Emp-RFT: Empathetic Response Generation via Recognizing Feature Transitions between Utterances
This work addresses the challenge of empathetic response generation for conversational AI systems, but it appears incremental as it builds on existing methods by focusing on fine-grained feature transitions.
The paper tackled the problem of generating empathetic responses in multi-turn dialogues by recognizing feature transitions between utterances, achieving significant improvements over baselines, especially in multi-turn contexts.
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.