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.

I recently collaborated on several projects involving chatbots and had the opportunity to discuss with industry experts about the main difficulties that are often encountered in this type of project. While it is becoming easier and easier to build conversational assistants, it looks like there are some problems that emerge systematically as the chatbot grows, as a consequence of not having a proper intent architecture. In this article, I propose a way of designing intents with the goal of avoiding these bad symptoms. I'll deal primarily with chatbots whose input may be both free text or voice (and so intent classification is involved), and from multiple choice. The good news is that we can use both input modes in the same chatbot, using the best one on the right occasion.