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Emotion-cause pair extraction based on interactive attention

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Abstract

In recent years, a new fine-grained task has been proposed in the field of sentiment analysis, the emotion-cause pair extraction (ECPE) task, whose purpose is to extract all emotions and their causes from a document. Most of existing methods produce effective emotion-cause pairs by filtering all possible pairs. However, this types of methods ignore the relationship between emotion clauses and cause clauses when learning the representations of emotions and causes clauses. In order to solve the above problem, we propose an end-to-end framework, which uses interactive attention and its fusion mechanism to learn the relationship between emotions and causes, and then pair them. Experimental results on quasi-base corpus shows our proposed method outperform the state-of-the-art baseline.

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Acknowledgements

This work was supported by the Natural Science Foundation of China (No. 61967006), (No. 62067002) and (No.62062033), The Project of Jiangxi Provincial Department of Education (No. GJJ219302), and the Natural Science Foundation of Jiangxi Province (No.20212BAB202008, No.20192ACBL21006). We thank the anonymous reviewers for their valuable comments.

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Authors and Affiliations

  1. School of Software Department, East China Jiaotong University, Nanchang, 330013, China

    Weichun Huang, Yixue Yang, Xiaohui Huang, Zhiying Peng & Liyan Xiong

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  1. Weichun Huang
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  2. Yixue Yang
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  3. Xiaohui Huang
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  4. Zhiying Peng
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  5. Liyan Xiong
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Corresponding author

Correspondence to Yixue Yang.

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The datasets generated during and analysed during the current study are available from the corresponding author on reasonable request.

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Weichun Huang and Yixue Yang contributed equally to this work.

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Huang, W., Yang, Y., Huang, X. et al. Emotion-cause pair extraction based on interactive attention. Appl Intell 53, 10548–10558 (2023). https://doi.org/10.1007/s10489-022-03873-x

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