Affective Decoding for Empathetic Response Generation
This work addresses the need for more emotionally aware dialogue systems, but it is incremental as it builds on existing empathetic response generation techniques.
The paper tackled the problem of generating empathetic responses in dialogue systems by proposing Affective Decoding, which incorporates emotion signals during decoding and uses an auxiliary dual emotion encoder. The result showed that their models were perceived as more empathetic in human evaluations compared to strong baseline methods.
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.