Empathy Level Alignment via Reinforcement Learning for Empathetic Response Generation

TL;DR

This paper introduces EmpRL, a reinforcement learning framework that improves empathetic response generation in dialogue systems by aligning empathy levels using a specialized reward function and fine-tuning a pre-trained language model.

Contribution

It proposes a novel empathy reward function with three mechanisms and fine-tunes a T5 model using reinforcement learning to generate more empathetic responses.

Findings

Significantly improves response empathy levels.

Enhances similarity between generated and target responses.

Produces responses covering affective and cognitive empathy.

Abstract

Empathetic response generation, aiming to understand the user's situation and feelings and respond empathically, is crucial in building human-like dialogue systems. Traditional approaches typically employ maximum likelihood estimation as the optimization objective during training, yet fail to align the empathy levels between generated and target responses. To this end, we propose an empathetic response generation framework using reinforcement learning (EmpRL). The framework develops an effective empathy reward function and generates empathetic responses by maximizing the expected reward through reinforcement learning. EmpRL utilizes the pre-trained T5 model as the generator and further fine-tunes it to initialize the policy. To align the empathy levels between generated and target responses within a given context, an empathy reward function containing three empathy communication mechanisms -- emotional reaction, interpretation, and exploration -- is constructed using pre-designed and pre-trained empathy identifiers. During reinforcement learning training, the proximal policy optimization algorithm is used to fine-tune the policy, enabling the generation of empathetic responses. Both automatic and human evaluations demonstrate that the proposed EmpRL framework significantly improves the quality of generated responses, enhances the similarity in empathy levels between generated and target responses, and produces empathetic responses covering both affective and cognitive aspects.