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Graph attention network based detection of causality for textual emotion-cause pair

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Abstract

To solve the problem that the existing model cannot adequately express inter-sentence structural information, this paper proposes a textual Emotion-Cause Pair (ECP) causal relationship detection method (GAT-ECP-CD) fused with graph attention network (GAT). A structural relationship graph directly propagates causal features from the context to integrate syntactic dependency information between different sentences in a document. First, using a word-level Bidirectional Long Short-Term Memory (BiLSTM) network to obtain intraclause semantic representations respectively. Then, the independent sentence vector is sent to the GAT as a graph node to capture the local and global dependency information between clauses to obtain richer features. Finally, a multi-task learning module bridges the first and second stages for dynamic prediction. On the benchmark dataset, compared with the existing method, the F1 score is improved by 4.38%, which verifies the effectiveness of the proposed model.

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Acknowledgements

This work was partially supported by Postgraduates’ Scientific Research and Innovation Project of Huzhou University under Grant 2020KYCX24 and Zhejiang Province Key Laboratory of Smart Management & Application of Modern Agricultural Resources under Grant 2020E10017.

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  1. Zhejiang Province Key Laboratory of Smart Management & Application of Modern Agricultural Resources, School of Information Engineering, HuZhou University, 759 Erhuan Rd, Huzhou, 313000, Zhejiang, China

    Qian Cao, Xiulan Hao, Huajian Ren, Wenjing Xu, Shiluo Xu & Charles Jnr. Asiedu

  2. China Construction Bank Huzhou BR., 118 Hongqi Rd, Huzhou, 313000, Zhejiang, China

    Qian Cao

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  1. Qian Cao
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Cao, Q., Hao, X., Ren, H. et al. Graph attention network based detection of causality for textual emotion-cause pair. World Wide Web 26, 1731–1745 (2023). https://doi.org/10.1007/s11280-022-01111-5

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