The majority of existing methods for empathetic response generation rely on the emotion of the context to generate empathetic responses. However, empathy is much more than generating responses with an appropriate emotion. It also often entails subtle expressions of understanding and personal resonance with the situation of the other interlocutor. Unfortunately, such qualities are difficult to quantify and the datasets lack the relevant annotations. To address this issue, in this paper we propose an approach that relies on exemplars to cue the generative model on fine stylistic properties that signal empathy to the interlocutor. To this end, we employ dense passage retrieval to extract relevant exemplary responses from the training set. Three elements of human communication -- emotional presence, interpretation, and exploration, and sentiment are additionally introduced using synthetic labels to guide the generation towards empathy. The human evaluation is also extended by these elements of human communication. We empirically show that these approaches yield significant improvements in empathetic response quality in terms of both automated and human-evaluated metrics. The implementation is available at https://github.com/declare-lab/exemplary-empathy.

翻译：为解决这一问题,在本文中,我们建议了一种方法,即依靠Exemplakers来推介向对话者表示同情的细微体格特性的基因模型。为此,我们利用密集的通道检索来从培训集中提取相关的示范性反应。人类交流的三个要素 -- -- 情感存在、解释和探索,以及情绪被进一步引入,使用合成标签引导产生同情感。人类交流的这些要素也扩大了人类评价的范围。我们从经验上表明,这些方法在自动和人文评价的测量值两方面都大大改善了同情性反应的质量。实施方法见https://github.com/declare-lab/exemplary-empathy。