CEM: Commonsense-aware Empathetic Response Generation
This work addresses the problem of generating more human-like empathetic responses in dialogue systems, though it is incremental by building on existing emotion-focused methods.
The paper tackled empathetic response generation by incorporating commonsense reasoning to better understand the user's situation, resulting in improved performance over baselines on the EmpatheticDialogues dataset in both automatic and human evaluations.
A key trait of daily conversations between individuals is the ability to express empathy towards others, and exploring ways to implement empathy is a crucial step towards human-like dialogue systems. Previous approaches on this topic mainly focus on detecting and utilizing the user's emotion for generating empathetic responses. However, since empathy includes both aspects of affection and cognition, we argue that in addition to identifying the user's emotion, cognitive understanding of the user's situation should also be considered. To this end, we propose a novel approach for empathetic response generation, which leverages commonsense to draw more information about the user's situation and uses this additional information to further enhance the empathy expression in generated responses. We evaluate our approach on EmpatheticDialogues, which is a widely-used benchmark dataset for empathetic response generation. Empirical results demonstrate that our approach outperforms the baseline models in both automatic and human evaluations and can generate more informative and empathetic responses.