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Dialogue emotion model based on local–global context encoder and commonsense knowledge fusion attention

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Abstract

Emotion Recognition in Conversation (ERC) is a task aimed at predicting the emotions conveyed by an utterance in a dialogue. It is common in ERC research to integrate intra-utterance, local contextual, and global contextual information to obtain the utterance vectors. However, there exist complex semantic dependencies among these factors, and failing to model these dependencies accurately can adversely affect the effectiveness of emotion recognition. Moreover, to enhance the semantic dependencies within the context, researchers commonly introduce external commonsense knowledge after modeling it. However, injecting commonsense knowledge into the model simply without considering its potential impact can introduce unexpected noise. To address these issues, we propose a dialogue emotion model based on local–global context encoder and commonsense knowledge fusion attention. The local–global context encoder effectively integrates the information of intra-utterance, local context, and global context to capture the semantic dependencies among them. To provide more accurate external commonsense information, we present a fusion module to filter the commonsense information through multi-head attention. Our proposed method has achieved competitive results on four datasets and exhibits advantages compared with mainstream models using commonsense knowledge.

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Data sharing not applicable to this article as no datasets were generated or analysed during the current study.

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Acknowledgements

The authors would like to respect and thank all reviewers for their constructive and helpful review. This research is funded by the National Natural Science Foundation of China (62372283, 62206163), Science and Technology Major Project of Guangdong Province (STKJ2021005, STKJ202209002, STKJ2023076), Natural Science Foundation of Guangdong Province (2019A1515010943).

Funding

The authors would like to respect and thank all reviewers for their constructive and helpful review. This research is funded by the National Natural Science Foundation of China (62372283, 62206163), Natural Science Foundation of Guangdong Province (2019A1515010943), The Basic and Applied Basic Research of Colleges and Universities in Guangdong Province (Special Projects in Artificial Intelligence)(2019KZDZX1030), 2020 Li Ka Shing Foundation Cross-Disciplinary Research Grant (2020LKSFG04D), Science and Technology Major Project of Guangdong Province(STKJ2021005, STKJ202209002), and the Opening Project of GuangDong Province Key Laboratory of Information Security Technology (2020B1212060078).

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Authors and Affiliations

  1. Department of Computer Science, Shantou University, Shantou, China

    Weilun Yu, Wenhua Zhu & Dazhi Jiang

  2. Department of Electronic and Computer Engineering, Shenzhen MSU-BIT University, Shenzhen, 518172, China

    Chengming Li

  3. School of Medical Technology, Beijing Institute of Technology, Beijing, China

    Xiping Hu

  4. School of Computer Science and Engineering, Nanyang Technological University, Singapore, Singapore

    Erik Cambria

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  1. Weilun Yu
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  2. Chengming Li
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  4. Wenhua Zhu
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  5. Erik Cambria
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Contributions

W.Y.: Original Draft. C.L.: Review & Editing. X.H.: Review & Editing. W.Z.: Validation. E.C.: Review & Editing. D.J.: Supervision.

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Correspondence to Erik Cambria or Dazhi Jiang.

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Yu, W., Li, C., Hu, X. et al. Dialogue emotion model based on local–global context encoder and commonsense knowledge fusion attention. Int. J. Mach. Learn. & Cyber. 15, 2811–2825 (2024). https://doi.org/10.1007/s13042-023-02066-3

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