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Emotion-cause pair extraction with bidirectional multi-label sequence tagging

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Abstract

Emotion-cause pair extraction (ECPE) is a challenging natural language understanding task that aims to extract all potential emotion-cause clause pairs in a document. Existing works have adopted an end-to-end sequence tagging framework to solve this task. However, these approaches struggle to solve the overlapping pairs problem, which means more than one emotion-cause pairs share the same emotion or cause clause. And they suffer from poor generalization ability when the document contains multiple emotion-cause pairs. To address the mentioned issues, we propose a Bidirectional Multi-Label Sequence Tagging (BMST) framework in this paper. Specifically, the proposed method can leverage two sequence tagging modules to achieve bidirectional emotion-cause pair extraction, each of which integrates relevant distance information into the label scheme and employs a multi-label classification strategy. To improve the generalization ability of the model, we propose a joint reasoning mechanism that can exploit the two sequence tagging probabilities to interactively decode emotion-cause pairs. Besides, we design a distance-aware graph attention network to model the relationship between clauses of different distances. Experimental results show that BMST achieves state-of-the-art performance, demonstrating the effectiveness of our model.

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Data will be made available on reasonable request.

Notes

  1. http://news.sina.com.cn/society/

  2. https://pytorch.org

  3. https://huggingface.co/bert-base-chinese

  4. https://github.com/sovrasov/flops-counter.pytorch

References

  1. Lee SYM, Chen Y, Huang C-R (2010) 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

  2. Gui L, Wu D, Xu R, Lu Q, Zhou Y (2016) Event-driven emotion cause extraction with corpus construction. In: Proceedings of the 2016 conference on empirical methods in natural language processing, pp 1639–1649

  3. Xia R, Ding Z (2019) Emotion-cause pair extraction: a new task to emotion analysis in texts. In: Korhonen A, Traum DR, Màrquez L (eds) Proceedings of the 57th conference of the association for computational linguistics, ACL 2019, Florence, Italy, July 28-August 2, 2019, vol 1: Long Papers. Association for computational linguistics, pp 1003–1012

  4. Wu S, Chen F, Wu F, Huang Y, Li X (2020) A multi-task learning neural network for emotion-cause pair extraction. In: ECAI 2020. IOS Press, pp 2212–2219

  5. Ding Z, Xia R, Yu J (2020) ECPE-2D: emotion-cause pair extraction based on joint two-dimensional representation, interaction and prediction. In: Jurafsky D, Chai J, Schluter N, Tetreault JR (eds) Proceedings of the 58th annual meeting of the association for computational linguistics, ACL 2020, Online, July 5-10, 2020. Association for computational linguistics, pp 3161–3170

  6. Wei P, Zhao J, Mao W (2020) Effective inter-clause modeling for end-to-end emotion-cause pair extraction. In: Jurafsky D, Chai J, Schluter N, Tetreault JR (eds) Proceedings of the 58th annual meeting of the association for computational linguistics, ACL 2020, Online, July 5-10, 2020. Association for computational linguistics, pp 3171–3181

  7. Chen F, Shi Z, Yang Z, Huang Y (2022) Recurrent synchronization network for emotion-cause pair extraction. Knowl Based Syst 238:107965

    Article  Google Scholar 

  8. Cheng Z, Jiang Z, Yin Y, Li N, Gu Q (2021) A unified target-oriented sequence-to-sequence model for emotion-cause pair extraction. IEEE ACM Trans Audio Speech Lang Process 29:2779–2791

    Article  Google Scholar 

  9. Fan C, Yuan C, Gui L, Zhang Y, Xu R (2021) Multi-task sequence tagging for emotion-cause pair extraction via tag distribution refinement. IEEE ACM Trans Audio Speech Lang Process 29:2339–2350

    Article  Google Scholar 

  10. Gui L, Yuan L, Xu R, Liu B, Lu Q, Zhou Y (2014) Emotion cause detection with linguistic construction in chinese weibo text. In: Zong C, Nie J, Zhao D, Feng Y (eds) Natural language processing and Chinese computing - third CCF conference, NLPCC 2014, Shenzhen, China, December 5-9, 2014. Proceedings. Communications in computer and information science, vol 496. Springer, pp 457–464

  11. Ghazi D, Inkpen D, Szpakowicz S (2015) Detecting emotion stimuli in emotion-bearing sentences. In: Gelbukh AF (ed) Computational linguistics and intelligent text processing - 16th international conference, CICLing 2015, Cairo, Egypt, April 14-20, 2015, Proceedings, Part II. Lecture notes in computer science, vol 9042. Springer, pp 152–165

  12. Gao K, Xu H, Wang J (2015) Emotion cause detection for chinese micro-blogs based on ecocc model. In: Pacific-Asia conference on knowledge discovery and data mining. Springer, pp 3–14

  13. Chen Y, Hou W, Cheng X, Li S (2018) Joint learning for emotion classification and emotion cause detection. In: Proceedings of the 2018 conference on empirical methods in natural language processing, pp 646–651

  14. Liu H, Fang S, Zhang Z, Li D, Lin K, Wang J (2022) Mfdnet: collaborative poses perception and matrix fisher distribution for head pose estimation. IEEE Trans Multim 24:2449–2460

    Article  Google Scholar 

  15. Liu T, Yang B, Liu H, Ju J, Tang J, Subramanian S, Zhang Z (2022) Gmdl: toward precise head pose estimation via gaussian mixed distribution learning for students’ attention understanding. Infrared Physics & Technology 122:104099

    Article  Google Scholar 

  16. Liu H, Zheng C, Li D, Shen X, Lin K, Wang J, Zhang Z, Zhang Z, Xiong NN (2022) EDMF: efficient deep matrix factorization with review feature learning for industrial recommender system. IEEE Trans Ind Informatics 18(7):4361–4371

    Article  Google Scholar 

  17. Liu H, Zhang C, Deng Y, Xie B, Liu T, Zhang Z, Li Y-F (2023) Transifc: invariant cues-aware feature concentration learning for efficient fine-grained bird image classification. IEEE Transactions on Multimedia

  18. Chen Y, Lee SYM, Li S, Huang C-R (2010) Emotion cause detection with linguistic constructions. In: Proceedings of the 23rd international conference on computational linguistics (Coling 2010), pp 179–187

  19. Gui L, Hu J, He Y, Xu R, Lu Q, Du J (2017) A question answering approach for emotion cause extraction. In: Palmer M, Hwa R, Riedel S (eds) Proceedings of the 2017 conference on empirical methods in natural language processing, EMNLP 2017, Copenhagen, Denmark, September 9-11, 2017. Association for computational linguistics, pp 1593–1602

  20. Li X, Song K, Feng S, Wang D, Zhang Y (2018) A co-attention neural network model for emotion cause analysis with emotional context awareness. In: Riloff E, Chiang D, Hockenmaier J, Tsujii J (eds) Proceedings of the 2018 conference on empirical methods in natural language processing, Brussels, Belgium, October 31 - November 4, 2018. Association for computational linguistics, pp 4752–4757

  21. Fan C, Yan H, Du J, Gui L, Bing L, Yang M, Xu R, Mao R (2019) A knowledge regularized hierarchical approach for emotion cause analysis. In: Inui K, Jiang J, Ng V, Wan X (eds) 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, Hong Kong, China, November 3-7, 2019. Association for computational linguistics, pp 5613–5623

  22. Xu B, Lin H, Lin Y, Diao Y, Yang L, Xu K (2019) Extracting emotion causes using learning to rank methods from an information retrieval perspective. IEEE Access 7:15573–15583

    Article  Google Scholar 

  23. Hu G, Zhao Y, Lu G, Yin F, Chen J (2022) An exploration of mutual information based on emotion-cause pair extraction. Knowl Based Syst 256:109822

    Article  Google Scholar 

  24. Yu J, Liu W, He Y, Zhang C (2021) A mutually auxiliary multitask model with self-distillation for emotion-cause pair extraction. IEEE Access 9:26811–26821

    Article  Google Scholar 

  25. Sun Q, Yin Y, Yu H (2021) A dual-questioning attention network for emotion-cause pair extraction with context awareness. In: 2021 International joint conference on neural networks (IJCNN). IEEE, pp 1–8

  26. Liu J, Zhang Z, Guo Z, Jin L, Li X, Wei K, Sun X (2022) Kept: knowledge enhanced prompt tuning for event causality identification. Knowl-Based Syst, 110064

  27. Liu T, Wang J, Yang B, Wang X (2021) Facial expression recognition method with multi-label distribution learning for non-verbal behavior understanding in the classroom. Infrared Physics & Technology 112:103594

    Article  Google Scholar 

  28. Liu H, Liu T, Zhang Z, Sangaiah AK, Yang B, Li Y (2022) ARHPE: asymmetric relation-aware representation learning for head pose estimation in industrial human-computer interaction. IEEE Trans Ind Informatics 18(10):7107–7117

    Article  Google Scholar 

  29. Liu H, Nie H, Zhang Z, Li Y (2021) Anisotropic angle distribution learning for head pose estimation and attention understanding in human-computer interaction. Neurocomputing 433:310–322

    Article  Google Scholar 

  30. Liu H, Liu T, Chen Y, Zhang Z, Li Y-F (2022) Ehpe: skeleton cues-based gaussian coordinate encoding for efficient human pose estimation. IEEE Transactions on Multimedia

  31. Chen Y, Hou W, Li S, Wu C, Zhang X (2020) End-to-end emotion-cause pair extraction with graph convolutional network. In: Scott D, Bel N, Zong C (eds) Proceedings of the 28th international conference on computational linguistics, COLING 2020, Barcelona, Spain (Online), December 8-13, 2020. International committee on computational linguistics, pp 198–207

  32. Bao Y, Ma Q, Wei L, Zhou W, Hu S (2022) Multi-granularity semantic aware graph model for reducing position bias in emotion cause pair extraction. In: Muresan S, Nakov P, Villavicencio A (eds) Findings of the association for computational linguistics: ACL 2022, Dublin, Ireland, May 22–27, 2022. Association for computational linguistics, pp 1203–1213

    Google Scholar 

  33. Devlin J, Chang M, Lee K, Toutanova K (2019) BERT: pre-training of deep bidirectional transformers for language understanding. In: Burstein J, Doran C, Solorio T (eds) Proceedings of the 2019 conference of the North American chapter of the association for computational linguistics: human language technologies, NAACL-HLT 2019, Minneapolis, MN, USA, June 2-7, 2019, vol 1 (Long and Short Papers). Association for computational linguistics, pp 4171–4186

  34. Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9(8):1735–1780

    Article  Google Scholar 

  35. Chen H, Yang X, Li X (2022) Learning a general clause-to-clause relationships for enhancing emotion-cause pair extraction. CoRR arXiv:2208.13549

  36. Veličković P, Cucurull G, Casanova A, Romero A, Liò P, Bengio Y (2018) Graph attention networks. In: International conference on learning representations

  37. Liu H, Zheng C, Li D, Zhang Z, Lin K, Shen X, Xiong NN, Wang J (2022) Multi-perspective social recommendation method with graph representation learning. Neurocomputing 468:469–481

    Article  Google Scholar 

  38. Li Z, Liu H, Zhang Z, Liu T, Xiong NN (2022) Learning knowledge graph embedding with heterogeneous relation attention networks. IEEE Trans Neural Networks Learn Syst 33(8):3961–3973

    Article  MathSciNet  Google Scholar 

  39. Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser L, Polosukhin I (2017) Attention is all you need. In: Guyon I, von Luxburg U, Bengio S, Wallach HM, Fergus R, Vishwanathan SVN, Garnett R (eds) Advances in neural information processing systems 30: annual conference on neural information processing systems 2017, December 4-9, 2017, Long Beach, CA, USA, pp 5998–6008

  40. Fan C, Yuan C, Du J, Gui L, Yang M, Xu R (2020) Transition-based directed graph construction for emotion-cause pair extraction. In: Jurafsky D, Chai J, Schluter N, Tetreault JR (eds) Proceedings of the 58th annual meeting of the association for computational linguistics, ACL 2020, Online, July 5-10, 2020. Association for computational linguistics, pp 3707–3717

  41. Kingma DP, Ba J (2015) Adam: a method for stochastic optimization. In: Bengio Y, LeCun Y (eds) 3rd International conference on learning representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference track proceedings

  42. Song H, Zhang C, Li Q, Song D (2020) End-to-end emotion-cause pair extraction via learning to link. CoRR arXiv:2002.10710

  43. Tang H, Ji D, Zhou Q (2020) Joint multi-level attentional model for emotion detection and emotion-cause pair extraction. Neurocomputing 409:329–340

    Article  Google Scholar 

  44. Yuan C, Fan C, Bao J, Xu R (2020) Emotion-cause pair extraction as sequence labeling based on a novel tagging scheme. In: Proceedings of the 2020 conference on empirical methods in natural language processing (EMNLP), pp 3568–3573

  45. Ding Z, Xia R, Yu J (2020) End-to-end emotion-cause pair extraction based on sliding window multi-label learning. In: Webber B, Cohn T, He Y, Liu Y (eds) Proceedings of the 2020 conference on empirical methods in natural language processing, EMNLP 2020, Online, November 16-20, 2020. Association for computational linguistics, pp 3574–3583

  46. Xia R, Zhang M, Ding Z (2019) RTHN: a rnn-transformer hierarchical network for emotion cause extraction. In: Kraus S (ed) Proceedings of the twenty-eighth international joint conference on artificial intelligence, IJCAI 2019, Macao, China, August 10-16, 2019, pp 5285–5291. ijcai.org

  47. Li X, Feng S, Wang D, Zhang Y (2019) Context-aware emotion cause analysis with multi-attention-based neural network. Knowl Based Syst 174:205–218

    Article  Google Scholar 

  48. Yan H, Gui L, Pergola G, He Y (2021) Position bias mitigation: a knowledge-aware graph model for emotion cause extraction. In: Zong C, Xia F, Li W, Navigli R (eds) Proceedings of the 59th annual meeting of the association for computational linguistics and the 11th international joint conference on natural language processing, ACL/IJCNLP 2021, (vol 1: Long Papers), Virtual Event, August 1-6, 2021. Association for computational linguistics, pp 3364–3375

  49. Xu B, Lin H, Lin Y, Xu K (2021) Two-stage supervised ranking for emotion cause extraction. Knowl Based Syst 228:107225

    Article  Google Scholar 

  50. Hu G, Lu G, Zhao Y (2021) Bidirectional hierarchical attention networks based on document-level context for emotion cause extraction. In: Findings of the association for computational linguistics: EMNLP 2021, pp 558–568

  51. Hu G, Lu G, Zhao Y (2021) FSS-GCN: a graph convolutional networks with fusion of semantic and structure for emotion cause analysis. Knowl Based Syst 212:106584

    Article  Google Scholar 

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (No.62206267).

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Authors and Affiliations

  1. Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, 100190, China

    Jintao Liu, Zequn Zhang, Zhi Guo, Li Jin, Xiaoyu Li, Kaiwen Wei & Xian Sun

  2. Key Laboratory of Network Information System Technology(NIST), Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, 100190, China

    Jintao Liu, Zequn Zhang, Zhi Guo, Li Jin, Xiaoyu Li, Kaiwen Wei & Xian Sun

  3. University of Chinese Academy of Sciences, Beijing, 100190, China

    Jintao Liu, Kaiwen Wei & Xian Sun

  4. School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing, 100190, China

    Jintao Liu & Kaiwen Wei

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  1. Jintao Liu
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Correspondence to Zequn Zhang.

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Liu, J., Zhang, Z., Guo, Z. et al. Emotion-cause pair extraction with bidirectional multi-label sequence tagging. Appl Intell 53, 30400–30415 (2023). https://doi.org/10.1007/s10489-023-05140-z

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