CARE: Commonsense-Aware Emotional Response Generation with Latent Concepts

TL;DR

This paper introduces CARE, a model that integrates commonsense reasoning and emotional understanding to generate more accurate and emotionally appropriate responses in conversational AI, addressing the limitations of existing models.

Contribution

The paper presents a novel framework for learning and incorporating commonsense-aware emotional latent concepts into response generation, improving response quality in conversational AI.

Findings

CARE outperforms state-of-the-art models in human ratings.

The model generates more accurate and emotionally appropriate responses.

Experimental results validate the effectiveness of combining rationality and emotion.

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.