Chengkun Zeng


2021

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Affective Decoding for Empathetic Response Generation
Chengkun Zeng | Guanyi Chen | Chenghua Lin | Ruizhe Li | Zhi Chen
Proceedings of the 14th International Conference on Natural Language Generation

Understanding speaker’s feelings and producing appropriate responses with emotion connection is a key communicative skill for empathetic dialogue systems. In this paper, we propose a simple technique called Affective Decoding for empathetic response generation. Our method can effectively incorporate emotion signals during each decoding step, and can additionally be augmented with an auxiliary dual emotion encoder, which learns separate embeddings for the speaker and listener given the emotion base of the dialogue. Extensive empirical studies show that our models are perceived to be more empathetic by human evaluations, in comparison to several strong mainstream methods for empathetic responding.