Conversational Emotion Recognition Incorporating Knowledge Graph and Curriculum Learning

Du Jinming; Sun Yuanyuan; Lin Hongfei; Yang Liang

doi:10.7544/issn1000-1239.202220951

Journal of Computer Research and Development > 2024 > 61(5): 1299-1309. > DOI: 10.7544/issn1000-1239.202220951

Du Jinming, Sun Yuanyuan, Lin Hongfei, Yang Liang. Conversational Emotion Recognition Incorporating Knowledge Graph and Curriculum Learning[J]. Journal of Computer Research and Development, 2024, 61(5): 1299-1309. DOI: 10.7544/issn1000-1239.202220951

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Conversational Emotion Recognition Incorporating Knowledge Graph and Curriculum Learning

College of Computer Science and Technology, Dalian University of Technology, Dalian, Liaoning 116024

Funds: This work was supported by the National Key Research and Development Program of China (2022YFC3301801) and the Fundamental Research Funds for the Central Universities (DUT22ZD205)

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Author Bio:
Du Jinming: born in 2000. Master. His main research interests include deep learning, natural language processing, and sentiment analysis

Sun Yuanyuan: born in 1979. PhD, professor, PhD supervisor. Senior member of CCF. Her main research interests include deep learning and natural language processing

Lin Hongfei: born in 1962. PhD, professor, PhD supervisor. Member of CCF. His main research interests include deep learning, natural language processing, and sentiment analysis. (hflin@dlut.edu.cn)

Yang Liang: born in 1986. PhD, lecturer. His main research interests include deep learning, natural language processing, and information retrieval. (liang@dlut.edu.cn)
Received Date: November 17, 2022
Revised Date: July 23, 2023
Available Online: February 21, 2024

Graphical Abstract

Abstract

Abstract

Conversational emotion recognition is the task of classifying emotions based on conversations. The conversation data are characterized by colloquial language and a wide range of topics, with semantic similarities among labels. Colloquial language exhibits issues such as word ambiguity and the omission of semantic information, emphasizing the importance of common sense and grammatical knowledge in conversational emotion recognition tasks, and these factors enable the model to accurately capture semantic information. Moreover, the current challenge lies in the variations in text richness and the frequency of emotion transfer across different dialogue scenarios, which result in suboptimal classification performance. We propose CK-ERC model to address these challenges. In the pre-training phase, CK-ERC model extracts structured data to incorporate common sense knowledge graphs and grammatical knowledge graphs, aiding the model in accurately capturing colloquial information. In the fine-tuning phase, a supervised contrast learning task is introduced to help the model identify similar emotional labels. Furthermore, a dynamic threshold-based curriculum learning strategy is designed for training and optimizing the model based on text richness (from high to low) and emotion transfer frequency (from low to high). CK-ERC model demonstrates superior performance in various conversation modes, including two-person conversation, multi-person conversation, simulated conversation, and daily conversation. Particularly, CK-ERC model achieves the best performance on MELD and EmoryNLP datasets.
- conversational emotion recognition,
- contrast learning,
- knowledge graph,
- curriculum learning,
- transfer learning

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References (38)

References

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