  Existing dialog system models require extensive human annotations and are
difficult to generalize to different tasks. The recent success of large
pre-trained language models such as BERT and GPT-2 (Devlin et al., 2019;
Radford et al., 2019) have suggested the effectiveness of incorporating
language priors in down-stream NLP tasks. However, how much pre-trained
language models can help dialog response generation is still under exploration.
In this paper, we propose a simple, general, and effective framework:
Alternating Roles Dialog Model (ARDM). ARDM models each speaker separately and
takes advantage of the large pre-trained language model. It requires no
supervision from human annotations such as belief states or dialog acts to
achieve effective conversations. ARDM outperforms or is on par with
state-of-the-art methods on two popular task-oriented dialog datasets:
CamRest676 and MultiWOZ. Moreover, we can generalize ARDM to more challenging,
non-collaborative tasks such as persuasion. In persuasion tasks, ARDM is
capable of generating human-like responses to persuade people to donate to a
charity.
