Abstract
An engaging dialogue system is supposed to generate empathetic responses, which requires a cognitive understanding of users’ situations and an affective perception of their emotions. Most of the existing work only focuses on modeling the latter, while neglecting the importance of the former. Despite some efforts to enhance chatbots’ empathy in both cognition and affection, limited cognition conditions and inaccessible fine-grained information still impair the effectiveness of empathy modeling. To address this issue, we propose a novel fine-grained knowledge-enhanced empathetic dialogue generation model KEEM. We first explore strategies to filter fine-grained commonsense and emotional knowledge and leverage knowledge to construct cognitive and affective context graphs. And we learn corresponding context representations from the two knowledge-enhanced context graphs. Then we encode the raw dialogue context to learn the original cognitive and affective representations and fuse them with the knowledge-enhanced representations in cognition and affection. Finally, we feed the two fused representations into a decoder to produce empathetic replies. Extensive experiments conducted on the benchmark dataset EMPATHETICDIALOGUES verify the effectiveness of our model in comparison with several competitive models.
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Acknowledgements
The authors would like to thank the Associate Editor and anonymous reviewers for their valuable comments and suggestions. This work is funded in part by the National Natural Science Foundation of China under Grants No.62176029. This work also is supported in part by the Chongqing Technology Innovation and Application Development Special under Grants CSTB2022TIAD-KPX0206. Any opinions, findings, and conclusions, or recommendations expressed in this material are those of the authors and do not necessarily reflect those of the sponsor.
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Chen, A., Zhong, J., Dai, Q., Wang, C., Li, R. (2023). Fine-Grained Knowledge Enhancement for Empathetic Dialogue Generation. In: Yang, X., et al. Advanced Data Mining and Applications. ADMA 2023. Lecture Notes in Computer Science(), vol 14179. Springer, Cham. https://doi.org/10.1007/978-3-031-46674-8_6
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DOI: https://doi.org/10.1007/978-3-031-46674-8_6
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