
"Michiaki Tatsubori",
"Asim Munawar"
] | Text-Based Games (TBGs) have emerged as important testbeds for reinforcement learning (RL) in the natural language domain. Previous methods using LSTM-based action policies are uninterpretable and often overfit the training games showing poor performance to unseen test games. We present SymboLic Action policy for Textu... | 2021.emnlp-main.245 | 10.18653/v1/2021.emnlp-main.245 | null | null | null |
2021.emnlp-main.246 | Layer-wise Model Pruning based on Mutual Information | https://aclanthology.org/2021.emnlp-main.246/ | [
"Chun Fan",
"Jiwei Li",
"Tianwei Zhang",
"Xiang Ao",
"Fei Wu",
"Yuxian Meng",
"Xiaofei Sun"
] | Inspired by mutual information (MI) based feature selection in SVMs and logistic regression, in this paper, we propose MI-based layer-wise pruning: for each layer of a multi-layer neural network, neurons with higher values of MI with respect to preserved neurons in the upper layer are preserved. Starting from the top s... | 2021.emnlp-main.246 | 10.18653/v1/2021.emnlp-main.246 | null | 2108.12594 | title_snapshot |
2021.emnlp-main.247 | Hierarchical Heterogeneous Graph Representation Learning for Short Text Classification | https://aclanthology.org/2021.emnlp-main.247/ | [
"Yaqing Wang",
"Song Wang",
"Quanming Yao",
"Dejing Dou"
] | Short text classification is a fundamental task in natural language processing. It is hard due to the lack of context information and labeled data in practice. In this paper, we propose a new method called SHINE, which is based on graph neural network (GNN), for short text classification. First, we model the short text... | 2021.emnlp-main.247 | 10.18653/v1/2021.emnlp-main.247 | null | 2111.00180 | title_snapshot |
2021.emnlp-main.248 | kFolden: k-Fold Ensemble for Out-Of-Distribution Detection | https://aclanthology.org/2021.emnlp-main.248/ | [
"Xiaoya Li",
"Jiwei Li",
"Xiaofei Sun",
"Chun Fan",
"Tianwei Zhang",
"Fei Wu",
"Yuxian Meng",
"Jun Zhang"
] | Out-of-Distribution (OOD) detection is an important problem in natural language processing (NLP). In this work, we propose a simple yet effective framework kFolden, which mimics the behaviors of OOD detection during training without the use of any external data. For a task with k training labels, kFolden induces k sub-... | 2021.emnlp-main.248 | 10.18653/v1/2021.emnlp-main.248 | null | null | null |
2021.emnlp-main.249 | Frustratingly Simple Pretraining Alternatives to Masked Language Modeling | https://aclanthology.org/2021.emnlp-main.249/ | [
"Atsuki Yamaguchi",
"George Chrysostomou",
"Katerina Margatina",
"Nikolaos Aletras"
] | Masked language modeling (MLM), a self-supervised pretraining objective, is widely used in natural language processing for learning text representations. MLM trains a model to predict a random sample of input tokens that have been replaced by a [MASK] placeholder in a multi-class setting over the entire vocabulary. Whe... | 2021.emnlp-main.249 | 10.18653/v1/2021.emnlp-main.249 | null | 2109.01819 | title_snapshot |
2021.emnlp-main.250 | HRKD: Hierarchical Relational Knowledge Distillation for Cross-domain Language Model Compression | https://aclanthology.org/2021.emnlp-main.250/ | [
"Chenhe Dong",
"Yaliang Li",
"Ying Shen",
"Minghui Qiu"
] | On many natural language processing tasks, large pre-trained language models (PLMs) have shown overwhelming performances compared with traditional neural network methods. Nevertheless, their huge model size and low inference speed have hindered the deployment on resource-limited devices in practice. In this paper, we t... | 2021.emnlp-main.250 | 10.18653/v1/2021.emnlp-main.250 | null | 2110.08551 | title_snapshot |
2021.emnlp-main.251 | Searching for an Effective Defender: Benchmarking Defense against Adversarial Word Substitution | https://aclanthology.org/2021.emnlp-main.251/ | [
"Zongyi Li",
"Jianhan Xu",
"Jiehang Zeng",
"Linyang Li",
"Xiaoqing Zheng",
"Qi Zhang",
"Kai-Wei Chang",
"Cho-Jui Hsieh"
] | Recent studies have shown that deep neural network-based models are vulnerable to intentionally crafted adversarial examples, and various methods have been proposed to defend against adversarial word-substitution attacks for neural NLP models. However, there is a lack of systematic study on comparing different defense ... | 2021.emnlp-main.251 | 10.18653/v1/2021.emnlp-main.251 | null | 2108.12777 | title_snapshot |
2021.emnlp-main.252 | Re-embedding Difficult Samples via Mutual Information Constrained Semantically Oversampling for Imbalanced Text Classification | https://aclanthology.org/2021.emnlp-main.252/ | [
"Jiachen Tian",
"Shizhan Chen",
"Xiaowang Zhang",
"Zhiyong Feng",
"Deyi Xiong",
"Shaojuan Wu",
"Chunliu Dou"
] | Difficult samples of the minority class in imbalanced text classification are usually hard to be classified as they are embedded into an overlapping semantic region with the majority class. In this paper, we propose a Mutual Information constrained Semantically Oversampling framework (MISO) that can generate anchor ins... | 2021.emnlp-main.252 | 10.18653/v1/2021.emnlp-main.252 | null | null | null |
2021.emnlp-main.253 | Beyond Text: Incorporating Metadata and Label Structure for Multi-Label Document Classification using Heterogeneous Graphs | https://aclanthology.org/2021.emnlp-main.253/ | [
"Chenchen Ye",
"Linhai Zhang",
"Yulan He",
"Deyu Zhou",
"Jie Wu"
] | Multi-label document classification, associating one document instance with a set of relevant labels, is attracting more and more research attention. Existing methods explore the incorporation of information beyond text, such as document metadata or label structure. These approaches however either simply utilize the se... | 2021.emnlp-main.253 | 10.18653/v1/2021.emnlp-main.253 | null | null | null |
2021.emnlp-main.254 | Natural Language Processing Meets Quantum Physics: A Survey and Categorization | https://aclanthology.org/2021.emnlp-main.254/ | [
"Sixuan Wu",
"Jian Li",
"Peng Zhang",
"Yue Zhang"
] | Recent research has investigated quantum NLP, designing algorithms that process natural language in quantum computers, and also quantum-inspired algorithms that improve NLP performance on classical computers. In this survey, we review representative methods at the intersection of NLP and quantum physics in the past ten... | 2021.emnlp-main.254 | 10.18653/v1/2021.emnlp-main.254 | null | null | null |
2021.emnlp-main.255 | MetaTS: Meta Teacher-Student Network for Multilingual Sequence Labeling with Minimal Supervision | https://aclanthology.org/2021.emnlp-main.255/ | [
"Zheng Li",
"Danqing Zhang",
"Tianyu Cao",
"Ying Wei",
"Yiwei Song",
"Bing Yin"
] | Sequence labeling aims to predict a fine-grained sequence of labels for the text. However, such formulation hinders the effectiveness of supervised methods due to the lack of token-level annotated data. This is exacerbated when we meet a diverse range of languages. In this work, we explore multilingual sequence labelin... | 2021.emnlp-main.255 | 10.18653/v1/2021.emnlp-main.255 | null | null | null |
2021.emnlp-main.256 | Neural Machine Translation with Heterogeneous Topic Knowledge Embeddings | https://aclanthology.org/2021.emnlp-main.256/ | [
"Weixuan Wang",
"Wei Peng",
"Meng Zhang",
"Qun Liu"
] | Neural Machine Translation (NMT) has shown a strong ability to utilize local context to disambiguate the meaning of words. However, it remains a challenge for NMT to leverage broader context information like topics. In this paper, we propose heterogeneous ways of embedding topic information at the sentence level into a... | 2021.emnlp-main.256 | 10.18653/v1/2021.emnlp-main.256 | null | null | null |
2021.emnlp-main.257 | Allocating Large Vocabulary Capacity for Cross-Lingual Language Model Pre-Training | https://aclanthology.org/2021.emnlp-main.257/ | [
"Bo Zheng",
"Li Dong",
"Shaohan Huang",
"Saksham Singhal",
"Wanxiang Che",
"Ting Liu",
"Xia Song",
"Furu Wei"
] | Compared to monolingual models, cross-lingual models usually require a more expressive vocabulary to represent all languages adequately. We find that many languages are under-represented in recent cross-lingual language models due to the limited vocabulary capacity. To this end, we propose an algorithm VoCap to determi... | 2021.emnlp-main.257 | 10.18653/v1/2021.emnlp-main.257 | null | 2109.07306 | title_snapshot |
2021.emnlp-main.258 | Recurrent Attention for Neural Machine Translation | https://aclanthology.org/2021.emnlp-main.258/ | [
"Jiali Zeng",
"Shuangzhi Wu",
"Yongjing Yin",
"Yufan Jiang",
"Mu Li"
] | Recent research questions the importance of the dot-product self-attention in Transformer models and shows that most attention heads learn simple positional patterns. In this paper, we push further in this research line and propose a novel substitute mechanism for self-attention: Recurrent AtteNtion (RAN) . RAN directl... | 2021.emnlp-main.258 | 10.18653/v1/2021.emnlp-main.258 | null | null | null |
2021.emnlp-main.259 | Learning from Multiple Noisy Augmented Data Sets for Better Cross-Lingual Spoken Language Understanding | https://aclanthology.org/2021.emnlp-main.259/ | [
"Yingmei Guo",
"Linjun Shou",
"Jian Pei",
"Ming Gong",
"Mingxing Xu",
"Zhiyong Wu",
"Daxin Jiang"
] | Lack of training data presents a grand challenge to scaling out spoken language understanding (SLU) to low-resource languages. Although various data augmentation approaches have been proposed to synthesize training data in low-resource target languages, the augmented data sets are often noisy, and thus impede the perfo... | 2021.emnlp-main.259 | 10.18653/v1/2021.emnlp-main.259 | null | 2109.01583 | title_snapshot |
2021.emnlp-main.260 | Enlivening Redundant Heads in Multi-head Self-attention for Machine Translation | https://aclanthology.org/2021.emnlp-main.260/ | [
"Tianfu Zhang",
"Heyan Huang",
"Chong Feng",
"Longbing Cao"
] | Multi-head self-attention recently attracts enormous interest owing to its specialized functions, significant parallelizable computation, and flexible extensibility. However, very recent empirical studies show that some self-attention heads make little contribution and can be pruned as redundant heads. This work takes ... | 2021.emnlp-main.260 | 10.18653/v1/2021.emnlp-main.260 | null | null | null |
2021.emnlp-main.261 | Unsupervised Neural Machine Translation with Universal Grammar | https://aclanthology.org/2021.emnlp-main.261/ | [
"Zuchao Li",
"Masao Utiyama",
"Eiichiro Sumita",
"Hai Zhao"
] | Machine translation usually relies on parallel corpora to provide parallel signals for training. The advent of unsupervised machine translation has brought machine translation away from this reliance, though performance still lags behind traditional supervised machine translation. In unsupervised machine translation, t... | 2021.emnlp-main.261 | 10.18653/v1/2021.emnlp-main.261 | null | null | null |
2021.emnlp-main.262 | Encouraging Lexical Translation Consistency for Document-Level Neural Machine Translation | https://aclanthology.org/2021.emnlp-main.262/ | [
"Xinglin Lyu",
"Junhui Li",
"Zhengxian Gong",
"Min Zhang"
] | Recently a number of approaches have been proposed to improve translation performance for document-level neural machine translation (NMT). However, few are focusing on the subject of lexical translation consistency. In this paper we apply “one translation per discourse” in NMT, and aim to encourage lexical translation ... | 2021.emnlp-main.262 | 10.18653/v1/2021.emnlp-main.262 | null | null | null |
2021.emnlp-main.263 | Improving Neural Machine Translation by Bidirectional Training | https://aclanthology.org/2021.emnlp-main.263/ | [
"Liang Ding",
"Di Wu",
"Dacheng Tao"
] | We present a simple and effective pretraining strategy – bidirectional training (BiT) for neural machine translation. Specifically, we bidirectionally update the model parameters at the early stage and then tune the model normally. To achieve bidirectional updating, we simply reconstruct the training samples from “src\... | 2021.emnlp-main.263 | 10.18653/v1/2021.emnlp-main.263 | null | 2109.07780 | title_snapshot |
2021.emnlp-main.264 | Scheduled Sampling Based on Decoding Steps for Neural Machine Translation | https://aclanthology.org/2021.emnlp-main.264/ | [
"Yijin Liu",
"Fandong Meng",
"Yufeng Chen",
"Jinan Xu",
"Jie Zhou"
] | Scheduled sampling is widely used to mitigate the exposure bias problem for neural machine translation. Its core motivation is to simulate the inference scene during training by replacing ground-truth tokens with predicted tokens, thus bridging the gap between training and inference. However, vanilla scheduled sampling... | 2021.emnlp-main.264 | 10.18653/v1/2021.emnlp-main.264 | null | 2108.12963 | title_snapshot |
2021.emnlp-main.265 | Learning to Rewrite for Non-Autoregressive Neural Machine Translation | https://aclanthology.org/2021.emnlp-main.265/ | [
"Xinwei Geng",
"Xiaocheng Feng",
"Bing Qin"
] | Non-autoregressive neural machine translation, which decomposes the dependence on previous target tokens from the inputs of the decoder, has achieved impressive inference speedup but at the cost of inferior accuracy. Previous works employ iterative decoding to improve the translation by applying multiple refinement ite... | 2021.emnlp-main.265 | 10.18653/v1/2021.emnlp-main.265 | null | null | null |
2021.emnlp-main.266 | SHAPE: Shifted Absolute Position Embedding for Transformers | https://aclanthology.org/2021.emnlp-main.266/ | [
"Shun Kiyono",
"Sosuke Kobayashi",
"Jun Suzuki",
"Kentaro Inui"
] | Position representation is crucial for building position-aware representations in Transformers. Existing position representations suffer from a lack of generalization to test data with unseen lengths or high computational cost. We investigate shifted absolute position embedding (SHAPE) to address both issues. The basic... | 2021.emnlp-main.266 | 10.18653/v1/2021.emnlp-main.266 | null | 2109.05644 | title_snapshot |
2021.emnlp-main.267 | Self-Supervised Quality Estimation for Machine Translation | https://aclanthology.org/2021.emnlp-main.267/ | [
"Yuanhang Zheng",
"Zhixing Tan",
"Meng Zhang",
"Mieradilijiang Maimaiti",
"Huanbo Luan",
"Maosong Sun",
"Qun Liu",
"Yang Liu"
] | Quality estimation (QE) of machine translation (MT) aims to evaluate the quality of machine-translated sentences without references and is important in practical applications of MT. Training QE models require massive parallel data with hand-crafted quality annotations, which are time-consuming and labor-intensive to ob... | 2021.emnlp-main.267 | 10.18653/v1/2021.emnlp-main.267 | null | null | null |
2021.emnlp-main.268 | Generalised Unsupervised Domain Adaptation of Neural Machine Translation with Cross-Lingual Data Selection | https://aclanthology.org/2021.emnlp-main.268/ | [
"Thuy-Trang Vu",
"Xuanli He",
"Dinh Phung",
"Gholamreza Haffari"
] | This paper considers the unsupervised domain adaptation problem for neural machine translation (NMT), where we assume the access to only monolingual text in either the source or target language in the new domain. We propose a cross-lingual data selection method to extract in-domain sentences in the missing language sid... | 2021.emnlp-main.268 | 10.18653/v1/2021.emnlp-main.268 | null | 2109.04292 | title_snapshot |
2021.emnlp-main.269 | STANKER: Stacking Network based on Level-grained Attention-masked BERT for Rumor Detection on Social Media | https://aclanthology.org/2021.emnlp-main.269/ | [
"Dongning Rao",
"Xin Miao",
"Zhihua Jiang",
"Ran Li"
] | Rumor detection on social media puts pre-trained language models (LMs), such as BERT, and auxiliary features, such as comments, into use. However, on the one hand, rumor detection datasets in Chinese companies with comments are rare; on the other hand, intensive interaction of attention on Transformer-based models like... | 2021.emnlp-main.269 | 10.18653/v1/2021.emnlp-main.269 | null | null | null |
2021.emnlp-main.270 | ActiveEA: Active Learning for Neural Entity Alignment | https://aclanthology.org/2021.emnlp-main.270/ | [
"Bing Liu",
"Harrisen Scells",
"Guido Zuccon",
"Wen Hua",
"Genghong Zhao"
] | Entity Alignment (EA) aims to match equivalent entities across different Knowledge Graphs (KGs) and is an essential step of KG fusion. Current mainstream methods – neural EA models – rely on training with seed alignment, i.e., a set of pre-aligned entity pairs which are very costly to annotate. In this paper, we devise... | 2021.emnlp-main.270 | 10.18653/v1/2021.emnlp-main.270 | null | 2110.06474 | title_snapshot |
2021.emnlp-main.271 | Cost-effective End-to-end Information Extraction for Semi-structured Document Images | https://aclanthology.org/2021.emnlp-main.271/ | [
"Wonseok Hwang",
"Hyunji Lee",
"Jinyeong Yim",
"Geewook Kim",
"Minjoon Seo"
] | A real-world information extraction (IE) system for semi-structured document images often involves a long pipeline of multiple modules, whose complexity dramatically increases its development and maintenance cost. One can instead consider an end-to-end model that directly maps the input to the target output and simplif... | 2021.emnlp-main.271 | 10.18653/v1/2021.emnlp-main.271 | null | 2104.08041 | title_snapshot |
2021.emnlp-main.272 | Improving Math Word Problems with Pre-trained Knowledge and Hierarchical Reasoning | https://aclanthology.org/2021.emnlp-main.272/ | [
"Weijiang Yu",
"Yingpeng Wen",
"Fudan Zheng",
"Nong Xiao"
] | The recent algorithms for math word problems (MWP) neglect to use outside knowledge not present in the problems. Most of them only capture the word-level relationship and ignore to build hierarchical reasoning like the human being for mining the contextual structure between words and sentences. In this paper, we propos... | 2021.emnlp-main.272 | 10.18653/v1/2021.emnlp-main.272 | null | null | null |
2021.emnlp-main.273 | GraphMR: Graph Neural Network for Mathematical Reasoning | https://aclanthology.org/2021.emnlp-main.273/ | [
"Weijie Feng",
"Binbin Liu",
"Dongpeng Xu",
"Qilong Zheng",
"Yun Xu"
] | Mathematical reasoning aims to infer satisfiable solutions based on the given mathematics questions. Previous natural language processing researches have proven the effectiveness of sequence-to-sequence (Seq2Seq) or related variants on mathematics solving. However, few works have been able to explore structural or synt... | 2021.emnlp-main.273 | 10.18653/v1/2021.emnlp-main.273 | null | null | null |
2021.emnlp-main.274 | What Changes Can Large-scale Language Models Bring? Intensive Study on HyperCLOVA: Billions-scale Korean Generative Pretrained Transformers | https://aclanthology.org/2021.emnlp-main.274/ | [
"Boseop Kim",
"HyoungSeok Kim",
"Sang-Woo Lee",
"Gichang Lee",
"Donghyun Kwak",
"Jeon Dong Hyeon",
"Sunghyun Park",
"Sungju Kim",
"Seonhoon Kim",
"Dongpil Seo",
"Heungsub Lee",
"Minyoung Jeong",
"Sungjae Lee",
"Minsub Kim",
"Suk Hyun Ko",
"Seokhun Kim",
"Taeyong Park",
"Jinuk Kim",... | GPT-3 shows remarkable in-context learning ability of large-scale language models (LMs) trained on hundreds of billion scale data. Here we address some remaining issues less reported by the GPT-3 paper, such as a non-English LM, the performances of different sized models, and the effect of recently introduced prompt op... | 2021.emnlp-main.274 | 10.18653/v1/2021.emnlp-main.274 | null | 2109.04650 | title_snapshot |
2021.emnlp-main.275 | APIRecX: Cross-Library API Recommendation via Pre-Trained Language Model | https://aclanthology.org/2021.emnlp-main.275/ | [
"Yuning Kang",
"Zan Wang",
"Hongyu Zhang",
"Junjie Chen",
"Hanmo You"
] | For programmers, learning the usage of APIs (Application Programming Interfaces) of a software library is important yet difficult. API recommendation tools can help developers use APIs by recommending which APIs to be used next given the APIs that have been written. Traditionally, language models such as N-gram are app... | 2021.emnlp-main.275 | 10.18653/v1/2021.emnlp-main.275 | null | null | null |
2021.emnlp-main.276 | GMH: A General Multi-hop Reasoning Model for KG Completion | https://aclanthology.org/2021.emnlp-main.276/ | [
"Yao Zhang",
"Hongru Liang",
"Adam Jatowt",
"Wenqiang Lei",
"Xin Wei",
"Ning Jiang",
"Zhenglu Yang"
] | Knowledge graphs are essential for numerous downstream natural language processing applications, but are typically incomplete with many facts missing. This results in research efforts on multi-hop reasoning task, which can be formulated as a search process and current models typically perform short distance reasoning. ... | 2021.emnlp-main.276 | 10.18653/v1/2021.emnlp-main.276 | null | 2010.07620 | title_snapshot |
2021.emnlp-main.277 | BPM_MT: Enhanced Backchannel Prediction Model using Multi-Task Learning | https://aclanthology.org/2021.emnlp-main.277/ | [
"Jin Yea Jang",
"San Kim",
"Minyoung Jung",
"Saim Shin",
"Gahgene Gweon"
] | Backchannel (BC), a short reaction signal of a listener to a speaker’s utterances, helps to improve the quality of the conversation. Several studies have been conducted to predict BC in conversation; however, the utilization of advanced natural language processing techniques using lexical information presented in the u... | 2021.emnlp-main.277 | 10.18653/v1/2021.emnlp-main.277 | null | null | null |
2021.emnlp-main.278 | Graphine: A Dataset for Graph-aware Terminology Definition Generation | https://aclanthology.org/2021.emnlp-main.278/ | [
"Zequn Liu",
"Shukai Wang",
"Yiyang Gu",
"Ruiyi Zhang",
"Ming Zhang",
"Sheng Wang"
] | Precisely defining the terminology is the first step in scientific communication. Developing neural text generation models for definition generation can circumvent the labor-intensity curation, further accelerating scientific discovery. Unfortunately, the lack of large-scale terminology definition dataset hinders the p... | 2021.emnlp-main.278 | 10.18653/v1/2021.emnlp-main.278 | null | 2109.04018 | title_snapshot |
2021.emnlp-main.279 | Leveraging Order-Free Tag Relations for Context-Aware Recommendation | https://aclanthology.org/2021.emnlp-main.279/ | [
"Junmo Kang",
"Jeonghwan Kim",
"Suwon Shin",
"Sung-Hyon Myaeng"
] | Tag recommendation relies on either a ranking function for top-k tags or an autoregressive generation method. However, the previous methods neglect one of two seemingly conflicting yet desirable characteristics of a tag set: orderlessness and inter-dependency. While the ranking approach fails to address the inter-depen... | 2021.emnlp-main.279 | 10.18653/v1/2021.emnlp-main.279 | null | 2012.02957 | title_snapshot |
2021.emnlp-main.280 | End-to-End Conversational Search for Online Shopping with Utterance Transfer | https://aclanthology.org/2021.emnlp-main.280/ | [
"Liqiang Xiao",
"Jun Ma",
"Xin Luna Dong",
"Pascual Martínez-Gómez",
"Nasser Zalmout",
"Chenwei Zhang",
"Tong Zhao",
"Hao He",
"Yaohui Jin"
] | Successful conversational search systems can present natural, adaptive and interactive shopping experience for online shopping customers. However, building such systems from scratch faces real word challenges from both imperfect product schema/knowledge and lack of training dialog data. In this work we first propose Co... | 2021.emnlp-main.280 | 10.18653/v1/2021.emnlp-main.280 | null | 2109.05460 | title_snapshot |
2021.emnlp-main.281 | Self-Supervised Curriculum Learning for Spelling Error Correction | https://aclanthology.org/2021.emnlp-main.281/ | [
"Zifa Gan",
"Hongfei Xu",
"Hongying Zan"
] | Spelling Error Correction (SEC) that requires high-level language understanding is a challenging but useful task. Current SEC approaches normally leverage a pre-training then fine-tuning procedure that treats data equally. By contrast, Curriculum Learning (CL) utilizes training data differently during training and has ... | 2021.emnlp-main.281 | 10.18653/v1/2021.emnlp-main.281 | null | null | null |
2021.emnlp-main.282 | Fix-Filter-Fix: Intuitively Connect Any Models for Effective Bug Fixing | https://aclanthology.org/2021.emnlp-main.282/ | [
"Haiwen Hong",
"Jingfeng Zhang",
"Yin Zhang",
"Yao Wan",
"Yulei Sui"
] | Locating and fixing bugs is a time-consuming task. Most neural machine translation (NMT) based approaches for automatically bug fixing lack generality and do not make full use of the rich information in the source code. In NMT-based bug fixing, we find some predicted code identical to the input buggy code (called uncha... | 2021.emnlp-main.282 | 10.18653/v1/2021.emnlp-main.282 | null | null | null |
2021.emnlp-main.283 | Neuro-Symbolic Reinforcement Learning with First-Order Logic | https://aclanthology.org/2021.emnlp-main.283/ | [
"Daiki Kimura",
"Masaki Ono",
"Subhajit Chaudhury",
"Ryosuke Kohita",
"Akifumi Wachi",
"Don Joven Agravante",
"Michiaki Tatsubori",
"Asim Munawar",
"Alexander Gray"
] | Deep reinforcement learning (RL) methods often require many trials before convergence, and no direct interpretability of trained policies is provided. In order to achieve fast convergence and interpretability for the policy in RL, we propose a novel RL method for text-based games with a recent neuro-symbolic framework ... | 2021.emnlp-main.283 | 10.18653/v1/2021.emnlp-main.283 | null | 2110.10963 | title_snapshot |
2021.emnlp-main.284 | Biomedical Concept Normalization by Leveraging Hypernyms | https://aclanthology.org/2021.emnlp-main.284/ | [
"Cheng Yan",
"Yuanzhe Zhang",
"Kang Liu",
"Jun Zhao",
"Yafei Shi",
"Shengping Liu"
] | Biomedical Concept Normalization (BCN) is widely used in biomedical text processing as a fundamental module. Owing to numerous surface variants of biomedical concepts, BCN still remains challenging and unsolved. In this paper, we exploit biomedical concept hypernyms to facilitate BCN. We propose Biomedical Concept Norm... | 2021.emnlp-main.284 | 10.18653/v1/2021.emnlp-main.284 | null | null | null |
2021.emnlp-main.285 | Leveraging Capsule Routing to Associate Knowledge with Medical Literature Hierarchically | https://aclanthology.org/2021.emnlp-main.285/ | [
"Xin Liu",
"Qingcai Chen",
"Junying Chen",
"Wenxiu Zhou",
"Tingyu Liu",
"Xinlan Yang",
"Weihua Peng"
] | Integrating knowledge into text is a promising way to enrich text representation, especially in the medical field. However, undifferentiated knowledge not only confuses the text representation but also imports unexpected noises. In this paper, to alleviate this problem, we propose leveraging capsule routing to associat... | 2021.emnlp-main.285 | 10.18653/v1/2021.emnlp-main.285 | null | null | null |
2021.emnlp-main.286 | Label-Enhanced Hierarchical Contextualized Representation for Sequential Metaphor Identification | https://aclanthology.org/2021.emnlp-main.286/ | [
"Shuqun Li",
"Liang Yang",
"Weidong He",
"Shiqi Zhang",
"Jingjie Zeng",
"Hongfei Lin"
] | Recent metaphor identification approaches mainly consider the contextual text features within a sentence or introduce external linguistic features to the model. But they usually ignore the extra information that the data can provide, such as the contextual metaphor information and broader discourse information. In this... | 2021.emnlp-main.286 | 10.18653/v1/2021.emnlp-main.286 | null | null | null |
2021.emnlp-main.287 | SpellBERT: A Lightweight Pretrained Model for Chinese Spelling Check | https://aclanthology.org/2021.emnlp-main.287/ | [
"Tuo Ji",
"Hang Yan",
"Xipeng Qiu"
] | Chinese Spelling Check (CSC) is to detect and correct Chinese spelling errors. Many models utilize a predefined confusion set to learn a mapping between correct characters and its visually similar or phonetically similar misuses but the mapping may be out-of-domain. To that end, we propose SpellBERT, a pretrained model... | 2021.emnlp-main.287 | 10.18653/v1/2021.emnlp-main.287 | null | null | null |
2021.emnlp-main.288 | Automated Generation of Accurate & Fluent Medical X-ray Reports | https://aclanthology.org/2021.emnlp-main.288/ | [
"Hoang Nguyen",
"Dong Nie",
"Taivanbat Badamdorj",
"Yujie Liu",
"Yingying Zhu",
"Jason Truong",
"Li Cheng"
] | Our paper aims to automate the generation of medical reports from chest X-ray image inputs, a critical yet time-consuming task for radiologists. Existing medical report generation efforts emphasize producing human-readable reports, yet the generated text may not be well aligned to the clinical facts. Our generated medi... | 2021.emnlp-main.288 | 10.18653/v1/2021.emnlp-main.288 | null | 2108.12126 | title_snapshot |
2021.emnlp-main.289 | Enhancing Document Ranking with Task-adaptive Training and Segmented Token Recovery Mechanism | https://aclanthology.org/2021.emnlp-main.289/ | [
"Xingwu Sun",
"Yanling Cui",
"Hongyin Tang",
"Fuzheng Zhang",
"Beihong Jin",
"Shi Wang"
] | In this paper, we propose a new ranking model DR-BERT, which improves the Document Retrieval (DR) task by a task-adaptive training process and a Segmented Token Recovery Mechanism (STRM). In the task-adaptive training, we first pre-train DR-BERT to be domain-adaptive and then make the two-phase fine-tuning. In the firs... | 2021.emnlp-main.289 | 10.18653/v1/2021.emnlp-main.289 | null | null | null |
2021.emnlp-main.290 | Abstract, Rationale, Stance: A Joint Model for Scientific Claim Verification | https://aclanthology.org/2021.emnlp-main.290/ | [
"Zhiwei Zhang",
"Jiyi Li",
"Fumiyo Fukumoto",
"Yanming Ye"
] | Scientific claim verification can help the researchers to easily find the target scientific papers with the sentence evidence from a large corpus for the given claim. Some existing works propose pipeline models on the three tasks of abstract retrieval, rationale selection and stance prediction. Such works have the prob... | 2021.emnlp-main.290 | 10.18653/v1/2021.emnlp-main.290 | null | 2110.15116 | title_snapshot |
2021.emnlp-main.291 | A Fine-Grained Domain Adaption Model for Joint Word Segmentation and POS Tagging | https://aclanthology.org/2021.emnlp-main.291/ | [
"Peijie Jiang",
"Dingkun Long",
"Yueheng Sun",
"Meishan Zhang",
"Guangwei Xu",
"Pengjun Xie"
] | Domain adaption for word segmentation and POS tagging is a challenging problem for Chinese lexical processing. Self-training is one promising solution for it, which struggles to construct a set of high-quality pseudo training instances for the target domain. Previous work usually assumes a universal source-to-target ad... | 2021.emnlp-main.291 | 10.18653/v1/2021.emnlp-main.291 | null | null | null |
2021.emnlp-main.292 | Answering Open-Domain Questions of Varying Reasoning Steps from Text | https://aclanthology.org/2021.emnlp-main.292/ | [
"Peng Qi",
"Haejun Lee",
"Tg Sido",
"Christopher Manning"
] | We develop a unified system to answer directly from text open-domain questions that may require a varying number of retrieval steps. We employ a single multi-task transformer model to perform all the necessary subtasks—retrieving supporting facts, reranking them, and predicting the answer from all retrieved documents—i... | 2021.emnlp-main.292 | 10.18653/v1/2021.emnlp-main.292 | null | 2010.12527 | title_snapshot |
2021.emnlp-main.293 | Adaptive Information Seeking for Open-Domain Question Answering | https://aclanthology.org/2021.emnlp-main.293/ | [
"Yunchang Zhu",
"Liang Pang",
"Yanyan Lan",
"Huawei Shen",
"Xueqi Cheng"
] | Information seeking is an essential step for open-domain question answering to efficiently gather evidence from a large corpus. Recently, iterative approaches have been proven to be effective for complex questions, by recursively retrieving new evidence at each step. However, almost all existing iterative approaches us... | 2021.emnlp-main.293 | 10.18653/v1/2021.emnlp-main.293 | null | 2109.06747 | title_snapshot |
2021.emnlp-main.294 | Mapping probability word problems to executable representations | https://aclanthology.org/2021.emnlp-main.294/ | [
"Simon Suster",
"Pieter Fivez",
"Pietro Totis",
"Angelika Kimmig",
"Jesse Davis",
"Luc de Raedt",
"Walter Daelemans"
] | While solving math word problems automatically has received considerable attention in the NLP community, few works have addressed probability word problems specifically. In this paper, we employ and analyse various neural models for answering such word problems. In a two-step approach, the problem text is first mapped ... | 2021.emnlp-main.294 | 10.18653/v1/2021.emnlp-main.294 | null | null | null |
2021.emnlp-main.295 | Enhancing Multiple-choice Machine Reading Comprehension by Punishing Illogical Interpretations | https://aclanthology.org/2021.emnlp-main.295/ | [
"Yiming Ju",
"Yuanzhe Zhang",
"Zhixing Tian",
"Kang Liu",
"Xiaohuan Cao",
"Wenting Zhao",
"Jinlong Li",
"Jun Zhao"
] | Machine Reading Comprehension (MRC), which requires a machine to answer questions given the relevant documents, is an important way to test machines’ ability to understand human language. Multiple-choice MRC is one of the most studied tasks in MRC due to the convenience of evaluation and the flexibility of answer forma... | 2021.emnlp-main.295 | 10.18653/v1/2021.emnlp-main.295 | null | null | null |
2021.emnlp-main.296 | Large-Scale Relation Learning for Question Answering over Knowledge Bases with Pre-trained Language Models | https://aclanthology.org/2021.emnlp-main.296/ | [
"Yuanmeng Yan",
"Rumei Li",
"Sirui Wang",
"Hongzhi Zhang",
"Zan Daoguang",
"Fuzheng Zhang",
"Wei Wu",
"Weiran Xu"
] | The key challenge of question answering over knowledge bases (KBQA) is the inconsistency between the natural language questions and the reasoning paths in the knowledge base (KB). Recent graph-based KBQA methods are good at grasping the topological structure of the graph but often ignore the textual information carried... | 2021.emnlp-main.296 | 10.18653/v1/2021.emnlp-main.296 | null | null | null |
2021.emnlp-main.297 | Phrase Retrieval Learns Passage Retrieval, Too | https://aclanthology.org/2021.emnlp-main.297/ | [
"Jinhyuk Lee",
"Alexander Wettig",
"Danqi Chen"
] | Dense retrieval methods have shown great promise over sparse retrieval methods in a range of NLP problems. Among them, dense phrase retrieval—the most fine-grained retrieval unit—is appealing because phrases can be directly used as the output for question answering and slot filling tasks. In this work, we follow the in... | 2021.emnlp-main.297 | 10.18653/v1/2021.emnlp-main.297 | null | 2109.08133 | title_snapshot |
2021.emnlp-main.298 | Neural Natural Logic Inference for Interpretable Question Answering | https://aclanthology.org/2021.emnlp-main.298/ | [
"Jihao Shi",
"Xiao Ding",
"Li Du",
"Ting Liu",
"Bing Qin"
] | Many open-domain question answering problems can be cast as a textual entailment task, where a question and candidate answers are concatenated to form hypotheses. A QA system then determines if the supporting knowledge bases, regarded as potential premises, entail the hypotheses. In this paper, we investigate a neural-... | 2021.emnlp-main.298 | 10.18653/v1/2021.emnlp-main.298 | null | null | null |
2021.emnlp-main.299 | Smoothing Dialogue States for Open Conversational Machine Reading | https://aclanthology.org/2021.emnlp-main.299/ | [
"Zhuosheng Zhang",
"Siru Ouyang",
"Hai Zhao",
"Masao Utiyama",
"Eiichiro Sumita"
] | Conversational machine reading (CMR) requires machines to communicate with humans through multi-turn interactions between two salient dialogue states of decision making and question generation processes. In open CMR settings, as the more realistic scenario, the retrieved background knowledge would be noisy, which resul... | 2021.emnlp-main.299 | 10.18653/v1/2021.emnlp-main.299 | null | 2108.12599 | title_snapshot |
2021.emnlp-main.300 | FinQA: A Dataset of Numerical Reasoning over Financial Data | https://aclanthology.org/2021.emnlp-main.300/ | [
"Zhiyu Chen",
"Wenhu Chen",
"Charese Smiley",
"Sameena Shah",
"Iana Borova",
"Dylan Langdon",
"Reema Moussa",
"Matt Beane",
"Ting-Hao Huang",
"Bryan Routledge",
"William Yang Wang"
] | The sheer volume of financial statements makes it difficult for humans to access and analyze a business’s financials. Robust numerical reasoning likewise faces unique challenges in this domain. In this work, we focus on answering deep questions over financial data, aiming to automate the analysis of a large corpus of f... | 2021.emnlp-main.300 | 10.18653/v1/2021.emnlp-main.300 | null | 2109.00122 | title_snapshot |
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