A recent trend in the domain of open-domain conversational agents is enabling them to converse empathetically to emotional prompts. Current approaches either follow an end-to-end approach or condition the responses on similar emotion labels to generate empathetic responses. But empathy is a broad concept that refers to the cognitive and emotional reactions of an individual to the observed experiences of another and it is more complex than mere mimicry of emotion. Hence, it requires identifying complex human conversational strategies and dynamics in addition to generic emotions to control and interpret empathetic responding capabilities of chatbots. In this work, we make use of a taxonomy of eight empathetic response intents in addition to generic emotion categories in building a dialogue response generation model capable of generating empathetic responses in a controllable and interpretable manner. It consists of two modules: 1) a response emotion/intent prediction module; and 2) a response generation module. We propose several rule-based and neural approaches to predict the next response's emotion/intent and generate responses conditioned on these predicted emotions/intents. Automatic and human evaluation results emphasize the importance of the use of the taxonomy of empathetic response intents in producing more diverse and empathetically more appropriate responses than end-to-end models.

翻译：开放域对话代理领域的最新趋势是使其能够对情感提示进行共情对话。当前方法要么采用端到端方式，要么将响应条件化为相似情感标签以生成共情响应。但共情是一个广义概念，指个体对他人所观察经历的认知和情感反应，其复杂性远高于简单的情感模仿。因此，除了通用情感外，还需识别复杂的人类对话策略和动态特性，以控制和解释聊天机器人的共情响应能力。本研究利用包含八种共情回应意图的分类法，结合通用情感类别，构建了兼具可控性与可解释性的对话响应生成模型。该模型包含两个模块：1) 响应情感/意图预测模块；2) 响应生成模块。我们提出多种基于规则和神经网络的预测方法，用于预测下一响应情感/意图，并基于预测情感/意图生成条件化响应。自动评估与人工评估结果表明，与端到端模型相比，采用共情回应意图分类法能生成更多样化且更具共情适切性的响应。