Emp-RFT: Empathetic Response Generation via Recognizing Feature Transitions between Utterances

TL;DR

This paper introduces Emp-RFT, a novel method for empathetic response generation that recognizes feature transitions between utterances to better understand dialogue flow and improve multi-turn response quality.

Contribution

It proposes a new approach to detect feature transitions in dialogues, enhancing empathetic response generation over existing coarse-grained methods.

Findings

Outperforms baseline models in empathetic response tasks.

Achieves significant improvements in multi-turn dialogue settings.

Effectively captures feature transitions to understand dialogue flow.

Abstract

Each utterance in multi-turn empathetic dialogues has features such as emotion, keywords, and utterance-level meaning. Feature transitions between utterances occur naturally. However, existing approaches fail to perceive the transitions because they extract features for the context at the coarse-grained level. To solve the above issue, we propose a novel approach of recognizing feature transitions between utterances, which helps understand the dialogue flow and better grasp the features of utterance that needs attention. Also, we introduce a response generation strategy to help focus on emotion and keywords related to appropriate features when generating responses. Experimental results show that our approach outperforms baselines and especially, achieves significant improvements on multi-turn dialogues.