Empathetic dialog generation aims at generating coherent responses following previous dialog turns and, more importantly, showing a sense of caring and a desire to help. Existing models either rely on pre-defined emotion labels to guide the response generation, or use deterministic rules to decide the emotion of the response. With the advent of advanced language models, it is possible to learn subtle interactions directly from the dataset, providing that the emotion categories offer sufficient nuances and other non-emotional but emotional regulating intents are included. In this paper, we describe how to incorporate a taxonomy of 32 emotion categories and 8 additional emotion regulating intents to succeed the task of empathetic response generation. To facilitate the training, we also curated a large-scale emotional dialog dataset from movie subtitles. Through a carefully designed crowdsourcing experiment, we evaluated and demonstrated how our model produces more empathetic dialogs compared with its baselines.
翻译:富有同情心的对话生成的目的是在先前的对话框转弯后产生一致的响应,更重要的是,显示一种关爱感和提供帮助的愿望。现有的模型要么依靠预先定义的情感标签来指导响应生成,要么利用确定性规则来决定响应的情感。随着先进语言模型的出现,有可能直接从数据集中学习微妙的相互作用,条件是情感类别提供了足够的细微差别和其他非情感性但情感调节的意图。我们在本文件中描述了如何纳入一个由32种情感类别和8种其他情感组成的分类,以调控意图来取代同情性响应生成的任务。为了便利培训,我们还从电影字幕中精心设计的众包实验中绘制了大规模情感对话数据集。我们通过精心设计的众包实验,评估并演示了我们的模型如何产生与其基线相比更具同情性的对话。