Rui Pan
ABSTRACT
Empathetic dialogue generation is dedicated to generating responses to empathize with users by perceiving and understanding context emotions and dialogue situations. Existing works typically emphasize that an empathetic response needs to express suitable emotion through perceiving context emotion but ignore the equal need to express informative content in response by understanding the dialogue situation. To this end, we propose a novel empathetic dialogue generation model abbreviated as EmpDGM, which is extended based on the Transformer by a semantics decoupler and empathetic generator. Specifically, the semantics decoupler can effectively decouple emotion semantics and content semantics in the input sequence using adversarial training and multi-task learning meanwhile ensuring the obtained content semantics is complete. And the empathetic generator introduces a gated fusion mechanism to fuse content semantics and context emotion embedding in a balanced manner throughout the whole generation process, which overcomes generally incorporating context emotion embedding as part of initial embedding in the generation module leading the insufficient emotion expression. We conduct automatic evaluation and manual evaluation on the benchmark dataset EMPATHETICDIALOGUES of empathetic dialogue generation. Experimental results reveal that our EmpDGM outperforms advanced baselines in both emotion perceptivity and content quality and generates more informative and affective responses.
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Index Terms
- Improving Empathetic Dialogue Generation with Semantics Decoupling
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