Perspective-taking and Pragmatics for Generating Empathetic Responses Focused on Emotion Causes
Hyunwoo Kim, Byeongchang Kim, Gunhee Kim

TL;DR
This paper presents a novel approach for generating more empathetic dialogue responses by identifying emotion cause words without sub-utterance annotations and focusing on these words during response generation, improving empathy in AI systems.
Contribution
It introduces a generative estimator for emotion cause word inference without word-level labels and a pragmatic method to enhance focus on these words during response generation, applicable to any dialogue model.
Findings
Improved empathetic response quality in multiple dialogue agents
Enhanced focus on emotion cause words during generation
Better automatic and human evaluation scores
Abstract
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…
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Taxonomy
TopicsTopic Modeling · Natural Language Processing Techniques · Speech and dialogue systems
MethodsGenerative Emotion Estimator
