878 research outputs found
Topic Shift Detection in Chinese Dialogues: Corpus and Benchmark
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
- …