Abstract
This article presents a mutual learning method using hierarchical deep semantics for the classification of implicit discourse relations in English. With the absence of explicit discourse markers, traditional discourse techniques mainly concentrate on discrete linguistic features in this task, which always leads to a data sparseness problem. To relieve this problem, we propose a mutual learning neural model that makes use of multilevel semantic information together, including the distribution of implicit discourse relations, the semantics of arguments, and the co-occurrence of phrases and words. During the training process, the predicting targets of the model, which are the probability of the discourse relation type and the distributed representation of semantic components, are learned jointly and optimized mutually. The experimental results show that this method outperforms the previous works, especially in multiclass identification attributed to the hierarchical semantic representations and the mutual learning strategy.
- Y. Bengio, H. Schwenk, J. S. Senécal, F. Morin, and J. L. Gauvain. 2003. A neural probabilistic language model. J. Mach. Learn. Res. 3 (2003), 1137--1155. Google Scholar
Digital Library
- O. Biran and K. Mckeown. 2013. Aggregated word pair features for implicit discourse relation disambiguation. In Proceedings of the Meeting of the Association for Computational Linguistics. 69--73.Google Scholar
- C. E. Braud and P. Denis. 2014. Combining natural and artificial examples to improve implicit discourse relation identification. In Proceedings of the 25th International Conference on Computational Linguistics. 1694--1705.Google Scholar
- L. Carlson, D. Marcu, and M. E. Okurowski. 2003. Building a Discourse-Tagged Corpus in the Framework of Rhetorical Structure Theory. Springer Netherlands, 2655--61.Google Scholar
- R. Fisher and R. Simmons. 2015. Spectral semi-supervised discourse relation classification. In Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing. 89--93.Google Scholar
- M. Lan, Y. Xu, and Z. Niu. 2013. Leveraging synthetic discourse data via multi-task learning for implicit discourse relation recognition. In Proceedings of the Meeting of the Association for Computational Linguistics. 476--485.Google Scholar
- Q. V. Le and T. Mikolov. 2014. Distributed representations of sentences and documents. arXiv preprint arXiv:1405.4053.Google Scholar
- Z. Lin, M. Y. Kan, and H. T. Ng. 2009. Recognizing implicit discourse relations in the penn discourse treebank. In Proceedings of the Conference on Empirical Methods in Natural Language Processing. 343--351. Google Scholar
Digital Library
- D. Marcu and A. Echihabi. 2002. An unsupervised approach to recognizing discourse relations. In Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics. 368--375. Google Scholar
Digital Library
- M. P. Marcus, M. A. Marcinkiewicz, and B. Santorini. 1993. Building a large annotated corpus of English: The penn treebank. MIT Press, 313--330.Google Scholar
- T. Mikolov, I. Sutskever, K. Chen, G. Corrado, and J. Dean. 2013. Distributed representations of words and phrases and their compositionality. arXiv preprint arXiv:1310.4546.Google Scholar
- J. Park and C. Cardie. 2009. Improving implicit discourse relation recognition through feature set optimization. In Proceedings of the 13th Annual Meeting of the Special Interest Group on Discourse and Dialogue. 108--112. Google Scholar
Digital Library
- E. Pitler, M. Raghupathy, H. Mehta, A. Nenkova, A. Lee, and A. Joshi. 2008. Easily identifiable discourse relations. In Companion volume — Posters and Demonstrations (COLING’08), 87--90.Google Scholar
- E. Pitler, A. Louis, and A. Nenkova. 2009. Automatic sense prediction for implicit discourse relations in text. In Proceedings of the 47th Annual Meeting of the ACL and the 4th IJCNLP of the AFNLP. 683--691. Google Scholar
Digital Library
- R. Prasad, E. Miltsakaki, A. Joshi, and B. Webber. 2008. The penn discourse treebank 2.0 annotation manual. Proceedings of Lrec. 2961--2968.Google Scholar
- A. T. Rutherford and N. Xue. 2014. Discovering implicit discourse relations through Brown cluster pair representation and coreference patterns. In Proceedings of the 14th Conference of the European Chapter of the Association for Computational Linguistics. 645--654.Google Scholar
- A. Rutherford and N. Xue. 2015. Improving the inference of implicit discourse relations via classifying explicit discourse connectives. In Human Language Technologies: The 2015 Annual Conference of the North American Chapter of the ACL. 799--808.Google Scholar
- R. Socher, A. Perelygin, J. Y. Wu, J. Chuang, C. D. Manning, A. Y. Ng, and C. Potts. 2013. Recursive deep models for semantic compositionality over a sentiment treebank. In Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing. 1631--1642.Google Scholar
- R. Soricut and D. Marcu. 2003. Sentence level discourse parsing using syntactic and lexical information. In Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology. 149--156. Google Scholar
Digital Library
- P. J. Stone. 1968. The general inquirer: A computer approach to content analysis. Information Storage 8 Retrieva, 375--376.Google Scholar
- W. T. Wang, J. Su, and C. L. Tan. 2010. Kernel based discourse relation recognition with temporal ordering information. In Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics. 710--719. Google Scholar
Digital Library
- B. Wellner, J. Pustejovsky, C. Havasi, A. Rumshisky, and R. Saurí. 2006. Classification of discourse coherence relations: An exploratory study using multiple knowledge sources. In Proceedings of 7th SIGDIAL Workshop on Discourse and Dialogue. 117--125. Google Scholar
Digital Library
- T. Wilson, J. Wiebe, and P. Hoffmann. 2005. Recognizing contextual polarity in phrase-level sentiment analysis. In Proceedings of Human Language Technology Conference and Conference on Empirical Methods in Natural Language Processing. 347--354. Google Scholar
Digital Library
- F. Wolf and E. Gibson. 2005. Representing discourse coherence: A corpus-based study. Computational Linguistics. 249--287. Google Scholar
Digital Library
- M. Yang, T. Cui, and W. Tu. 2015. Ordering-sensitive and Semantic-aware topic modeling. arXiv preprint arXiv:1502.0363.Google Scholar
- Z. M. Zhou, Y. Xu, Z. Y. Niu, M. Lan, J. Su, and C. L. Tan. 2010. Predicting discourse connectives for implicit discourse relation recognition. In Coling 2010: Poster Volume. 1507--1514. Google Scholar
Digital Library
Index Terms
Leveraging Hierarchical Deep Semantics to Classify Implicit Discourse Relations via a Mutual Learning Method
Recommendations
Implicit Discourse Relation Classification Based on Semantic Graph Attention Networks
CSAE '21: Proceedings of the 5th International Conference on Computer Science and Application EngineeringTheimplicit discourse relation classification is of great importance to discourse analysis. It aims to identify the logical relation between sentence pair. Compared with the linear network model, the graph neural network has a more complex structure to ...
Enhanced semantic representation learning for implicit discourse relation classification
AbstractImplicit discourse relation classification is one of the most challenging tasks in discourse parsing. Without connectives as linguistic clues, classifying discourse relations usually requires understanding text semantics at the word level, ...
Learning explicit and implicit Arabic discourse relations
We propose in this paper a supervised learning approach to identify discourse relations in Arabic texts. To our knowledge, this work represents the first attempt to focus on both explicit and implicit relations that link adjacent as well as non adjacent ...






Comments