Abstract
Emotion cause pair extraction is a sub-task of sentiment analysis tasks, which aims to extract all emotion clauses in a given document and the cause clauses corresponding to the emotion. At present, only the method of sharing parameters at the bottom layer is used to build the connection between tasks, and the shared word encoder is used in word encoding, the phenomenon that the model pays different attention to emotional words and cause words is ignored, and the rich interactive relationship information between the three tasks cannot be fully utilized. This paper proposes a multi-task emotion cause pair extraction model based on feature fusion. The model learns the multi-information at the word level through the feature fusion module, and uses the two tasks of emotion clause extraction and cause clause extraction as auxiliary tasks. The extracted result is transformed into label information, and the method of label embedding is used to integrate into the generation of emotion cause pair representation, thereby improving the effectiveness of the emotion cause pair extraction.The experimental results of the model in the paper on the ECPE data set prove that the Mul-ECPE model has improved in the evaluation indicator of the three tasks compared to the previous series of models.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Lee, S.Y.M., et al.: Emotion cause events: corpus construction and analysis. In: International Conference on Language Resources & Evaluation. DBLP (2012)
Gui, L., et al.: A Question Answering Approach for Emotion Cause Extraction (2017)
Li, X., et al.: 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 (2018)
Yu, X., et al.: Multiple level hierarchical network-based clause selection for emotion cause extraction. IEEE Access (2019)
Xia, R., Zhang, M., Ding, Z.: RTHN: A RNN-Transformer Hierarchical Network for Emotion Cause Extraction (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) (2019)
Wu, S., et al.: A multi-task learning neural network for emotion-cause pair extraction. ECAI IOS Press 2020, 2212–2219 (2020)
Fan, C., et al.: Transition-based directed graph construction for emotion-cause pair extraction. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (2020)
Wei, P., Zhao, J., Mao, W.: Effective inter-clause modeling for end-to-end emotion-cause pair extraction. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (2020)
Tang, H., Ji, D., Zhou, Q.: Joint multi-level attentional model for emotion detection and emotion-cause pair extraction. Neurocomputing 409, 329–340 (2020)
Vaswani, A., et al.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 6000–6010 (2017)
Tang, J., Qu, M., Mei, Q.: Pte: predictive text embedding through large-scale heterogeneous text networks. In: Proceedings of the 21th ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1165–1174 (2015)
Zhang, Y., et al. Deconvolutional paragraph representation learning. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 4172–4182 (2017)
Wang, G., et al.: Joint embedding of words and labels for text classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 2321–2331 (2018)
Zhang, H., et al.: Multi-task label embedding for text classification. Proceedings of the Conference on Empirical Methods in Natural Language Processing. 2018, 4545–4553 (2018)
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, 1003–1012 (2019)
Ding, Z., Xia, R., Yu, J.: ECPE-2D: emotion-cause pair extraction based on joint two-dimensional representation, interaction and prediction. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3161–3170 (2020)
Song, H., et al.: End-to-end emotion-cause pair extraction via learning to link. arXiv preprint arXiv:2002.10710 (2020)
Fan, C., et al.: Multi-task sequence tagging for emotion-cause pair extraction via tag distribution refinement. IEEE/ACM Transactions on Audio, Speech, and Language Processing 29, 2339–2350 (2021)
Li, C., Zhai, R., Zuo, F., Yu, J., Zhang, L.: Mixed Multi-channel Graph Convolution Network on Complex Relation Graph. In: Xing, C., Fu, X., Zhang, Y., Zhang, G., Borjigin, C. (eds.) WISA 2021. LNCS, vol. 12999, pp. 497–504. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-87571-8_43
Chen, X., Li, Q., Wang, J.: Conditional causal relationships between emotions and causes in texts. Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP) 2020, 3111–3121 (2020)
Acknowledgments
This work was supported by the Key projects of the National Natural Science Foundation of China (No.62137001).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Li, Y., Gao, D., Liu, Y., Ju, S. (2022). Emotion Cause Pair Extraction Based on Multitask. In: Zhao, X., Yang, S., Wang, X., Li, J. (eds) Web Information Systems and Applications. WISA 2022. Lecture Notes in Computer Science, vol 13579. Springer, Cham. https://doi.org/10.1007/978-3-031-20309-1_32
Download citation
DOI: https://doi.org/10.1007/978-3-031-20309-1_32
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-20308-4
Online ISBN: 978-3-031-20309-1
eBook Packages: Computer ScienceComputer Science (R0)