Transformer-based Empathetic Response Generation Using Dialogue Situation and Advanced-Level Definition of Empathy
Yi‐Hsuan Wang, Jia-Hao Hsu, Chung‐Hsien Wu, Tsung‐Hsien Yang · 2021
This study proposes an approach to transformer-based empathetic response generation using dialogue situation and advanced-level definition of empathy. First, SBERT is adopted to extract the dialog situation vector from the user's historical sentences. A BERT-based emotion detector, a topic detector and an information estimator are constructed for empathy-related feature extraction. The change of the emotional valance and the textual information gain, obtained from the emotion detector and the information estimator, are used for adversarial training of the transformer-based empathetic response generator. The loss function of the transformer is defined to measure how good the expected response in terms of fluency and empathy. The EmpatheticDialogues corpus was adopted for system training and evaluation on empathetic response generation. According to the experimental results, the BLEU score was increased to 7.821 after considering the dialogue situation feature and empathy definition, outperforming the comparison models. In terms of human subjective evaluation, three evaluation results of empathy, relevance and fluency for the proposed system are better than that for the baseline model.