Empathy Level Alignment via Reinforcement Learning for Empathetic Response Generation
<|reference_start|>Empathy Level Alignment via Reinforcement Learning for Empathetic Response Generation: Empathetic response generation, aiming at understanding the user's situation and feelings and respond empathically, is crucial in building human-like dialogue systems. Previous methods mainly focus on using maximum likelihood estimation as the optimization objective for training response generation models, without taking into account the empathy level alignment between generated responses and target responses. To this end, we propose an empathetic response generation using reinforcement learning (EmpRL) framework. The framework designs an effective empathy reward function and generates empathetic responses by maximizing the expected reward through reinforcement learning. Given the powerful text generation capability of pre-trained language models, EmpRL utilizes the pre-trained T5 model as the generator and conducts further training to initialize the policy. To align the empathy level between generated responses and target responses in the context, an empathy reward function containing three empathy communication mechanisms, i.e., emotional reaction, interpretation, and exploration, is constructed using pre-designed and pre-trained empathy identifiers. Finally, the proximal policy optimization algorithm is used to further train the policy to produce empathetic responses. Both automatic and manual evaluations demonstrate that the proposed EmpRL framework can improve the quality of generated responses, enhance the empathy level similarity between generated and target responses, and produce empathetic responses covering both affective and cognitive aspects.<|reference_end|>
@article{ma2024empathy,
    title={Empathy Level Alignment via Reinforcement Learning for Empathetic
  Response Generation},
    author={Hui Ma, Bo Zhang, Bo Xu, Jian Wang, Hongfei Lin, and Xiao Sun},
    journal={arXiv preprint arXiv:2408.02976},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.02976},
    primaryClass={cs.CL cs.AI}
}