论文标题
通过解开同情对话来建模内容情感二元性
Modeling Content-Emotion Duality via Disentanglement for Empathetic Conversation
论文作者
论文摘要
善解人意的回应的任务旨在了解说话者对自己的经历表达的感觉，然后适当地回复演讲者。为了解决任务，必须对话的内容情绪对偶性建模，该对话是由内容视图组成的（即描述了哪些个人经历）和情感观点（即说话者对这些经验的感觉）。为此，我们设计了一个框架，以通过分离促进响应生成来对内容情绪二元性（cedual）建模。通过分解，我们从内容和情感观点中编码了对话历史，然后基于分离的表示产生善解人意的响应，从而可以将对话历史记录的内容和情感信息嵌入到生成的响应中。基准数据集促进性的实验表明，cedual模型在自动和人类指标上都达到了最新的性能，并且它还产生了比以前的方法产生更多的促进响应。
The task of empathetic response generation aims to understand what feelings a speaker expresses on his/her experiences and then reply to the speaker appropriately. To solve the task, it is essential to model the content-emotion duality of a dialogue, which is composed of the content view (i.e., what personal experiences are described) and the emotion view (i.e., the feelings of the speaker on these experiences). To this end, we design a framework to model the Content-Emotion Duality (CEDual) via disentanglement for empathetic response generation. With disentanglement, we encode the dialogue history from both the content and emotion views, and then generate the empathetic response based on the disentangled representations, thereby both the content and emotion information of the dialogue history can be embedded in the generated response. The experiments on the benchmark dataset EMPATHETICDIALOGUES show that the CEDual model achieves state-of-the-art performance on both automatic and human metrics, and it also generates more empathetic responses than previous methods.
