CARE: Commonsense-Aware Emotional Response Generation with Latent Concepts

Abstract

Rationality and emotion are two fundamental elements of humans. Endowing agents with rationality and emotion has been one of the major milestones in AI. However, in the field of conversational AI, most existing models only specialize in one aspect and neglect the other, which often leads to dull or unrelated responses. In this paper, we hypothesize that combining rationality and emotion into conversational agents can improve response quality. To test the hypothesis, we focus on one fundamental aspect of rationality, i.e., commonsense, and propose CARE, a novel model for commonsense-aware emotional response generation. Specifically, we first propose a framework to learn and construct commonsense-aware emotional latent concepts of the response given an input message and a desired emotion. We then propose three methods to collaboratively incorporate the latent concepts into response generation. Experimental results on two large-scale datasets support our hypothesis and show that our model can produce more accurate and commonsense-aware emotional responses and achieve better human ratings than state-of-the-art models that only specialize in one aspect.

Multi-Task Learning of Generation and Classification for Emotion-Aware Dialogue Response Generation

“…The model learns a distribution to infer both the emotion of the utterance and the response from a given utterance. CARE (Zhong et al, 2021) uses some commonsense to generate a response with both rationality and emotion. Through latent concepts obtained from an emotionally aware knowledge graph, predicted responses can be emotional and rational.…”