  The goal of dialogue topic shift detection is to identify whether the current
topic in a conversation has changed or needs to change. Previous work focused
on detecting topic shifts using pre-trained models to encode the utterance,
failing to delve into the various levels of topic granularity in the dialogue
and understand dialogue contents. To address the above issues, we take a
prompt-based approach to fully extract topic information from dialogues at
multiple-granularity, i.e., label, turn, and topic. Experimental results on our
annotated Chinese Natural Topic Dialogue dataset CNTD and the publicly
available English TIAGE dataset show that the proposed model outperforms the
baselines. Further experiments show that the information extracted at different
levels of granularity effectively helps the model comprehend the conversation
topics.
