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

SuperDialseg: A Large-scale Dataset for Supervised Dialogue Segmentation

Convolutional networks trained on large supervised dataset produce visual
features which form the basis for the state-of-the-art in many computer-vision
problems. Further improvements of these visual features will likely require
even larger manually labeled data sets, which severely limits the pace at which
progress can be made. In this paper, we explore the potential of leveraging
massive, weakly-labeled image collections for learning good visual features. We
train convolutional networks on a dataset of 100 million Flickr photos and
captions, and show that these networks produce features that perform well in a
range of vision problems. We also show that the networks appropriately capture
word similarity, and learn correspondences between different languages.

本文探讨了在大型弱标记图像集上训练卷积网络以提升视觉特征表达的潜力，并证明了在 1 亿张 Flickr 图片与样本说明上训练出来的特征在多种视觉问题中表现良好，还能够妥善地捕捉单词之间的相似性，并学习不同语言之间的对应关系。