Dialogue topic shift detection is to detect whether an ongoing topic has
shifted or should shift in a dialogue, which can be divided into two
categories, i.e., response-known task and response-unknown task. Currently,
only a few investigated the latter, because it is still a challenge to predict
the topic shift without the response information. In this paper, we first
annotate a Chinese Natural Topic Dialogue (CNTD) corpus consisting of 1308
dialogues to fill the gap in the Chinese natural conversation topic corpus. And
then we focus on the response-unknown task and propose a teacher-student
framework based on hierarchical contrastive learning to predict the topic shift
without the response. Specifically, the response at high-level teacher-student
is introduced to build the contrastive learning between the response and the
context, while the label contrastive learning is constructed at low-level
student. The experimental results on our Chinese CNTD and English TIAGE show
the effectiveness of our proposed model.

本文提出了基于分层对比学习的师生框架来预测没有响应的话题转移，并在中文自然对话话题语料库上进行了实验，结果表明该模型的有效性。