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.

翻译：对话分割是对话系统中一项关键任务，有助于更好地理解会话文本。尽管近年来无监督对话分割方法取得了进展，但其性能受限于缺少明确的监督信号进行训练。此外，会话中分割点的精确定义仍是一个具有挑战性的问题，增加了人工标注的难度。本文借助基于文档的对话，提供了对话分割点的可行定义，并发布了一个大规模有监督数据集SuperDialseg，该数据集包含基于两个主流文档对话语料库的9000个对话，同时继承了这些语料库中实用的对话相关标注。在此基础上，我们提出了两种模型以利用对话特征，在SuperDialseg上实现了最先进性能，并在域外数据集上展现出良好的泛化能力。此外，我们为对话分割任务构建了一个包含四个类别共20个模型的基准测试，并采用适当的评估指标。基于实证研究分析，我们还为对话分割任务提供了若干见解。我们相信，本工作是对话分割领域的重要进展。