CLAIAug 6, 2024

Empathy Level Alignment via Reinforcement Learning for Empathetic Response Generation

arXiv:2408.02976v311 citationsh-index: 8
Originality Incremental advance
AI Analysis

This work addresses the challenge of generating more human-like empathetic responses in dialogue systems, representing an incremental advancement over traditional maximum likelihood estimation methods.

The authors tackled the problem of aligning empathy levels between generated and target responses in empathetic dialogue systems by proposing a reinforcement learning framework with a novel empathy reward function, resulting in significant improvements in response quality and empathy similarity as demonstrated through evaluations.

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.

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