  A multi-turn dialogue always follows a specific topic thread, and topic shift
at the discourse level occurs naturally as the conversation progresses,
necessitating the model's ability to capture different topics and generate
topic-aware responses. Previous research has either predicted the topic first
and then generated the relevant response, or simply applied the attention
mechanism to all topics, ignoring the joint distribution of the topic
prediction and response generation models and resulting in uncontrollable and
unrelated responses. In this paper, we propose a joint framework with a topic
refinement mechanism to learn these two tasks simultaneously. Specifically, we
design a three-pass iteration mechanism to generate coarse response first, then
predict corresponding topics, and finally generate refined response conditioned
on predicted topics. Moreover, we utilize GPT2DoubleHeads and BERT for the
topic prediction task respectively, aiming to investigate the effects of joint
learning and the understanding ability of GPT model. Experimental results
demonstrate that our proposed framework achieves new state-of-the-art
performance at response generation task and the great potential understanding
capability of GPT model.
