计算机科学 ›› 2020, Vol. 47 ›› Issue (4): 157-163.doi: 10.11896/jsjkx.190300115
胡超文1, 杨亚连2, 邬昌兴1
HU Chao-wen1, YANG Ya-lian2, WU Chang-xing1
摘要: 隐式篇章关系识别是自然语言处理中一项富有挑战性的任务,旨在判断缺少连接词的两个论元(子句或者句子)之间的语义关系(例如转折)。近年来,随着深度学习在自然语言处理领域的广泛应用,各种基于深度学习的隐式篇章关系识别方法取得了不错的效果,其性能全面超越了早期基于人工特征的方法。文中分三大类对最近的隐式篇章关系识别方法进行讨论:基于论元编码的方法、基于论元交互的方法和引入显式篇章数据的半监督方法。在PDTB数据集上的实验结果显示:1)通过显式地建模论元中词或文本片段之间的语义关系,基于论元交互的方法的性能明显好于基于论元编码的方法;2)引入显式篇章数据的半监督方法能有效地缓解数据稀疏问题,从而进一步提升识别的性能。最后,分析了当前面临的主要问题,并指出了未来可能的研究方向。
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[1]PITLER E,NENKOVA A.Revisiting Readability:A UnifiedFramework for Predicting Text Quality[C]//Proceedings of the 2008 Conference on Empirical Methods in Natural Language Processing.Honolulu,Hawaii:Association for Computational Linguistics,2008:186-195. [2]LIN Z,NG H T,KAN M Y.Automatically Evaluating Text Coherence Using Discourse Relations[C]//Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics:Human Language Technologies-Volume 1.Association for Computational Linguistics,2011:997-1006. [3]PITLER E,NENKOVA A.Using Syntax to Disambiguate Explicit Discourse Connectives in Text[C]//Proceedings of the ACL-IJCNLP 2009.Suntec,Singapore:Association for Computational Linguistics,2009:13-16. [4]LI Y C,SUN J,ZHOU G D.Automatic Recognition and Classification on Chinese Discourse Connective[J].Acta Scientiarum Naturalium Universitatis Pekinensis,2015,51(2):307-314. [5]PRASAD R,DINESH N,LEE A,et al.The Penn DiscourseTreeBank 2.0[C]//Proceedings of the 6th Conference of the Language Resources and Evaluation.2008:2961-2968. [6]RUTHERFORD A,DEMBERG V,XUE N.A Systematic Studyof Neural Discourse Models for Implicit Discourse Relation[C]//Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics.2017:281-291. [7]LAN M,WANG J,WU Y,et al.Multi-task Attention-basedNeural Networks for Implicit Discourse Relationship Representation and Identification[C]//Proceedings of the 2017 Confe-rence on Empirical Methods in Natural Language Processing.Copenhagen,Denmark:Association for Computational Linguistics,2017:1299-1308. [8]WU C X.Semi-supervised Implicit Discourse Relation Recognition[D].Xiamen:Xiamen Uuniversity,2017. [9]TU M,ZHOU Y,ZONG C.Enhancing Grammatical Cohesion:Generating Transitional Expressions for SMT[C]//Proceedings of the 52nd Annual Meeting of the Association for Computatio-nal Linguistics.2014:850-860. [10]ZHOU L,LI B,GAO W,et al.Unsupervised Discovery of Discourse Relations for Eliminating Intra-sentence Polarity Ambiguities[C]//Proceedings of the Conference on Empirical Me-thods in Natural Language Processing.Association for Computational Linguistics,2011:162-171. [11]VERBERNE S,BOVES L,OOSTDIJK N,et al.Evaluating Discourse-based Answer Extraction for Why Question Answering[C]//Proceedings of the 30th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR ’07).Amsterdam,ACM Press,2007:735. [12]MARCU D,ECHIHABI A.An Unsupervised Approach to Recognizing Discourse Relations[C]//Proceedings of 40th Annual Meeting of The Association for Computational Linguistics.Philadelphia,Pennsylvania,USA:Association for Computational Linguistics,2002:368-375. [13]RUTHERFORD A,XUE N.Discovering Implicit Discourse Relations Through Brown Cluster Pair Representation and Coreference Patterns[C]//Proceedings of the 14th Conference of the European Chapter of the Association for Computational Linguistics.Gothenburg,Sweden:Association for Computational Linguistics,2014:645-654. [14]WELLNER B,PUSTEJOVSKY J,HAVASI C,et al.Classification of Discourse Coherence Relations:An Exploratory Study using Multiple Knowledge Sources[C]//Proceedings of the 7th SIGdial Workshop on Discourse and Dialogue.Sydney,Austra-lia:Association for Computational Linguistics,2006:117-125. [15]LIN Z,KAN M Y,NG H T.Recognizing Implicit Discourse Relations in the Penn Discourse Treebank[C]//Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing.Singapore:Association for Computational Linguistics,2009:343-351. [16]PITLER E,LOUIS A,NENKOVA A.Automatic Sense Prediction for Implicit Discourse Relations in Text[C]//Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP.Suntec,Singapore:Association for Computational Linguistics,2009:683-691. [17]XU F,ZHU Q M,ZHOU G D.Implicit Discourse Relation Recognition Based on Tree Kernel[J].Acta Journal of Software,2013,24(5):1022-1035. [18]ZHANG M Y,SONG Y,QIN B,et al.Chinese Discourse Relation Recognition[J].Journal of Chinese Information processing,2013,27(6):51-58. [19]BAI H,ZHAO H.Deep Enhanced Representation for ImplicitDiscourse Relation Recognition[C]//Proceedings of the 27th International Conference on Computational Linguistics.Santa Fe,New Mexico,USA:Association for Computational Linguistics,2018:571-583. [20]DAI Z,HUANG R.Improving Implicit Discourse Relation Classification by Modeling Inter-dependencies of Discourse Units in a Paragraph[C]//Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics:Human Language Technologies.New Orleans,Louisiana:Association for Computational Linguistics,2018:141-151. [21]ZHANG B,SU J,XIONG D,et al.Shallow Convolutional Neural Network for Implicit Discourse Relation Recognition[C]//Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing.Lisbon,Portugal:Association for Computational Linguistics,2015:2230-2235. [22]SAK H,SENIOR A,BEAUFAYS F.Long Short-term Memory Recurrent Neural Network Architectures for Large Scale Acoustic Modeling[C]//Fifteenth Annual Conference of the International Speech Communication Association.2014:338-342. [23]JI Y,EISENSTEIN J.One Vector is Not Enough:Entity-Augmented Distributed Semantics for Discourse Relations[J].Transactions of the Association for Computational Linguistics,2015,3:329-344. [24]WANG Y,LI S,YANG J,et al.Tag-Enhanced Tree-Structured Neural Networks for Implicit Discourse Relation Classification[C]//Proceedings of the Eighth International Joint Conference on Natural Language Processing.2017:496-505. [25]TAI K S,SOCHER R,MANNING C D.Improved SemanticRepresentations From Tree-Structured Long Short-Term Memory Networks[C]//Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing.Beijing,China:Association for Computational Linguistics,2015:1556-1566. [26]QIN L,ZHANG Z,ZHAO H.Implicit Discourse Relation Recognition with Context-aware Character-enhanced Embeddings[C]//the 26th International Conference on Computational Linguistics.Osaka,Japan:The COLING 2016 Organizing Committee,2016:1914-1924. [27]ZHANG B,XIONG D,SU J,et al.Learning Better Discourse Representation for Implicit Discourse Relation Recognition via Attention Networks[J].Neurocomputing,2018,275:1241-1249. [28]KISHIMOTO Y,MURAWAKI Y,KUROHASHI S.A Knowledge-Augmented Neural Network Model for Implicit Discourse Relation Classification[C]//Proceedings of the 27th International Conference on Computational Linguistics.Santa Fe,New Mexico,USA:Association for Computational Linguistics,2018:584-595. [29]CHEN J,ZHANG Q,LIU P,et al.Implicit Discourse Relation Detection via a Deep Architecture with Gated Relevance Network[C]//Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics.Berlin,Germany:Association for Computational Linguistics,2016:1726-1735. [30]LEI W,WANG X,LIU M,et al.SWIM:A Simple Word Interaction Model for Implicit Discourse Relation Recognition[C]//International Joint Conferences on Artificial Intelligence Organization.2017:4026-4032. [31]LIU Y,LI S,ZHANG X,et al.Implicit Discourse Relation Classification via Multi-task Neural Networks[C]//Thirtieth AAAI Conference on Artificial Intelligence.2016:2750-2756. [32]LIU Y,LI S.Recognizing Implicit Discourse Relations via Repeated Reading:Neural Networks with Multi-Level Attention[C]//Association for Computational Linguistics.2016:1224-1233. [33]RÖNNQVIST S,SCHENK N,CHIARCOS C.A RecurrentNeural Model with Attention for the Recognition of Chinese Implicit Discourse Relations[C]//Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics.Vancouver,Canada:Association for Computational Linguistics,2017:256-262. [34]WU W,WANG H,LIU T,et al.Phrase-level Self-AttentionNetworks for Universal Sentence Encoding[C]//Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing.Brussels,Belgium:Association for Computational Linguistics,2018:3729-3738. [35]SPORLEDER C,LASCARIDES A.Using Automatically Labelled Examples to Classify Rhetorical Relations:an Assessment[J].Natural Language Engineering,2008,14(3):369-416. [36]RUTHERFORD A,XUE N.Improving the Inference of Implicit Discourse Relations Via Classifying Explicit Discourse Connectives[C]//Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics:Human Language Technologies.2015:799-808. [37]WU C,SHI X,SU J,et al.Co-training for Implicit Discourse Relation Recognition Based on Manual and Distributed Features[J].Neural Processing Letters,2017,46(1):233-250. [38]XU Y,HONG Y,RUAN H,et al.Using Active Learning to Expand Training Data for Implicit Discourse Relation Recognition[C]//Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing.Brussels,Belgium:Association for Computational Linguistics,2018:725-731. [39]CARLSON L,MARCU D,OKUROWSKI M E.Building a discourse-tagged corpus in the framework of rhetorical structure theory//Current and new directions in discourse and dialogue.Dordrecht:Springer,2003:85-112. [40]WU C,SHI X D,CHEN Y,et al.Bilingually-constrained Synthetic Data for Implicit Discourse Relation Recognition[C]//Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing.Austin,Texas:Association for Computational Linguistics,2016:2306-2312. [41]PAN S J,YANG Q.A Survey on Transfer Learning[J].IEEE Transactions on Knowledge and Data Engineering,2010,22(10):1345-1359. [42]MIKOLOV T,SUTSKEVER I,CHEN K,et al.DistributedRepresentations of Words and Phrases and Their Compositio-nality[C]//Advances in Neural Information Processing Systems.2013:3111-3119. [43]PENNINGTON J,SOCHER R,MANNING C.Glove:GlobalVectors for Word Representation[C]//Proceedings of the 2014 Conference on Empirical Methods in Natural Language Proces-sing (EMNLP).Doha,Qatar:Association for Computational Linguistics,2014:1532-1543. [44]PETERS M,NEUMANN M,IYYER M,et al.Deep Contextualized Word Representations[C]//Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics:Human Language Technologies.New Orleans,Louisiana:Association for Computational Linguistics,2018:2227-2237. [45]RADFORD A,NARASIMHAN K,SALIMANS T,et al.Improving Language Understanding by Generative Pre-training[OL].https://s3-us-west-2.amazonaws.com/openai-assets/research-covers/language unsupervised/ language understanding paper.pdf,2018. [46]DEVLIN J,CHANG M W,LEE K,et al.BERT:Pre-training of Deep Bidirectional Transformers for Language Understanding[J].arXiv:1810.04805,2018. [47]BRAUD C,DENIS P.Learning Connective-based Word Representations for Implicit Discourse Relation Identification[C]//Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing.Austin,Texas:Association for Computational Linguistics,2016:203-213. [48]TURNEY P D,PANTEL P.From Frequency to Meaning:Vector Space Models of Semantics[J].Journal of Artificial Intelligence Research,2010,37:141-188. [49]WU C,SHI X D,CHEN Y,et al.Improving Implicit Discourse Relation Recognition with Discourse-specific Word Embeddings[C]//Association for Computational Linguistics.2017:269-274. [50]PARK J,CARDIE C.Improving Implicit Discourse RelationRecognition Through Feature Set Optimization[C]// Procee-dings of the 13th Annual Meeting of the Special Interest Group on Discourse and Dialogue.Association for Computational Linguistics,2012:108-112. [51]MCCANN B,BRADBURY J,XIONG C,et al.Learned inTranslation:Contextualized Word Vectors[C]//Advances in Neural Information Processing Systems.2017:6294-6305. [52]HOWARD J,RUDER S.Universal Language Model Fine-tuning for Text Classification[C]//Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics.2018:328-339. [53]PARK J,CARDIE C.Building Chinese Discourse Corpus with Connective-driven Dependency Tree Structure[C]//Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP).Doha,Qatar:Association for Computational Linguistics,2014:2105-2114. |
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