EmpDG: Multiresolution Interactive Empathetic Dialogue Generation
This work addresses the challenge of creating more humanized dialogue systems for applications like chatbots or virtual assistants, though it appears incremental by building on existing empathetic dialogue generation tasks.
The paper tackles the problem of generating empathetic dialogue responses by capturing nuanced user emotions and incorporating user feedback, resulting in a model that significantly outperforms state-of-the-art baselines in content quality and emotion perceptivity.
A humanized dialogue system is expected to generate empathetic replies, which should be sensitive to the users' expressed emotion. The task of empathetic dialogue generation is proposed to address this problem. The essential challenges lie in accurately capturing the nuances of human emotion and considering the potential of user feedback, which are overlooked by the majority of existing work. In response to this problem, we propose a multi-resolution adversarial model -- EmpDG, to generate more empathetic responses. EmpDG exploits both the coarse-grained dialogue-level and fine-grained token-level emotions, the latter of which helps to better capture the nuances of user emotion. In addition, we introduce an interactive adversarial learning framework which exploits the user feedback, to identify whether the generated responses evoke emotion perceptivity in dialogues. Experimental results show that the proposed approach significantly outperforms the state-of-the-art baselines in both content quality and emotion perceptivity.