Empathetic Response Generation Based on Plug-and-Play Mechanism With Empathy Perturbation

Abstract

Spoken dialogue systems have rapidly developed but are often viewed as inhumane because they lack empathetic communication skills. In this study, a transformer-based language model (DialoGPT fine-tuned on the EmpatheticDialogues dataset) was combined with two proposed attribute models for affective and cognitive empathy to improve its performance. The affective empathy model ensures that the user sentence and system response have similar emotional valence, and the cognitive empathy model ensures that the system response is relevant to the user's input by using a DialoGPT-based reverse generation model to calculate the cross-entropy loss. A plug-and-play structure with these empathy attribute models was used to perturb the language generation model to increase response empathy without fine-tuning or retraining the generation model. Experiments indicated that the proposed model responses had substantially higher affective empathy, cognitive empathy, and BLEU scores than did the baseline model. Subjective evaluations also indicated that the responses of the proposed model had greater empathy, relevance, and fluency than did the baseline model. Moreover, the proposed model outperformed other similar models in A/B tests.