Perspective-taking and Pragmatics for Generating Empathetic Responses Focused on Emotion Causes
This work addresses the challenge of enhancing empathy in AI dialogue systems for applications like mental health support or customer service, though it is incremental as it builds on existing models.
The paper tackled the problem of generating empathetic responses by identifying emotion cause words without word-level annotations and focusing on those words during generation, resulting in improved performance of multiple dialogue agents in both automatic and human evaluations.
Empathy is a complex cognitive ability based on the reasoning of others' affective states. In order to better understand others and express stronger empathy in dialogues, we argue that two issues must be tackled at the same time: (i) identifying which word is the cause for the other's emotion from his or her utterance and (ii) reflecting those specific words in the response generation. However, previous approaches for recognizing emotion cause words in text require sub-utterance level annotations, which can be demanding. Taking inspiration from social cognition, we leverage a generative estimator to infer emotion cause words from utterances with no word-level label. Also, we introduce a novel method based on pragmatics to make dialogue models focus on targeted words in the input during generation. Our method is applicable to any dialogue models with no additional training on the fly. We show our approach improves multiple best-performing dialogue agents on generating more focused empathetic responses in terms of both automatic and human evaluation.