pwc-archive/papers-with-abstracts huggingface dataset:
Emotion Classification of COVID-19 Chinese Microblogs based on the Emotion Category Description
Authors: Guo Xianwei, Lai Hua, Xiang Yan, Yu Zhengtao, Huang Yuxin
Date: 2021-08-01 00:00:00
Proceeding: CCL 2021 8
Tasks: Classification, Emotion Classification, Question Answering

“Emotion classification of COVID-19 Chinese microblogs helps analyze the public opinion triggered by COVID-19. Existing methods only consider the features of the microblog itself with-out combining the semantics of emotion categories for modeling. Emotion classification of mi-croblogs is a process of reading the content of microblogs and combining the semantics of emo-tion categories to understand whether it contains a certain emotion. Inspired by this we proposean emotion classification model based on the emotion category description for COVID-19 Chi-nese microblogs. Firstly we expand all emotion categories into formalized category descriptions.Secondly based on the idea of question answering we construct a question for each microblogin the form of ‘What is the emotion expressed in the text X?’ and regard all category descrip-tions as candidate answers. Finally we construct a question-and-answer pair and use it as the input of the BERT model to complete emotion classification. By integrating rich contextual andcategory semantics the model can better understand the emotion of microblogs. Experimentson the COVID-19 Chinese microblog dataset show that our approach outperforms many existinge motion classification methods including the BERT baseline.”
Multi-level Emotion Cause Analysis by Multi-head Attention Based Multi-task Learning
Authors: Li Xiangju, Feng Shi, Zhang Yifei, Wang Daling
Date: 2021-08-01 00:00:00
Proceeding: CCL 2021 8
Tasks: Multi-Task Learning

“Emotion cause analysis (ECA) aims to identify the potential causes behind certain emotions intext. Lots of ECA models have been designed to extract the emotion cause at the clause level.However in many scenarios only extracting the cause clause is ambiguous. To ease the problemin this paper we introduce multi-level emotion cause analysis which focuses on identifying emotion cause clause (ECC) and emotion cause keywords (ECK) simultaneously. ECK is a more challenging task since it not only requires capturing the specific understanding of the role of eachword in the clause but also the relation between each word and emotion expression. We observethat ECK task can incorporate the contextual information from the ECC task while ECC taskcan be improved by learning the correlation between emotion cause keywords and emotion fromthe ECK task. To fulfill the goal of joint learning we propose a multi-head attention basedmulti-task learning method which utilizes a series of mechanisms including shared and privatefeature extractor multi-head attention emotion attention and label embedding to capture featuresand correlations between the two tasks. Experimental results show that the proposed method consistently outperforms the state-of-the-art methods on a benchmark emotion cause dataset.”
Jointly Learning Salience and Redundancy by Adaptive Sentence Reranking for Extractive Summarization
Authors: Zhang Ximing, Liu Ruifang
Date: 2021-08-01 00:00:00
Proceeding: CCL 2021 8
Tasks: Extractive Summarization, Extractive Text Summarization, Reranking, Sentence, Text Summarization

“Extractive text summarization seeks to extract indicative sentences from a source document andassemble them to form a summary. Selecting salient but not redundant sentences has alwaysbeen the main challenge. Unlike the previous two-stage strategies this paper presents a unifiedend-to-end model learning to rerank the sentences by modeling salience and redundancy simul-taneously. Through this ranking mechanism our method can improve the quality of the overall candidate summary by giving higher scores to sentences that can bring more novel informa-tion. We first design a summary-level measure to evaluate the cumulating gain of each candidate summaries. Then we propose an adaptive training objective to rerank the sentences aiming atobtaining a summary with a high summary-level score. The experimental results and evalua-tion show that our method outperforms the strong baselines on three datasets and further booststhe quality of candidate summaries which intensely indicate the effectiveness of the proposed framework.”
Topic Knowledge Acquisition and Utilization for Machine Reading Comprehension in Social Media Domain
Authors: Tian Zhixing, Zhang Yuanzhe, Liu Kang, Zhao Jun
Date: 2021-08-01 00:00:00
Proceeding: CCL 2021 8
Tasks: Clustering, Machine Reading Comprehension, Reading Comprehension

“In this paper we focus on machine reading comprehension in social media. In this domain onenormally posts a message on the assumption that the readers have specific background knowledge. Therefore those messages are usually short and lacking in background information whichis different from the text in the other domain. Thus it is difficult for a machine to understandthe messages comprehensively. Fortunately a key nature of social media is clustering. A group of people tend to express their opinion or report news around one topic. Having realized this we propose a novel method that utilizes the topic knowledge implied by the clustered messages to aid in the comprehension of those short messages. The experiments on TweetQA datasets demonstrate the effectiveness of our method.”
Category-Based Strategy-Driven Question Generator for Visual Dialogue
Authors: Shi Yanan, Tan Yanxin, Feng Fangxiang, Zheng Chunping, Wang Xiaojie
Date: 2021-08-01 00:00:00
Proceeding: CCL 2021 8
Tasks: Sentence

“GuessWhat?! is a task-oriented visual dialogue task which has two players a guesser and anoracle. Guesser aims to locate the object supposed by oracle by asking several Yes/No questions which are answered by oracle. How to ask proper questions is crucial to achieve the final goal of the whole task. Previous methods generally use an word-level generator which is hard to grasp the dialogue-level questioning strategy. They often generate repeated or useless questions. This paper proposes a sentence-level category-based strategy-driven question generator(CSQG) to explicitly provide a category based questioning strategy for the generator. First we encode the image and the dialogue history to decide the category of the next question to be generated. Thenthe question is generated with the helps of category-based dialogue strategy as well as encoding of both the image and dialogue history. The evaluation on large-scale visual dialogue dataset GuessWhat?! shows that our method can help guesser achieve 51.71% success rate which is the state-of-the-art on the supervised training methods.”
From Learning-to-Match to Learning-to-Discriminate:Global Prototype Learning for Few-shot Relation Classification
Authors: Liu Fangchao, Xiao Xinyan, Yan Lingyong, Lin Hongyu, Han Xianpei, Dai Dai, Wu Hua, Sun Le
Date: 2021-08-01 00:00:00
Proceeding: CCL 2021 8
Tasks: Classification, Few-Shot Relation Classification, Relation, Relation Classification

“Few-shot relation classification has attracted great attention recently and is regarded as an ef-fective way to tackle the long-tail problem in relation classification. Most previous works onfew-shot relation classification are based on learning-to-match paradigms which focus on learn-ing an effective universal matcher between the query and one target class prototype based oninner-class support sets. However the learning-to-match paradigm focuses on capturing the sim-ilarity knowledge between query and class prototype while fails to consider discriminative infor-mation between different candidate classes. Such information is critical especially when targetclasses are highly confusing and domain shifting exists between training and testing phases. Inthis paper we propose the Global Transformed Prototypical Networks(GTPN) which learns tobuild a few-shot model to directly discriminate between the query and all target classes with bothinner-class local information and inter-class global information. Such learning-to-discriminate paradigm can make the model concentrate more on the discriminative knowledge between allcandidate classes and therefore leads to better classification performance. We conducted exper-iments on standard FewRel benchmarks. Experimental results show that GTPN achieves very competitive performance on few-shot relation classification and reached the best performance onthe official leaderboard of FewRel 2.0 1.”
Multi-Strategy Knowledge Distillation Based Teacher-Student Framework for Machine Reading Comprehension
Authors: Yu Xiaoyan, Liu Qingbin, He Shizhu, Liu Kang, Liu Shengping, Zhao Jun, Zhou Yongbin
Date: 2021-08-01 00:00:00
Proceeding: CCL 2021 8
Tasks: Knowledge Distillation, Machine Reading Comprehension, Reading Comprehension, Sentence

“The irrelevant information in documents poses a great challenge for machine reading compre-hension (MRC). To deal with such a challenge current MRC models generally fall into twoseparate parts: evidence extraction and answer prediction where the former extracts the key evi-dence corresponding to the question and the latter predicts the answer based on those sentences.However such pipeline paradigms tend to accumulate errors i.e. extracting the incorrect evi-dence results in predicting the wrong answer. In order to address this problem we propose aMulti-Strategy Knowledge Distillation based Teacher-Student framework (MSKDTS) for ma-chine reading comprehension. In our approach we first take evidence and document respec-tively as the input reference information to build a teacher model and a student model. Then the multi-strategy knowledge distillation method transfers the knowledge from the teacher model to the student model at both feature and prediction level through knowledge distillation approach.Therefore in the testing phase the enhanced student model can predict answer similar to the teacher model without being aware of which sentence is the corresponding evidence in the docu-ment. Experimental results on the ReCO dataset demonstrate the effectiveness of our approachand further ablation studies prove the effectiveness of both knowledge distillation strategies.”
LRRA:A Transparent Neural-Symbolic Reasoning Framework for Real-World Visual Question Answering
Authors: Wan Zhang, Chen Keming, Zhang Yujie, Xu Jinan, Chen Yufeng
Date: 2021-08-01 00:00:00
Proceeding: CCL 2021 8
Tasks: Question Answering, Visual Question Answering, Visual Question Answering (VQA)

“The predominant approach of visual question answering (VQA) relies on encoding the imageand question with a ”black box” neural encoder and decoding a single token into answers suchas ”yes” or ”no”. Despite this approach’s strong quantitative results it struggles to come up withhuman-readable forms of justification for the prediction process. To address this insufficiency we propose LRRA[LookReadReasoningAnswer]a transparent neural-symbolic framework forvisual question answering that solves the complicated problem in the real world step-by-steplike humans and provides human-readable form of justification at each step.Specifically LRRAlearns to first convert an image into a scene graph and parse a question into multiple reasoning instructions. It then executes the reasoning instructions one at a time by traversing the scenegraph using a recurrent neural-symbolic execution module.Finally it generates answers to the given questions and makes corresponding marks on the image. Furthermore we believe that the relations between objects in the question is of great significance for obtaining the correct answerso we create a perturbed GQA test set by removing linguistic cues (attributes and relations) in the questions to analyze which part of the question contributes more to the answer.Our experimentson the GQA dataset show that LRRA is significantly better than the existing representative model(57.12% vs. 56.39%). Our experiments on the perturbed GQA test set show that the relations between objects is more important for answering complicated questions than the attributes ofobjects.Keywords:Visual Question Answering Relations Between Objects Neural-Symbolic Reason-ing.”
Meaningfulness and unit of Zipf’s law: evidence from danmu comments
Authors: Zhou Yihan
Date: 2021-08-01 00:00:00
Proceeding: CCL 2021 8

“Zipf’s law is a succinct yet powerful mathematical law in linguistics. However the mean-ingfulness and units of the law have remained controversial. The current study usesonline video comments call “danmu comment” to investigate these two questions. Theresults are consistent with previous studies arguing Zipf’s law is subject to topical coher-ence. Specifically it is found that danmu comments sampled from a single video followZipf’s law better than danmu comments sampled from a collection of videos. The resultsalso suggest the existence of multiple units of Zipf’s law. When different units includingwords n-grams and danmu comments are compared both words and danmu commentsobey Zipf’s law and words may be a better fit. The issues of combined n-grams in the literature are also discussed.”
A Chinese Machine Reading Comprehension Dataset Automatic Generated Based on Knowledge Graph
Authors: Zhao Hanyu, Yuan Sha, Leng Jiahong, Pan Xiang, Xue Zhao, Ma Quanyue, Liang Yangxiao
Date: 2021-08-01 00:00:00
Proceeding: CCL 2021 8
Tasks: Dataset Generation, Machine Reading Comprehension, Reading Comprehension

“Machine reading comprehension (MRC) is a typical natural language processing (NLP)task and has developed rapidly in the last few years. Various reading comprehension datasets have been built to support MRC studies. However large-scale and high-quality datasets are rare due to the high complexity and huge workforce cost of making sucha dataset. Besides most reading comprehension datasets are in English and Chinesedatasets are insufficient.In this paper we propose an automatic method for MRCdataset generation and build the largest Chinese medical reading comprehension dataset presently named CMedRC. Our dataset contains 17k questions generated by our auto-matic method and some seed questions. We obtain the corresponding answers from amedical knowledge graph and manually check all of them. Finally we test BiLSTM andBERT-based pre-trained language models (PLMs) on our dataset and propose a base-line for the following studies. Results show that the automatic MRC dataset generation method is considerable for future model improvements.”
Morphological Analysis Corpus Construction of Uyghur
Authors: Abudouwaili Gulinigeer, Abiderexiti Kahaerjiang, Wushouer Jiamila, Shen Yunfei, Maimaitimin Turenisha, Yibulayin Tuergen
Date: 2021-08-01 00:00:00
Proceeding: CCL 2021 8
Tasks: LEMMA, Lemmatization, Morphological Analysis, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), Translation

“Morphological analysis is a fundamental task in natural language processing and results can beapplied to different downstream tasks such as named entity recognition syntactic analysis andmachine translation. However there are many problems in morphological analysis such as lowaccuracy caused by a lack of resources. In this paper to alleviate the lack of resources in Uyghurmorphological analysis research we construct a Uyghur morphological analysis corpus based onthe analysis of grammatical features and the format of the general morphological analysis corpus.We define morphological tags from 14 dimensions and 53 features manually annotate and correctthe dataset. Finally the corpus provided some informations such as word lemma part of speech morphological analysis tags morphological segmentation and lemmatization. Also this paperanalyzes some basic features of the corpus and we use the models and datasets provided bySIGMORPHON Shared Task organizers to design comparative experiments to verify the corpus’savailability. Results of the experiment are 85.56% 88.29% respectively. The corpus provides areference value for morphological analysis and promotes the research of Uyghur natural language processing.”
Improving Entity Linking by Encoding Type Information into Entity Embeddings
Authors: Li Tianran, Yang Erguang, Zhang Yujie, Chen Yufeng, Xu Jinan
Date: 2021-08-01 00:00:00
Proceeding: CCL 2021 8
Tasks: Entity Embeddings, Entity Linking, Vocal Bursts Type Prediction

“Entity Linking (EL) refers to the task of linking entity mentions in the text to the correct entities inthe Knowledge Base (KB) in which entity embeddings play a vital and challenging role because of the subtle differences between entities. However existing pre-trained entity embeddings onlylearn the underlying semantic information in texts yet the fine-grained entity type informationis ignored which causes the type of the linked entity is incompatible with the mention context.In order to solve this problem we propose to encode fine-grained type information into entity embeddings. We firstly pre-train word vectors to inject type information by embedding wordsand fine-grained entity types into the same vector space. Then we retrain entity embeddings withword vectors containing fine-grained type information. By applying our entity embeddings to twoexisting EL models our method respectively achieves 0.82% and 0.42% improvement on average F1 score of the test sets. Meanwhile our method is model-irrelevant which means it can helpother EL models.”
A Unified Representation Learning Strategy for Open Relation Extraction with Ranked List Loss
Authors: Lou Renze, Zhang Fan, Zhou Xiaowei, Wang Yutong, Wu Minghui, Sun Lin
Date: 2021-08-01 00:00:00
Proceeding: CCL 2021 8
Tasks: Clustering, Metric Learning, Relation, Relation Extraction, Representation Learning

“Open Relation Extraction (OpenRE) aiming to extract relational facts from open-domain cor-pora is a sub-task of Relation Extraction and a crucial upstream process for many other NLPtasks. However various previous clustering-based OpenRE strategies either confine themselves to unsupervised paradigms or can not directly build a unified relational semantic space henceimpacting down-stream clustering. In this paper we propose a novel supervised learning frame-work named MORE-RLL (Metric learning-based Open Relation Extraction with Ranked ListLoss) to construct a semantic metric space by utilizing Ranked List Loss to discover new rela-tional facts. Experiments on real-world datasets show that MORE-RLL can achieve excellent performance compared with previous state-of-the-art methods demonstrating the capability of MORE-RLL in unified semantic representation learning and novel relational fact detection.”
NS-Hunter: BERT-Cloze Based Semantic Denoising for Distantly Supervised Relation Classification
Authors: Shen Tielin, Wang Daling, Feng Shi, Zhang Yifei
Date: 2021-08-01 00:00:00
Proceeding: CCL 2021 8
Tasks: Classification, Denoising, Language Modelling, Relation, Relation Classification, Sentence

“Distant supervision can generate large-scale relation classification data quickly and economi-cally. However a great number of noise sentences are introduced which can not express their labeled relations. By means of pre-trained language model BERT’s powerful function in this paper we propose a BERT-based semantic denoising approach for distantly supervised relation classification. In detail we define an entity pair as a source entity and a target entity. For the specific sentences whose target entities in BERT-vocabulary (one-token word) we present the differences of dependency between two entities for noise and non-noise sentences. For general sentences whose target entity is multi-token word we further present the differences of last hid-den states of [MASK]-entity (MASK-lhs for short) in BERT for noise and non-noise sentences.We regard the dependency and MASK-lhs in BERT as two semantic features of sentences. With BERT we capture the dependency feature to discriminate specific sentences first then capturethe MASK-lhs feature to denoise distant supervision datasets. We propose NS-Hunter a noveldenoising model which leverages BERT-cloze ability to capture the two semantic features andintegrates above functions. According to the experiment on NYT data our NS-Hunter modelachieves the best results in distant supervision denoising and sentence-level relation classification. Keywords: Distant supervision relation classification semantic denoisingIntroduction”
A Trigger-Aware Multi-Task Learning for Chinese Event Entity Recognition
Authors: Xiang Yangxiao, Li Chenliang
Date: 2021-08-01 00:00:00
Proceeding: CCL 2021 8
Tasks: ARC, Multi-Task Learning, NER, Relation, Relation Extraction

“This paper tackles a new task for event entity recognition (EER). Different from named entity recognizing (NER) task it only identifies the named entities which are related to a specific event type. Currently there is no specific model to directly deal with the EER task. Previous namedentity recognition methods that combine both relation extraction and argument role classification(named NER+TD+ARC) can be adapted for the task by utilizing the relation extraction component for event trigger detection (TD). However these technical alternatives heavily rely on the efficiency of the event trigger detection which have to require the tedious yet expensive human la-beling of the event triggers especially for languages where triggers contain multiple tokens andhave numerous synonymous expressions (such as Chinese). In this paper a novel trigger-awaremulti-task learning framework (TAM) which jointly performs both trigger detection and evententity recognition is proposed to tackle Chinese EER task. We conduct extensive experimentson a real-world Chinese EER dataset. Compared with the previous methods TAM outperformsthe existing technical alternatives in terms of F1 measure. Besides TAM can accurately identifythe synonymous expressions that are not included in the trigger dictionary. Morover TAM canobtain a robust performance when only a few labeled triggers are available.”
Improving Low-Resource Named Entity Recognition via Label-Aware Data Augmentation and Curriculum Denoising
Authors: Zhu Wenjing, Liu Jian, Xu Jinan, Chen Yufeng, Zhang Yujie
Date: 2021-08-01 00:00:00
Proceeding: CCL 2021 8
Tasks: Data Augmentation, Denoising, Language Modeling, Language Modelling, Low Resource Named Entity Recognition, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER

“Deep neural networks have achieved state-of-the-art performances on named entity recognition(NER) with sufficient training data while they perform poorly in low-resource scenarios due to data scarcity. To solve this problem we propose a novel data augmentation method based on pre-trained language model (PLM) and curriculum learning strategy. Concretely we use the PLMto generate diverse training instances through predicting different masked words and design atask-specific curriculum learning strategy to alleviate the influence of noises. We evaluate the effectiveness of our approach on three datasets: CoNLL-2003 OntoNotes5.0 and MaScip of which the first two are simulated low-resource scenarios and the last one is a real low-resource dataset in material science domain. Experimental results show that our method consistently outperform the baseline model. Specifically our method achieves an absolute improvement of3.46% F1 score on the 1% CoNLL-2003 2.58% on the 1% OntoNotes5.0 and 0.99% on the full of MaScip.”