Empathetic Dialogue Generation with Pre-trained RoBERTa-GPT2 and External Knowledge
This addresses the problem of generating empathetic responses in dialogue agents, which is incremental as it builds on pre-trained models with added knowledge.
The paper tackled empathetic dialogue generation by proposing a RoBERTa-GPT2 model combined with external commonsense and emotional knowledge, achieving a new state-of-the-art emotion accuracy.
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.