Empathy is a complex cognitive ability based on the reasoning of others' affective states. In order to better understand others and express stronger empathy in dialogues, we argue that two issues must be tackled at the same time: (i) identifying which word is the cause for the other's emotion from his or her utterance and (ii) reflecting those specific words in the response generation. However, previous approaches for recognizing emotion cause words in text require sub-utterance level annotations, which can be demanding. Taking inspiration from social cognition, we leverage a generative estimator to infer emotion cause words from utterances with no word-level label. Also, we introduce a novel method based on pragmatics to make dialogue models focus on targeted words in the input during generation. Our method is applicable to any dialogue models with no additional training on the fly. We show our approach improves multiple best-performing dialogue agents on generating more focused empathetic responses in terms of both automatic and human evaluation.
翻译:同情是一种基于他人感性状态推理的复杂的认知能力。 为了更好地了解他人并在对话中表达更强烈的同情心,我们主张必须同时解决两个问题:(一) 确定哪个词是对方发自其言语的情感原因,(二) 反映反应一代中的具体词。然而,先前在文本中承认情感致因词的方法需要低调层次的注释,这要求很高。我们从社会认知中汲取灵感,利用一个典型的估算器从没有字级标签的言语中推断情感导致言词。此外,我们还采用了一种基于务实的新颖的方法,使对话模式侧重于一代人的投入中的目标词。我们的方法适用于任何对话模式,而没有这方面的额外培训。我们展示了我们的方法改进多种最佳对话工具,在自动和人文评估方面产生更加集中的同情反应。