pwc-archive/papers-with-abstracts huggingface dataset:
Using a Penalty-based Loss Re-estimation Method to Improve Implicit Discourse Relation Classification
Authors: Xiao Li, Yu Hong, Huibin Ruan, Zhen Huang
Date: 2020-12-01 00:00:00
Proceeding: COLING 2020 8
Tasks: Implicit Discourse Relation Classification, Relation, Relation Classification

We tackle implicit discourse relation classification, a task of automatically determining semantic relationships between arguments. The attention-worthy words in arguments are crucial clues for classifying the discourse relations. Attention mechanisms have been proven effective in highlighting the attention-worthy words during encoding. However, our survey shows that some inessential words are unintentionally misjudged as the attention-worthy words and, therefore, assigned heavier attention weights than should be. We propose a penalty-based loss re-estimation method to regulate the attention learning process, integrating penalty coefficients into the computation of loss by means of overstability of attention weight distributions. We conduct experiments on the Penn Discourse TreeBank (PDTB) corpus. The test results show that our loss re-estimation method leads to substantial improvements for a variety of attention mechanisms, and it obtains highly competitive performance compared to the state-of-the-art methods.
A Review of Dataset and Labeling Methods for Causality Extraction
Authors: Jinghang Xu, Wanli Zuo, Shining Liang, Xianglin Zuo
Date: 2020-12-01 00:00:00
Proceeding: COLING 2020 8

Causality represents the most important kind of correlation between events. Extracting causali-ty from text has become a promising hot topic in NLP. However, there is no mature research systems and datasets for public evaluation. Moreover, there is a lack of unified causal sequence label methods, which constitute the key factors that hinder the progress of causality extraction research. We survey the limitations and shortcomings of existing causality research field com-prehensively from the aspects of basic concepts, extraction methods, experimental data, and la-bel methods, so as to provide reference for future research on causality extraction. We summa-rize the existing causality datasets, explore their practicability and extensibility from multiple perspectives and create a new causal dataset ESC. Aiming at the problem of causal sequence labeling, we analyse the existing methods with a summarization of its regulation and propose a new causal label method of core word. Multiple candidate causal label sequences are put for-ward according to label controversy to explore the optimal label method through experiments, and suggestions are provided for selecting label method.
Graph Enhanced Dual Attention Network for Document-Level Relation Extraction
Authors: Bo Li, Wei Ye, Zhonghao Sheng, Rui Xie, Xiangyu Xi, Shikun Zhang
Date: 2020-12-01 00:00:00
Proceeding: COLING 2020 8
Tasks: Document-level Relation Extraction, Relation, Relation Extraction, Sentence

Document-level relation extraction requires inter-sentence reasoning capabilities to capture local and global contextual information for multiple relational facts. To improve inter-sentence reasoning, we propose to characterize the complex interaction between sentences and potential relation instances via a Graph Enhanced Dual Attention network (GEDA). In GEDA, sentence representation generated by the sentence-to-relation (S2R) attention is refined and synthesized by a Heterogeneous Graph Convolutional Network before being fed into the relation-to-sentence (R2S) attention . We further design a simple yet effective regularizer based on the natural duality of the S2R and R2S attention, whose weights are also supervised by the supporting evidence of relation instances during training. An extensive set of experiments on an existing large-scale dataset show that our model achieve competitive performance, especially for the inter-sentence relation extraction, while the neural predictions can also be interpretable and easily observed.
Joint Entity and Relation Extraction for Legal Documents with Legal Feature Enhancement
Authors: Yanguang Chen, Yuanyuan Sun, Zhihao Yang, Hongfei Lin
Date: 2020-12-01 00:00:00
Proceeding: COLING 2020 8
Tasks: Joint Entity and Relation Extraction, Multi-Task Learning, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), Relation, Relation Extraction, Triplet

In recent years, the plentiful information contained in Chinese legal documents has attracted a great deal of attention because of the large-scale release of the judgment documents on China Judgments Online. It is in great need of enabling machines to understand the semantic information stored in the documents which are transcribed in the form of natural language. The technique of information extraction provides a way of mining the valuable information implied in the unstructured judgment documents. We propose a Legal Triplet Extraction System for drug-related criminal judgment documents. The system extracts the entities and the semantic relations jointly and benefits from the proposed legal lexicon feature and multi-task learning framework. Furthermore, we manually annotate a dataset for Named Entity Recognition and Relation Extraction in Chinese legal domain, which contributes to training supervised triplet extraction models and evaluating the model performance. Our experimental results show that the legal feature introduction and multi-task learning framework are feasible and effective for the Legal Triplet Extraction System. The F1 score of triplet extraction finally reaches 0.836 on the legal dataset.
Meta-Information Guided Meta-Learning for Few-Shot Relation Classification
Authors: Bowen Dong, Yuan YAO, Ruobing Xie, Tianyu Gao, Xu Han, Zhiyuan Liu, Fen Lin, Leyu Lin, Maosong Sun
Date: 2020-12-01 00:00:00
Proceeding: COLING 2020 8
Tasks: Classification, Few-Shot Relation Classification, Meta-Learning, Relation, Relation Classification

Few-shot classification requires classifiers to adapt to new classes with only a few training instances. State-of-the-art meta-learning approaches such as MAML learn how to initialize and fast adapt parameters from limited instances, which have shown promising results in few-shot classification. However, existing meta-learning models solely rely on implicit instance-based statistics, and thus suffer from instance unreliability and weak interpretability. To solve this problem, we propose a novel meta-information guided meta-learning (MIML) framework, where semantic concepts of classes provide strong guidance for meta-learning in both initialization and adaptation. In effect, our model can establish connections between instance-based information and semantic-based information, which enables more effective initialization and faster adaptation. Comprehensive experimental results on few-shot relation classification demonstrate the effectiveness of the proposed framework. Notably, MIML achieves comparable or superior performance to humans with only one shot on FewRel evaluation.
Unsupervised Deep Language and Dialect Identification for Short Texts
Authors: Koustava Goswami, Rajdeep Sarkar, Bharathi Raja Chakravarthi, Theodorus Fransen, John P. McCrae
Date: 2020-12-01 00:00:00
Proceeding: COLING 2020 8
Tasks: Dialect Identification, Language Identification, Sentence, Sentence Embeddings

Automatic Language Identification (LI) or Dialect Identification (DI) of short texts of closely related languages or dialects, is one of the primary steps in many natural language processing pipelines. Language identification is considered a solved task in many cases; however, in the case of very closely related languages, or in an unsupervised scenario (where the languages are not known in advance), performance is still poor. In this paper, we propose the Unsupervised Deep Language and Dialect Identification (UDLDI) method, which can simultaneously learn sentence embeddings and cluster assignments from short texts. The UDLDI model understands the sentence constructions of languages by applying attention to character relations which helps to optimize the clustering of languages. We have performed our experiments on three short-text datasets for different language families, each consisting of closely related languages or dialects, with very minimal training sets. Our experimental evaluations on these datasets have shown significant improvement over state-of-the-art unsupervised methods and our model has outperformed state-of-the-art LI and DI systems in supervised settings.
A Two-phase Prototypical Network Model for Incremental Few-shot Relation Classification
Authors: Haopeng Ren, Yi Cai, Xiaofeng Chen, Guohua Wang, Qing Li
Date: 2020-12-01 00:00:00
Proceeding: COLING 2020 8
Tasks: Few-Shot Learning, Few-Shot Relation Classification, Lifelong learning, Relation, Relation Classification, Triplet

Relation Classification (RC) plays an important role in natural language processing (NLP). Current conventional supervised and distantly supervised RC models always make a closed-world assumption which ignores the emergence of novel relations in open environment. To incrementally recognize the novel relations, current two solutions (i.e, re-training and lifelong learning) are designed but suffer from the lack of large-scale labeled data for novel relations. Meanwhile, prototypical network enjoys better performance on both fields of deep supervised learning and few-shot learning. However, it still suffers from the incompatible feature embedding problem when the novel relations come in. Motivated by them, we propose a two-phase prototypical network with prototype attention alignment and triplet loss to dynamically recognize the novel relations with a few support instances meanwhile without catastrophic forgetting. Extensive experiments are conducted to evaluate the effectiveness of our proposed model.
Document-level Relation Extraction with Dual-tier Heterogeneous Graph
Authors: Zhenyu Zhang, Bowen Yu, Xiaobo Shu, Tingwen Liu, Hengzhu Tang, Wang Yubin, Li Guo
Date: 2020-12-01 00:00:00
Proceeding: COLING 2020 8
Tasks: Decision Making, Document-level Relation Extraction, Relation, Relation Extraction, Sentence

Document-level relation extraction (RE) poses new challenges over its sentence-level counterpart since it requires an adequate comprehension of the whole document and the multi-hop reasoning ability across multiple sentences to reach the final result. In this paper, we propose a novel graph-based model with Dual-tier Heterogeneous Graph (DHG) for document-level RE. In particular, DHG is composed of a structure modeling layer followed by a relation reasoning layer. The major advantage is that it is capable of not only capturing both the sequential and structural information of documents but also mixing them together to benefit for multi-hop reasoning and final decision-making. Furthermore, we employ Graph Neural Networks (GNNs) based message propagation strategy to accumulate information on DHG. Experimental results demonstrate that the proposed method achieves state-of-the-art performance on two widely used datasets, and further analyses suggest that all the modules in our model are indispensable for document-level RE.
ToHRE: A Top-Down Classification Strategy with Hierarchical Bag Representation for Distantly Supervised Relation Extraction
Authors: Erxin Yu, Wenjuan Han, Yuan Tian, Yi Chang
Date: 2020-12-01 00:00:00
Proceeding: COLING 2020 8
Tasks: Classification, Relation, Relation Extraction

Distantly Supervised Relation Extraction (DSRE) has proven to be effective to find relational facts from texts, but it still suffers from two main problems: the wrong labeling problem and the long-tail problem. Most of the existing approaches address these two problems through flat classification, which lacks hierarchical information of relations. To leverage the informative relation hierarchies, we formulate DSRE as a hierarchical classification task and propose a novel hierarchical classification framework, which extracts the relation in a top-down manner. Specifically, in our proposed framework, 1) we use a hierarchically-refined representation method to achieve hierarchy-specific representation; 2) a top-down classification strategy is introduced instead of training a set of local classifiers. The experiments on NYT dataset demonstrate that our approach significantly outperforms other state-of-the-art approaches, especially for the long-tail problem.
Unsupervised Fact Checking by Counter-Weighted Positive and Negative Evidential Paths in A Knowledge Graph
Authors: Jiseong Kim, Key-Sun Choi
Date: 2020-12-01 00:00:00
Proceeding: COLING 2020 8
Tasks: Fact Checking, Knowledge Graphs, Misinformation

Misinformation spreads across media, community, and knowledge graphs in the Web by not only human agents but also information extraction algorithms that extract factual statements from unstructured textual data to populate the existing knowledge graphs. Traditional fact checking by experts or crowds is increasingly difficult to keep pace with the volume of newly created misinformation in the Web. Therefore, it is important and necessary to enhance the computational ability to determine whether a given factual statement is truthful or not. We view this problem as a truth scoring task in a knowledge graph. We present a novel rule-based approach that finds positive and negative evidential paths in a knowledge graph for a given factual statement and calculates a truth score for the given statement by unsupervised ensemble of the found positive and negative evidential paths. For example, we can determine the factual statement {``}United States is the birth place of Barack Obama{''} as truthful if there is the positive evidential path (Barack Obama, birthPlace, Hawaii) ∧ (Hawaii, country, United States) in a knowledge graph. For another example, we can determine the factual statement {``}Canada is the nationality of Barack Obama{''} as untruthful if there is the negative evidential path (Barack Obama, nationality, United States) ∧ (United States, {\mbox{$\neq$}}, Canada) in a knowledge graph. For evaluating on a real-world situation, we constructed an evaluation dataset by labeling truth or untruth label on factual statements that were extracted from Wikipedia texts by using the state-of-the-art BERT-based information extraction system. Our evaluation results show that our approach outperforms the state-of-the-art unsupervised approaches significantly by up to 0.12 AUC-ROC and even outperforms the supervised approach by up to 0.05 AUC-ROC not only in our dataset but also in the two different standard datasets.
Improving Relation Extraction with Relational Paraphrase Sentences
Authors: Junjie Yu, Tong Zhu, Wenliang Chen, Wei zhang, Min Zhang
Date: 2020-12-01 00:00:00
Proceeding: COLING 2020 8
Tasks: Relation, Relation Extraction

Supervised models for Relation Extraction (RE) typically require human-annotated training data. Due to the limited size, the human-annotated data is usually incapable of covering diverse relation expressions, which could limit the performance of RE. To increase the coverage of relation expressions, we may enlarge the labeled data by hiring annotators or applying Distant Supervision (DS). However, the human-annotated data is costly and non-scalable while the distantly supervised data contains many noises. In this paper, we propose an alternative approach to improve RE systems via enriching diverse expressions by relational paraphrase sentences. Based on an existing labeled data, we first automatically build a task-specific paraphrase data. Then, we propose a novel model to learn the information of diverse relation expressions. In our model, we try to capture this information on the paraphrases via a joint learning framework. Finally, we conduct experiments on a widely used dataset and the experimental results show that our approach is effective to improve the performance on relation extraction, even compared with a strong baseline.
Unequal Representations: Analyzing Intersectional Biases in Word Embeddings Using Representational Similarity Analysis
Authors: Michael Lepori
Date: 2020-12-01 00:00:00
Proceeding: COLING 2020 8
Tasks: Word Embeddings

We present a new approach for detecting human-like social biases in word embeddings using representational similarity analysis. Specifically, we probe contextualized and non-contextualized embeddings for evidence of intersectional biases against Black women. We show that these embeddings represent Black women as simultaneously less feminine than White women, and less Black than Black men. This finding aligns with intersectionality theory, which argues that multiple identity categories (such as race or sex) layer on top of each other in order to create unique modes of discrimination that are not shared by any individual category.
Multi-Task Learning for Knowledge Graph Completion with Pre-trained Language Models
Authors: Bosung Kim, Taesuk Hong, Youngjoong Ko, Jungyun Seo
Date: 2020-12-01 00:00:00
Proceeding: COLING 2020 8
Tasks: Knowledge Graph Completion, Knowledge Graphs, Language Modeling, Language Modelling, Link Prediction, Multi-Task Learning, Relation Prediction

As research on utilizing human knowledge in natural language processing has attracted considerable attention in recent years, knowledge graph (KG) completion has come into the spotlight. Recently, a new knowledge graph completion method using a pre-trained language model, such as KG-BERT, is presented and showed high performance. However, its scores in ranking metrics such as Hits@k are still behind state-of-the-art models. We claim that there are two main reasons: 1) failure in sufficiently learning relational information in knowledge graphs, and 2) difficulty in picking out the correct answer from lexically similar candidates. In this paper, we propose an effective multi-task learning method to overcome the limitations of previous works. By combining relation prediction and relevance ranking tasks with our target link prediction, the proposed model can learn more relational properties in KGs and properly perform even when lexical similarity occurs. Experimental results show that we not only largely improve the ranking performances compared to KG-BERT but also achieve the state-of-the-art performances in Mean Rank and Hits@10 on the WN18RR dataset.
Try to Substitute: An Unsupervised Chinese Word Sense Disambiguation Method Based on HowNet
Authors: Bairu Hou, Fanchao Qi, Yuan Zang, Xurui Zhang, Zhiyuan Liu, Maosong Sun
Date: 2020-12-01 00:00:00
Proceeding: COLING 2020 8
Tasks: Language Modeling, Language Modelling, Word Sense Disambiguation

Word sense disambiguation (WSD) is a fundamental natural language processing task. Unsupervised knowledge-based WSD only relies on a lexical knowledge base as the sense inventory and has wider practical use than supervised WSD that requires a mass of sense-annotated data. HowNet is the most widely used lexical knowledge base in Chinese WSD. Because of its uniqueness, however, most of existing unsupervised WSD methods cannot work for HowNet-based WSD, and the tailor-made methods have not obtained satisfying results. In this paper, we propose a new unsupervised method for HowNet-based Chinese WSD, which exploits the masked language model task of pre-trained language models. In experiments, considering existing evaluation dataset is small and out-of-date, we build a new and larger HowNet-based WSD dataset. Experimental results demonstrate that our model achieves significantly better performance than all the baseline methods. All the code and data of this paper are available at https://github.com/thunlp/SememeWSD.
Syntactically Aware Cross-Domain Aspect and Opinion Terms Extraction
Authors: Oren Pereg, Daniel Korat, Moshe Wasserblat
Date: 2020-12-01 00:00:00
Proceeding: COLING 2020 8
Tasks: Domain Adaptation, Sentiment Analysis, Unsupervised Domain Adaptation

A fundamental task of fine-grained sentiment analysis is aspect and opinion terms extraction. Supervised-learning approaches have shown good results for this task; however, they fail to scale across domains where labeled data is lacking. Non pre-trained unsupervised domain adaptation methods that incorporate external linguistic knowledge have proven effective in transferring aspect and opinion knowledge from a labeled source domain to un-labeled target domains; however, pre-trained transformer-based models like BERT and RoBERTa already exhibit substantial syntactic knowledge. In this paper, we propose a method for incorporating external linguistic information into a self-attention mechanism coupled with the BERT model. This enables leveraging the intrinsic knowledge existing within BERT together with externally introduced syntactic information, to bridge the gap across domains. We successfully demonstrate enhanced results on three benchmark datasets.
A Deep Generative Approach to Native Language Identification
Authors: Ehsan Lotfi, Ilia Markov, Walter Daelemans
Date: 2020-12-01 00:00:00
Proceeding: COLING 2020 8
Tasks: BIG-bench Machine Learning, Language Identification, Language Modelling, Marketing, Multi-class Classification, Native Language Identification

Native language identification (NLI) {--} identifying the native language (L1) of a person based on his/her writing in the second language (L2) {--} is useful for a variety of purposes, including marketing, security, and educational applications. From a traditional machine learning perspective,NLI is usually framed as a multi-class classification task, where numerous designed features are combined in order to achieve the state-of-the-art results. We introduce a deep generative language modelling (LM) approach to NLI, which consists in fine-tuning a GPT-2 model separately on texts written by the authors with the same L1, and assigning a label to an unseen text based on the minimum LM loss with respect to one of these fine-tuned GPT-2 models. Our method outperforms traditional machine learning approaches and currently achieves the best results on the benchmark NLI datasets.
Predicting Personal Opinion on Future Events with Fingerprints
Authors: Fan Yang, Eduard Dragut, Arjun Mukherjee
Date: 2020-12-01 00:00:00
Proceeding: COLING 2020 8
Tasks: Articles

Predicting users{'} opinions in their response to social events has important real-world applications, many of which political and social impacts. Existing approaches derive a population{'}s opinion on a going event from large scores of user generated content. In certain scenarios, we may not be able to acquire such content and thus cannot infer an unbiased opinion on those emerging events. To address this problem, we propose to explore opinion on unseen articles based on one{'}s fingerprinting: the prior reading and commenting history. This work presents a focused study on modeling and leveraging fingerprinting techniques to predict a user{'}s future opinion. We introduce a recurrent neural network based model that integrates fingerprinting. We collect a large dataset that consists of event-comment pairs from six news websites. We evaluate the proposed model on this dataset. The results show substantial performance gains demonstrating the effectiveness of our approach.