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.
翻译:个人之间日常对话的一个关键特征是能够表达对他人的同情,探索实现同情的方法是走向人式对话系统的关键一步。以前关于这一专题的方法主要侧重于发现和利用用户的情感产生同情反应。然而,由于同情包括感情和认知两个方面,我们认为,除了确定用户的情感外,还应考虑对用户状况的认知理解。为此,我们提议为同情反应产生一种新颖的方法,利用常识来收集更多关于用户状况的信息,并利用这种额外信息来进一步加强生成的响应中的同情表达。我们评估了我们关于同情对话的方法,这是用于产生同情反应的广泛使用的基准数据集。 经验性结果表明,我们的方法在自动和人类评价中都超过了基线模型,能够产生更多信息性和同情性的反应。