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A Survey of implicit discourse relation Recognition

ACM COMPUTING SURVEYS 2023年第12期55卷 1-34页

作者： Xiang, Wei Wang, Bang Huazhong Univ Sci & Technol Luoyu Rd 1037 Wuhan 430074 Hubei Peoples R China

A discourse containing one or more sentences describes daily issues and events for people to communicate their thoughts and opinions. As sentences are normally consist of multiple text segments, correct understanding of the theme of a discourse should take into consideration of the relations in between text segments. Although sometimes a connective exists in raw texts for conveying relations, it is more often the cases that no connective exists in between two text segments but some implicit relation does exist in between them. The task of implicit discourse relation recognition (IDRR) is to detect implicit relation and classify its sense between two text segments without a connective. Indeed, the IDRR task is important to diverse downstream natural language processing tasks, such as text summarization, machine translation, and so on. This article provides a comprehensive and up-to-date survey for the IDRR task. We first summarize the task definition and data sources widely used in the field. We categorize the main solution approaches for the IDRR task from the viewpoint of its development history. In each solution category, we present and analyze the most representative methods, including their origins, ideas, strengths, and weaknesses. We also present performance comparisons for those solutions experimented on a public corpus with standard data processing procedures. Finally, we discuss future research directions for discourse relation analysis.

关键词： implicit discourse relation relation recognition Penn discourse TreeBank natural language processing

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Enhancing implicit discourse relation Classification by Perceiving External Semantics and Convolving Internal Semantics 34

Enhancing Implicit Discourse Relation Classification by Perc...

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34th IEEE International Conference on Tools with Artificial Intelligence (ICTAI)

作者： Dou, Zujun Hong, Yu Sun, Yu Li, Xiao Zhou, Guodong Soochow Univ Comp Sci & Technol Suzhou Peoples R China

ISBN: (纸本)9798350397444

implicit discourse relation classification refers to a task of automatically determining relationships between arguments. It has been widely proven that, in a neural classification architecture, decoding discourse relations heavily relies on the reliable semantic representations of arguments. In addition, our previous survey shows that, for a target argument, the external semantic information hidden in the accompanying argument benefits the encoding of the target, either wholly or partially. Moreover, dependency structure appears as the crucial feature for synthesizing word senses of the entire words in arguments. Accordingly, we propose a novel method to enhance the current representation learning of pairwise arguments, which takes into consideration both external semantic information and internal dependency structure. In particular, we inject external semantic information into the Long-Short Term Memory (LSTM) unit of Recurrent Neural Network (RNN) through the input and forget gates. Different from the existing one-off interactive learning models, our method allows the neuronal memory of internal argument semantics to be affected by external information at each encoding step. On the basis, we apply the parser-based Graph Convolutional Networks (GCN) over the semantic presentations of words, so as to accumulate the closely-related semantic information in terms of dependency structures. We conduct experiments on Penn discourse TreeBank Corpus of version 2.0 (PDTB 2.0). The test results illustrate that the proposed method enhances the baseline significantly, and it obtains comparable performance compared to the state of the art.

关键词： implicit discourse relation gcn pdtb

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Focus-sensitive relation disambiguation for implicit discourse relation detection

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Frontiers of Computer Science 2019年第6期13卷 1266-1281页

作者： Yu HONG Siyuan DING Yang XU Xiaoxia JIANG Yu WANG Jianmin YAO Qiaoming ZHU Guodong ZHOU Natural Language Processing Lab School of Computer Science&TechnologySoochow UniversitySuzhou 215006China Science and Technology on Information Systems Engineering Laboratory Nanjing 210007China

We study implicit discourse relation detection,which is one of the most challenging tasks in the field of discourse *** specialize in ambiguous implicit discourse relation,which is an imperceptible linguistic phenomenon and therefore difficult to identify and *** this paper,we first create a novel task named implicit discourse relation disambiguation(IDRD).Second,we propose a focus-sensitive relation disambiguation model that affirms a truly-correct relation when it is triggered by focal sentence *** addition,we specifically develop a topicdriven focus identification method and a relation search system(RSS)to support the relation ***,we improve current relation detection systems by using the disambiguation *** on the penn discourse treebank(PDTB)show promising improvements.

关键词： implicit discourse relation focus-sensitive implicit relation disambiguation topic-driven focus identification

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implicit discourse relation Recognition for English and Chinese with Multiview Modeling and Effective Representation Learning

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ACM TRANSACTIONS ON ASIAN AND LOW-RESOURCE LANGUAGE INFORMATION PROCESSING 2017年第3期16卷 1–21页

作者： Li, Haoran Zhang, Jiajun Zong, Chengqing Chinese Acad Sci Univ Chinese Acad Sci Inst Automat Natl Lab Pattern Recognit Intelligence Bldg 95Zhongguancun East Rd Beijing 100190 Peoples R China Chinese Acad Sci Univ Chinese Acad Sci Ctr Excellence Brain Sci & Intelligence Technol Inst AutomatNatl Lab Pattern Recognit Intelligence Bldg 95Zhongguancun East Rd Beijing 100190 Peoples R China

discourse relations between two text segments play an important role inmany Natural Language Processing (NLP) tasks. The connectives strongly indicate the sense of discourse relations, while in fact, there are no connectives in a large proportion of discourse relations, that is, implicit discourse relations. Compared with explicit relations, implicit relations are much harder to detect and have drawn significant attention. Until now, there have been many studies focusing on English implicit discourse relations, and few studies address implicit relation recognition in Chinese even though the implicit discourse relations in Chinese are more common than those in English. In our work, both the English and Chinese languages are our focus. The key to implicit relation prediction is to properly model the semantics of the two discourse arguments, as well as the contextual interaction between them. To achieve this goal, we propose a neural network based framework that consists of two hierarchies. The first one is the model hierarchy, in which we propose a maxmargin learning method to explore the implicit discourse relation from multiple views. The second one is the feature hierarchy, in which we learn multilevel distributed representations from words, arguments, and syntactic structures to sentences. We have conducted experiments on the standard benchmarks of English and Chinese, and the results show that compared with several methods our proposed method can achieve the best performance in most cases.

关键词： implicit discourse relation neural network multilevel features maxmargin learning

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Expanding Native Training Data for implicit discourse relation Classification 3

Expanding Native Training Data for Implicit Discourse Relati...

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3rd National Conference on Social Media Processing (SMP)

作者： Hong, Yu Zhu, Shanshan Yan, Weirong Yao, Jianmin Zhu, Qiaoming Zhou, Guodong Soochow Univ Sch Comp & Technol Key Lab Nat Language Proc Jiangsu Prov Suzhou Jiangsu Peoples R China

ISBN: (纸本)9783662455579

Linguistically informed features are provably useful in classifying implicit discourse relations among adjacent text spans. However the state of the art methods in this area suffer from either sparse natively implicit relation corpus or counter-intuitive artificially implicit one, and consequently either insufficient or distorted training in automatically learning discriminative features. To overcome the problem, this paper proposes a semantic frame based vector model towards unsupervised acquisition of semantically and relationally parallel data, aiming to enlarge natively implicit relation corpus so as to optimize the training effect. Experiments on PDTB 2.0 show the usage of the acquired parallel corpus gives statistically significant improvements over that of the prototypical corpus.

关键词： implicit discourse relation Semantic frame Parallel corpus discourse analysis

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Cross-argument inference for implicit discourse relation recognition 12

Cross-argument inference for implicit discourse relation rec...

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21st ACM International Conference on Information and Knowledge Management, CIKM 2012

作者： Hong, Yu Zhou, Xiaopei Che, Tingting Yao, Jianmin Zhu, Qiaoming Zhou, Guodong Key Laboratory of Natural Language Processing of Jiangsu Province School of Computer Science and Technology Soochow University No.1 Shizi Street Suzhou City Jiangsu Province 215006 China

ISBN: (纸本)9781450311564

Motivated by the critical importance of connectives in recognizing discourse relations, we present an unsupervised cross-argument inference mechanism to implicit discourse relation recognition. The basic idea is to infer the implicit discourse relation of an argument pair from a large number of comparable argument pairs, which are automatically retrieved from the web in an unsupervised way. In this way, the inference proceeds from explicit relations to implicit ones via connective as bridge. This kind of pair-to-pair inference is based on the assumption that two argument pairs with high content similarity (i.e. comparable argument pairs) should have similar discourse relationship. Evaluation on PDTB proves the effectiveness of our inference mechanism in implicit relation recognition to the four level-1 relations. It also shows that our mechanism significantly outperforms other alternatives. © 2012 ACM.

关键词： implicit discourse relation pair-to-pair inference

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Predicting implicit discourse relations with Purely Distributed Representations 1

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14th China National Conference on Computational Linguistics (CCL) / 3rd International Symposium on Natural Language Processing Based on Naturally Annotated Big Data (NLP-NABD)

作者： Li, Haoran Zhang, Jiajun Zong, Chengqing CASIA Natl Lab Pattern Recognit Beijing Peoples R China

ISBN: (数字)9783319258164

ISBN: (纸本)9783319258164;9783319258157

discourse relations between two consecutive segments play an important role in many natural language processing (NLP) tasks. However, a large portion of the discourse relations are implicit and difficult to detect due to the absence of connectives. Traditional detection approaches utilize discrete features, such as words, clusters and syntactic production rules, which not only depend strongly on the linguistic resources, but also lead to severe data sparseness. In this paper, we instead propose a novel method to predict the implicit discourse relations based on the purely distributed representations of words, sentences and syntactic features. Furthermore, we learn distributed representations for different kinds of features. The experiments show that our proposed method can achieve the best performance in most cases on the standard data sets.

关键词： implicit discourse relation Distributed Word level representation Sentence level representation

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