Abstract
A lack of labeled corpora obstructs the research progress on implicit discourse relation recognition (DRR) for Chinese, while there are some available discourse corpora in other languages, such as English. In this paper, we propose a cross-lingual implicit DRR framework that exploits an available English corpus for the Chinese DRR task. We use machine translation to generate Chinese instances from a labeled English discourse corpus. In this way, each instance has two independent views: Chinese and English views. Then we train two classifiers in Chinese and English in a co-training way, which exploits unlabeled Chinese data to implement better implicit DRR for Chinese. Experimental results demonstrate the effectiveness of our method.
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References
Biran O, McKeown K, 2013. Aggregated word pair features for implicit discourse relation disambiguation. Proc 51st Annual Meeting of the Association for Computational Linguistics, p.69–73. https://doi.org/10.7916/D8PN9FZ4
Blum A, Mitchell T, 1998. Combining labeled and unlabeled data with cotraining. Proc 11th Annual Conf on Computational Learning Theory, p.92-100. https://doi.org/10.1145/279943.279962
Braud C, Denis P, 2015. Comparing word representations for implicit discourse relation classification. Proc Conf on Empirical Methods in Natural Language Processing, p.2201–2211. https://doi.org/10.18653/v1/d15-1262
Carlson L, Marcu D, Okurowski M, 2001. Building a discourse-tagged corpus in the framework of rhetorical structure theory. Proc 2nd SIGDIAL Workshop on Discourse and Dialogue, p.1–10. https://doi.org/10.3115/1118078.1118083
Chen J, Zhang Q, Liu P, et al., 2016. Implicit discourse relation detection via a deep architecture with gated relevance network. Proc 54th Annual Meeting of the Association for Computational Linguistics, p.1726–1735. https://doi.org/10.18653/v1/p16-1163
Chen L, 2006. English and Chinese Discourse Structure Dimension Theory and Practice. PhD Thesis, Shanghai International Studies University, China.
Chiarcos C, 2012. Towards the unsupervised acquisition of discourse relations. Proc 50th Annual Meeting of the Association for Computational Linguistics, p.213–217.
Cimiano P, Reyle U, Šaric J, 2005. Ontology-driven discourse analysis for information extraction. Data Knowl Eng, 55(1):59–83. https://doi.org/10.1016/j.datak.2004.11.009
Clark S, Curran J, Osborne M, 2003. Bootstrapping POStaggers using unlabelled data. Proc 7th Conf on Natural Language Learning, p.49–55. https://doi.org/10.3115/1119176.1119183
Guzmán F, Joty S, Màrquez L, et al., 2014. Using discourse structure improves machine translation evaluation. Proc 52nd Annual Meeting of the Association for Computational Linguistics, p.687–698. https://doi.org/10.3115/v1/p14-1065
Hernault H, Bollegala D, Ishizuka M, 2010. Towards semisupervised classification of discourse relations using feature correlations. Proc SIGDIAL Conf and the 11th Annual Meeting of the Special Interest Group on Discourse and Dialogue, p.55–58.
Huang H, Chen H, 2011. Chinese discourse relation recognition. Proc 5th Int Joint Conf on Natural Language Processing, p.1442–1446.
Jain S, Batra S, 2015. Cross lingual sentiment analysis using modified BRAE. Proc Conf on Empirical Methods in Natural Language Processing, p.159–168. https://doi.org/10.18653/v1/d15-1016
Ji Y, Eisenstein J, 2015. One vector is not enough: entityaugmented distributed semantics for discourse relations. Trans Assoc Comput Ling, 3:329–344.
Ji Y, Haffari G, Eisenstein J, 2016. A latent variable recurrent neural network for discourse relation language models. Proc Conf North American Chapter of the Association for Computational Linguistics on Human Language Technologies, p.332–342. https://doi.org/10.18653/v1/n16-1037
Laali M, Kosseim L, 2014. Inducing discourse connectives from parallel texts. Proc 25th Int Conf on Computational Linguistics, p.610–619.
Lan M, Xu Y, Niu Z, 2013. Leveraging synthetic discourse data via multi-task learning for implicit discourse relation recognition. Proc 51st Annual Meeting of the Association for Computational Linguistics, p.476–485.
Li J, Carpuat M, Nenkova A, 2014. Cross-lingual discourse relation analysis: a corpus study and a semi-supervised classification system. Proc 25th Int Conf on Computational Linguistics, p.577–587.
Li Y, Feng W, Sun J, et al., 2014. Building Chinese discourse corpus with connective-driven dependency tree structure. Proc Conf on Empirical Methods in Natural Language Processing, p.2105–2114. https://doi.org/10.3115/v1/d14-1224
Lin Z, Kan M, Ng H, 2009. Recognizing implicit discourse relations in the Penn discourse treebank. Proc Conf on Empirical Methods in Natural Language Processing, p.343–351. https://doi.org/10.3115/1699510.1699555
Liu Y, Li S, Zhang X, et al., 2016. Implicit discourse relation classification via multi-task neural networks. Proc 30th Conf on Artificial Intelligence, p.2750–2756.
Louis A, Joshi A, Prasad R, et al., 2010. Using entity features to classify implicit discourse relations. Proc 11th Annual Meeting of the Special Interest Group on Discourse and Dialogue, p.59–62.
Marcu D, Echihabi A, 2002. An unsupervised approach to recognizing discourse relations. Proc 40th Annual Meeting of the Association for Computational Linguistics, p.368–375. https://doi.org/10.3115/1073083.1073145
Miltsakaki E, Dinesh N, Prasad R, et al., 2005. Experiments on sense annotations and sense disambiguation of discourse connectives. Proc 4th Workshop on Treebanks and Linguistic Theories, p.1–13.
Ming Y, 2008. Rhetorical structure annotation of Chinese news commentaries. J Chin Inform Proc, 22(4):19–23.
Ng V, Cardie C, 2003. Weakly supervised natural language learning without redundant views. Proc Conf North American Chapter of the Association for Computational Linguistics on Human Language Technology, p.94–101. https://doi.org/10.3115/1073445.1073468
Park J, Cardie C, 2012. Improving implicit discourse relation recognition through feature set optimization. Proc 13th Annual Meeting of the Special Interest Group on Discourse and Dialogue, p.108–112.
Pitler E, Nenkova A, 2009. Using syntax to disambiguate explicit discourse connectives in text. Proc ACL-IJCNLP Conf, p.13–16. https://doi.org/10.3115/1667583.1667589
Pitler E, Louis A, Nenkova A, 2009. Automatic sense prediction for implicit discourse relations in text. Proc of the Joint Conf 47th Annual Meeting of the ACL and the 4th Int Joint Conf on Natural Language Processing of the AFNLP, p.683–691. https://doi.org/10.3115/1690219.1690241
Prasad R, Dinesh N, Lee A, et al., 2008. The Penn discourse treebank 2.0. Proc Int Conf on Language Resources and Evaluation, p.2961–2968.
Qian L, Hui H, Hu Y, et al., 2014. Bilingual active learning for relation classification via pseudo parallel corpora. Proc 52nd Annual Meeting of the Association for Computational Linguistics, p.582–592. https://doi.org/10.3115/v1/p14-1055
Qin L, Zhang Z, Zhao H, 2016. A stacking gated neural architecture for implicit discourse relation classification. Proc Conf on Empirical Methods in Natural Language Processing, p.2263–2270. https://doi.org/10.18653/v1/d16-1246
Rutherford A, Xue N, 2014. Discovering implicit discourse relations through brown cluster pair representation and coreference patterns. Proc 14th Conf European Chapter of the Association for Computational Linguistics, p.645–654. https://doi.org/10.3115/v1/e14-1068
Rutherford A, Xue N, 2015. Improving the inference of implicit discourse relations via classifying explicit discourse connectives. Proc Conf of the North American Chapter of the Association for Computational Linguistics on Human Language Technologies, p.799–808. https://doi.org/10.3115/v1/n15-1081
Rutherford A, Demberg V, Xue N, 2016. Neural network models for implicit discourse relation classification in English and Chinese without surface features. http://arxiv.org/abs/1606.01990
Sarkar A, 2001. Applying co-training methods to statistical parsing. Proc 2nd Meeting of the North American Chapter of the Association for Computational Linguistics on Language Technologies, p.1–8. https://doi.org/10.3115/1073336.1073359
Sporleder C, Lascarides A, 2008. Using automatically labelled examples to classify rhetorical relations: an assessment. Nat Lang Eng, 14:369–416. https://doi.org/10.1017/S1351324906004451
Verberne S, Boves L, Oostdijk N, et al., 2007. Evaluating discourse-based answer extraction for why-question answering. Proc 30th Annual Int ACM SIGIR Conf on Research and Development in Information Retrieval, p.735–736. https://doi.org/10.1145/1277741.1277883
Wan X, 2009. Co-training for cross-lingual sentiment classification. Proc of the Joint Conf 47th Annual Meeting of the ACL and the 4th Int Joint Conf on Natural Language Processing of the AFNLP, p.235–243.
Wang W, Su J, Tan C, 2010. Kernel based discourse relation recognition with temporal ordering information. Proc 48th Annual Meeting of the Association for Computational Linguistics, p.710–719.
Wang X, Li S, Li J, et al., 2012. Implicit discourse relation recognition by selecting typical training examples. Proc of COLING, p.2757–2772.
Xue N, 2005. Annotating discourse connectives in the Chinese treebank. Proc Workshop on Frontiers in Corpus Annotations II: Pie in the Sky, p.84–91. https://doi.org/10.3115/1608829.1608841
Zhang B, Su J, Xiong D, et al., 2015. Shallow convolutional neural network for implicit discourse relation recognition. Proc Conf on Empirical Methods in Natural Language Processing, p.2230–2235. https://doi.org/10.18653/v1/d15-1266
Zhang B, Xiong D, Su J, et al., 2016. Variational neural discourse relation recognizer. Proc Conf on Empirical Methods in Natural Language Processing, p.382–391. https://doi.org/10.18653/v1/d16-1037
Zhang M, Song Y, Qin B, et al., 2013. Chinese discourse relation recognition. J Chin Inform Proc, 27(6):51–57.
Zhang M, Qin B, Liu T, 2014. Chinese discourse relation hierarchy and annotation. J Chin Inform Proc, 28(2):28–36.
Zhou L, GaoW, Li B, et al., 2012. Cross-lingual identification of ambiguous discourse connectives for resource poor language. Proc COLING, p.1409–1418.
Zhou Y, Xue N, 2015. The Chinese discourse treebank: a Chinese corpus annotated with discourse relations. Lang Res Eval, 49(2):397–431. https://doi.org/10.1007/s10579-014-9290-3
Zhou Z, Xu Y, Niu Z, et al., 2010. Predicting discourse connectives for implicit discourse relation recognition. 23rd Int Conf on Computational Linguistics, p.1507–1514.
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Project supported by the National Natural Science Foundation of China (No. 61672440), the Natural Science Foundation of Fujian Province, China (No. 2016J05161), the Research Fund of the State Key Laboratory for Novel Software Technology in Nanjing University, China (No. KFKT2015B11), the Scientific Research Project of the National Language Committee of China (No. YB135-49), and the Fundamental Research Funds for the Central Universities, China (No. ZK1024)
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Lu, Yj., Xu, M., Wu, Cx. et al. Cross-lingual implicit discourse relation recognition with co-training. Frontiers Inf Technol Electronic Eng 19, 651–661 (2018). https://doi.org/10.1631/FITEE.1601865
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DOI: https://doi.org/10.1631/FITEE.1601865