中文对话中的话题转移检测：语料库与基准
Topic Shift Detection in Chinese Dialogues: Corpus and Benchmark
AI总结：
针对无回复信息的对话话题转移预测难题，构建了含1308段对话的中文CNTD语料库，提出基于层级对比学习的师生框架，实验验证了模型有效性。
摘要：
对话话题转移检测旨在识别对话中正在进行的话题是否已经转移或应当转移，可分为两类，即已知回复任务和未知回复任务。目前仅有少数研究关注后者，因为在没有回复信息的情况下预测话题转移仍是一项挑战。本文中，我们首先标注了包含1308段对话的中文自然话题对话（Chinese Natural Topic Dialogue，CNTD）语料库，以填补中文自然会话语料库在话题相关资源上的空白。随后我们聚焦于未知回复任务，提出了一种基于层级对比学习的师生框架，用于在无回复的情况下预测话题转移。具体而言，在高层师生结构中引入回复，构建回复与上下文之间的对比学习，而在低层学生网络中构建标签对比学习。在我们构建的中文CNTD和英文TIAGE数据集上的实验结果表明，所提模型具有有效性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
