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

This paper proposes a novel task on commonsense-enhanced task-based dialogue
grounded in documents and describes the Task2Dial dataset, a novel dataset of
document-grounded task-based dialogues, where an Information Giver (IG)
provides instructions (by consulting a document) to an Information Follower
(IF), so that the latter can successfully complete the task. In this unique
setting, the IF can ask clarification questions which may not be grounded in
the underlying document and require commonsense knowledge to be answered. The
Task2Dial dataset poses new challenges: (1) its human reference texts show more
lexical richness and variation than other document-grounded dialogue datasets;
(2) generating from this set requires paraphrasing as instructional responses
might have been modified from the underlying document; (3) requires commonsense
knowledge, since questions might not necessarily be grounded in the document;
(4) generating requires planning based on context, as task steps need to be
provided in order. The Task2Dial dataset contains dialogues with an average
$18.15$ number of turns and 19.79 tokens per turn, as compared to 12.94 and 12
respectively in existing datasets. As such, learning from this dataset promises
more natural, varied and less template-like system utterances.

本文提出了一种基于常识增强的基于任务的对话任务，并描述了 Task2Dial 数据集，这是一个基于文档的任务对话的新数据集，在此设置中，IF 可以提出澄清问题，这些问题可能不基于基础文档，需要常识知识来回答。