Abstract
We analyze narrative text spans (also named as arguments) in this paper, and merely concentrate on the recognition of semantic relations between them. Because larger-grain linguistic units (such as phrase, chunk) are inherently cohesive in semantics, they generally contribute more than words in the representation of sentence-level text spans. On the basis of it, we propose the multi-grain representation learning method, which uses different convolution filters to form larger-grain linguistic units. Methodologically, Bi-LSTM based attention mechanism is used to strengthen suitable-grain representation, which is concatenated with word-level representation to form multi-grain representation. In addition, we employ bidirectional interactive attention mechanism to focus on the key information in the arguments. Experimental results on the Penn Discourse TreeBank show that the proposed method is effective.
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References
Anderson, P., et al.: Bottom-up and top-down attention for image captioning and visual question answering. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6077–6086 (2018)
Chen, J., Zhang, Q., Liu, P., Qiu, X., Huang, X.: Implicit discourse relation detection via a deep architecture with gated relevance network. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), vol. 1, pp. 1726–1735 (2016)
Do, Q.X., Chan, Y.S., Roth, D.: Minimally supervised event causality identification. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing, pp. 294–303. Association for Computational Linguistics (2011)
Guo, F., He, R., Jin, D., Dang, J., Wang, L., Li, X.: Implicit discourse relation recognition using neural tensor network with interactive attention and sparse learning. In: Proceedings of the 27th International Conference on Computational Linguistics, pp. 547–558 (2018)
Ji, Y., Zhang, G., Eisenstein, J.: Closing the gap: domain adaptation from explicit to implicit discourse relations. In: Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, pp. 2219–2224 (2015)
Lei, W., Wang, X., Liu, M., Ilievski, I., He, X., Kan, M.Y.: Swim: a simple word interaction model for implicit discourse relation recognition. In: IJCAI, pp. 4026–4032 (2017)
Lei, W., Xiang, Y., Wang, Y., Zhong, Q., Liu, M., Kan, M.Y.: Linguistic properties matter for implicit discourse relation recognition: combining semantic interaction, topic continuity and attribution. In: Thirty-Second AAAI Conference on Artificial Intelligence (2018)
Litkowski, K.C.: Question-answering using semantic relation triples. In: TREC. Citeseer (1999)
Liu, Y., Li, S.: Recognizing implicit discourse relations via repeated reading: neural networks with multi-level attention. arXiv preprint arXiv:1609.06380 (2016)
Pitler, E., Louis, A., Nenkova, A.: Automatic sense prediction for implicit discourse relations in text. In: 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: Volume 2-Volume 2, pp. 683–691. Association for Computational Linguistics (2009)
Pitler, E., Nenkova, A.: Revisiting readability: a unified framework for predicting text quality. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing, pp. 186–195. Association for Computational Linguistics (2008)
Qian, N.: On the momentum term in gradient descent learning algorithms. Neural Netw. 12(1), 145–151 (1999)
Qin, L., Zhang, Z., Zhao, H.: A stacking gated neural architecture for implicit discourse relation classification. In: Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pp. 2263–2270 (2016)
Qin, L., Zhang, Z., Zhao, H., Hu, Z., Xing, E.P.: Adversarial connective-exploiting networks for implicit discourse relation classification. arXiv preprint arXiv:1704.00217 (2017)
Rutherford, A., Xue, N.: 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, pp. 645–654 (2014)
Rutherford, A., Xue, N.: Improving the inference of implicit discourse relations via classifying explicit discourse connectives. In: Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 799–808 (2015)
Tuason, R., Grazian, D., Kondo, G.: Bidaf model for question answering. Table III EVALUATION ON MRC MODELS (TEST SET). Search Zhidao All (2017)
Xiong, D., Ding, Y., Zhang, M., Tan, C.L.: Lexical chain based cohesion models for document-level statistical machine translation. In: Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing, pp. 1563–1573 (2013)
Yang, Z., et al.: HotpotQA: a dataset for diverse, explainable multi-hop question answering. arXiv preprint arXiv:1809.09600 (2018)
Zhou, L., Li, B., Gao, W., Wei, Z., Wong, K.F.: Unsupervised discovery of discourse relations for eliminating intra-sentence polarity ambiguities. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing, pp. 162–171. Association for Computational Linguistics (2011)
Zhou, P., et al.: Attention-based bidirectional long short-term memory networks for relation classification. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), vol. 2, pp. 207–212 (2016)
Acknowledgments
This research work is supported by National Natural Science Foundation of China (Grants No. 61672367, No. 61672368, No. 61751206). The authors would like to thank the anonymous reviewers for their insightful comments and suggestions.
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Sun, Y., Ruan, H., Hong, Y., Wu, C., Zhang, M., Zhou, G. (2019). Multi-grain Representation Learning for Implicit Discourse Relation Recognition. In: Tang, J., Kan, MY., Zhao, D., Li, S., Zan, H. (eds) Natural Language Processing and Chinese Computing. NLPCC 2019. Lecture Notes in Computer Science(), vol 11838. Springer, Cham. https://doi.org/10.1007/978-3-030-32233-5_56
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