Dialogue segmentation is a crucial task for dialogue systems allowing a better understanding of conversational texts. Despite recent progress in unsupervised dialogue segmentation methods, their performances are limited by the lack of explicit supervised signals for training. Furthermore, the precise definition of segmentation points in conversations still remains as a challenging problem, increasing the difficulty of collecting manual annotations. In this paper, we provide a feasible definition of dialogue segmentation points with the help of document-grounded dialogues and release a large-scale supervised dataset called SuperDialseg, containing 9K dialogues based on two prevalent document-grounded dialogue corpora, and also inherit their useful dialogue-related annotations. Moreover, we propose two models to exploit the dialogue characteristics, achieving state-of-the-art performance on SuperDialseg and showing good generalization ability on the out-of-domain datasets. Additionally, we provide a benchmark including 20 models across four categories for the dialogue segmentation task with several proper evaluation metrics. Based on the analysis of the empirical studies, we also provide some insights for the task of dialogue segmentation. We believe our work is an important step forward in the field of dialogue segmentation.

本文通过文件引导对话，并发布一个大规模的受监督数据集SuperDialseg，该数据集包含基于两个流行的文件引导对话语料库的9K个对话。此外，我们提出了两个模型来利用对话特征，实现关于对话分割任务的表现，并提供包括20个模型在内的基准测试，并根据实证研究提供了一些洞见。我们相信我们的工作是对话分割领域向前迈出的重要一步。

SuperDialseg：一款大规模的用于有监督对话分段的数据集