Enhancing Empathetic and Emotion Support Dialogue Generation with
  Prophetic Commonsense Inference
Lanrui Wang Jiangnan Li Chenxu Yang Zheng Lin Weiping Wang
The interest in Empathetic and Emotional Support conversations among the
public has significantly increased. To offer more sensitive and understanding
responses, leveraging commonsense knowledge has become a common strategy to
better understand psychological aspects and causality. However, such
commonsense inferences can be out of context and unable to predict upcoming
dialogue themes, resulting in responses that lack coherence and empathy. To
remedy this issue, we present Prophetic Commonsense Inference, an innovative
paradigm for inferring commonsense knowledge. By harnessing the capabilities of
Large Language Models in understanding dialogue and making commonsense
deductions, we train tunable models to bridge the gap between past and
potential future dialogues. Extensive experiments conducted on
EmpatheticDialogues and Emotion Support Conversation show that equipping
dialogue agents with our proposed prophetic commonsense inference significantly
enhances the quality of their responses.