Knowledge Bridging for Empathetic Dialogue Generation
This addresses the challenge of generating empathetic responses in dialogue systems, which is incremental as it builds on existing methods by incorporating external knowledge.
The paper tackles the problem of empathetic dialogue generation lacking external knowledge by leveraging commonsense and emotional lexical knowledge to understand and express emotions, achieving verified effectiveness on a benchmark dataset.
Lack of external knowledge makes empathetic dialogue systems difficult to perceive implicit emotions and learn emotional interactions from limited dialogue history. To address the above problems, we propose to leverage external knowledge, including commonsense knowledge and emotional lexical knowledge, to explicitly understand and express emotions in empathetic dialogue generation. We first enrich the dialogue history by jointly interacting with external knowledge and construct an emotional context graph. Then we learn emotional context representations from the knowledge-enriched emotional context graph and distill emotional signals, which are the prerequisites to predicate emotions expressed in responses. Finally, to generate the empathetic response, we propose an emotional cross-attention mechanism to learn the emotional dependencies from the emotional context graph. Extensive experiments conducted on a benchmark dataset verify the effectiveness of the proposed method. In addition, we find the performance of our method can be further improved by integrating with a pre-trained model that works orthogonally.