Empathetic conversational models have been shown to improve user satisfaction and task outcomes in numerous domains. In Psychology, persona has been shown to be highly correlated to personality, which in turn influences empathy. In addition, our empirical analysis also suggests that persona plays an important role in empathetic conversations. To this end, we propose a new task towards persona-based empathetic conversations and present the first empirical study on the impact of persona on empathetic responding. Specifically, we first present a novel large-scale multi-domain dataset for persona-based empathetic conversations. We then propose CoBERT, an efficient BERT-based response selection model that obtains the state-of-the-art performance on our dataset. Finally, we conduct extensive experiments to investigate the impact of persona on empathetic responding. Notably, our results show that persona improves empathetic responding more when CoBERT is trained on empathetic conversations than non-empathetic ones, establishing an empirical link between persona and empathy in human conversations.
翻译:我们的经验分析还表明,个人在同情性对话中起着重要作用。为此,我们建议对基于人的同情性对话执行一项新任务,并介绍关于人对同情性反应的影响的第一次经验研究。具体地说,我们首先为基于人的同情性对话展示了一个新的大型多域数据集。我们随后提出了CABERT,这是一个高效的基于BERT的反应选择模型,在我们的数据集中获取最先进的表现。最后,我们进行了广泛的实验,以调查人对同情性反应的影响。值得注意的是,我们的结果显示,当CABERT接受关于同情性对话而不是非同情性对话的培训时,人的反应能力会得到改善,从而在人与人之间建立起一种经验联系。