Abstract
Emotion-cause pair extraction (ECPE) aims to extract all potential pairs of emotions and corresponding causes in a document. It has an advantage over traditional emotion cause extraction (ECE) that it does not require annotating emotions manually. Existing methods for ECPE task are based on two-step framework. However, they ignore the fact that the emotion-cause pair is regarded as a whole unit and there are cascading errors in two-step framework. In this paper, we propose an end-to-end hierarchical neural network model, which directly extracts emotion-cause pairs and enhances mutual interaction between emotions and causes via multi-task learning. In addition, we introduce a scope controller to constrain the emotion-cause pair predictions in a high probability area, according to the position correlation between emotions and causes. The experimental results demonstrate that our method achieves the state-of-the-art performance and improves F-measure by 2.24%.
R. Fan and Y. Wang—contributed equally to this work and should be regarded as co-first authors.
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References
Xia, R., Ding, Z.: Emotion-Cause pair extraction: a new task to emotion analysis in texts. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics ACL 2019, Florence, Italy, 28 July–2 August 2019 (2019)
Gui, L., Wu, D., Xu, R., Lu, Q., Zhou, Y.: Event-driven emotion cause extraction with corpus construction. In: Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing EMNLP 2016, Austin, Texas, USA, 1–4 November 2016, pp. 1639–1649 (2016)
Lee, S.Y.M., Chen, Y., Huang, C.R.: A text-driven rule-based system for emotion cause detection. In: Proceedings of the NAACL HLT 2010 Workshop on Computational Approaches to Analysis and Generation of Emotion in Text, pp. 45–53. Association for Computational Linguistics (2010)
Chen, Y., Lee, S.Y.M., Li, S., Huang, C.R.:Emotion cause detection with linguistic constructions. In: Proceedings of the Conference 23rd International Conference on Computational Linguistics COLING 2010, 23–27 August 2010, Beijing, China, pp. 179–187 (2010)
Neviarouskaya, A., Aono, M.: Extracting causes of emotions from text. In: Proceedings of the Sixth International Joint Conference on Natural Language Processing (IJCNLP), pp. 932–936 (2013)
Gao, K., Xu, H., Wang, J.: Emotion cause detection for chinese micro-blogs based on ECOCC model. In: Cao, T., Lim, E.-P., Zhou, Z.-H., Ho, T.-B., Cheung, D., Motoda, H. (eds.) PAKDD 2015. LNCS (LNAI), vol. 9078, pp. 3–14. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-18032-8_1
Gao, K., Xu, H., Wang, J.: A rule- based approach to emotion cause detection for chinese micro-blogs. Expert Syst. Appl. 42(9), 4517–4528 (2015)
Yada, S., Ikeda, K., Hoashi, K., Kageura, K.: A bootstrap method for automatic rule acquisition on emotion cause extraction. In: IEEE International Conference on Data Mining Workshops, pp. 414–421 (2017)
Ghazi, D., Inkpen, D., Szpakowicz, S.: Detecting emotion stimuli in emotion-bearing sentences. In: Gelbukh, A. (ed.) CICLing 2015. LNCS, vol. 9042, pp. 152–165. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-18117-2_12
Song, S., Meng, Y.: Detecting concept-level emotion cause in microblogging. In: Proceedings of the 24th International Conference on World Wide Web (WWW), pp. 119–120 (2015)
Xu, B., Lin, H., Lin, Y., Diao, Y., Yang, L., Xu, K.: Extracting emotion causes using learning to rank methods from an information retrieval perspective. IEEE Access 7, 15573–15583 (2019)
Gui, L., Hu, J., He, Y., Xu, R., Lu, Q., Du, J.: A question answering approach for emotion cause extraction. In: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing EMNLP 2017, Copenhagen, Denmark, 9–11 September 2017, pp. 1593–1602 (2017)
Li, X., Song, K., Feng, S., Wang, D., Zhang, Y.: A co-attention neural network model for emotion cause analysis with emotional context awareness. In: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, Brussels, Belgium, 31–October–4 November 2018, pp. 4752–4757 (2018)
Chen, Y., Hou, W., Cheng, X., Li, S.: Joint learning for emotion classification and emotion cause detection. In: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, Brussels, Belgium, 31 October–4 November 2018, pp. 646–651 (2018)
Yu, X., Rong, W., Zhang, Z., Ouyang, Y., Xiong, Z.: Multiple level hierarchical network-based clause selection for emotion cause extraction. IEEE Access 7, 9071–9079 (2019)
Li, X., Feng, S., Wang, D., Zhang, Y.: Context-aware emotion cause analysis with multi-attention-based neural network. Knowl. Based Syst. 174, 205–218 (2019)
Fan, C., et al.: A knowledge regularized hierarchical approach for emotion cause analysis. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 5618–5628 (2019)
Gui, L., Yuan, L., Xu, R., Liu, B., Lu, Q., Zhou, Yu.: Emotion cause detection with linguistic construction in chinese weibo text. In: Zong, C., Nie, J.-Y., Zhao, D., Feng, Y. (eds.) NLPCC 2014. CCIS, vol. 496, pp. 457–464. Springer, Heidelberg (2014). https://doi.org/10.1007/978-3-662-45924-9_42
Gui, L., Xu, R., Lu, Q., Wu, D., Zhou, Yu.: Emotion cause extraction, a challenging task with corpus construction. In: Li, Y., Xiang, G., Lin, H., Wang, M. (eds.) SMP 2016. CCIS, vol. 669, pp. 98–109. Springer, Singapore (2016). https://doi.org/10.1007/978-981-10-2993-6_8
Cheng, X., Chen, Y., Cheng, B., Li, S., Zhou, G.: An emotion cause corpus for chinese microblogs with multiple-user structures. ACM Trans. Asian Low-Resource Lang. Inf. Process. 17(1), 6 (2017)
Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. In: Advances in Neural Information Processing Systems (NIPS), pp. 3111–3119 (2013)
Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. In: Conference Track Proceedings 3rd Inter-national Conference on Learning Representations ICLR 2015, San Diego, CA, USA, 7–9 May 2015
Acknowledgments
This research is supported by the National Natural Science Foundation of China (61532008), the National Natural Science Foundation of China (61572223), the National Key Research and Development Program of China (2017YFC0909502), and Wuhan Science and Technology Program (2019010701011392).
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Fan, R., Wang, Y., He, T. (2020). An End-to-End Multi-task Learning Network with Scope Controller for Emotion-Cause Pair Extraction. In: Zhu, X., Zhang, M., Hong, Y., He, R. (eds) Natural Language Processing and Chinese Computing. NLPCC 2020. Lecture Notes in Computer Science(), vol 12430. Springer, Cham. https://doi.org/10.1007/978-3-030-60450-9_60
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