论文标题
哑剧：模仿善解人意的响应产生的情绪
MIME: MIMicking Emotions for Empathetic Response Generation
论文作者
论文摘要
当前的移情响应生成方法将输入文本中表达的情绪集视为平坦的结构，在该结构中，所有情绪都均匀地对待。我们认为，善解人意的反应通常取决于其积极性或负面性和内容，以不同程度模仿用户的情感。我们表明，与最先进的艺术相比，对这种基于极性的情感群和情感模仿的考虑会改善反应的同理心和上下文相关性。此外，我们将随机性引入情感混合物中，这些情感混合物比以前的工作产生的情感上的善解人意反应更多。我们证明了这些因素对使用自动和人类基于人类的评估的同情反应产生的重要性。 MIME的实现可在https://github.com/declare-lab/mime上公开获得。
Current approaches to empathetic response generation view the set of emotions expressed in the input text as a flat structure, where all the emotions are treated uniformly. We argue that empathetic responses often mimic the emotion of the user to a varying degree, depending on its positivity or negativity and content. We show that the consideration of this polarity-based emotion clusters and emotional mimicry results in improved empathy and contextual relevance of the response as compared to the state-of-the-art. Also, we introduce stochasticity into the emotion mixture that yields emotionally more varied empathetic responses than the previous work. We demonstrate the importance of these factors to empathetic response generation using both automatic- and human-based evaluations. The implementation of MIME is publicly available at https://github.com/declare-lab/MIME.
