  One challenge for dialogue agents is to recognize feelings of the
conversation partner and respond accordingly. In this work, RoBERTa-GPT2 is
proposed for empathetic dialogue generation, where the pre-trained
auto-encoding RoBERTa is utilised as encoder and the pre-trained
auto-regressive GPT-2 as decoder. With the combination of the pre-trained
RoBERTa and GPT-2, our model realizes a new state-of-the-art emotion accuracy.
To enable the empathetic ability of RoBERTa-GPT2 model, we propose a
commonsense knowledge and emotional concepts extractor, in which the
commonsensible and emotional concepts of dialogue context are extracted for the
GPT-2 decoder. The experiment results demonstrate that the empathetic dialogue
generation benefits from both pre-trained encoder-decoder architecture and
external knowledge.
