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.
@article{arxiv.2109.03004,
title = {Empathetic Dialogue Generation with Pre-trained RoBERTa-GPT2 and External Knowledge},
author = {Ye Liu and Wolfgang Maier and Wolfgang Minker and Stefan Ultes},
journal= {arXiv preprint arXiv:2109.03004},
year = {2021}
}
Comments
accepted at International Workshop on Spoken Dialog System Technology (IWSDS) 2021