The advancements of neural dialogue generation models show promising results
on modeling short-text conversations. However, training such models usually
needs a large-scale high-quality dialogue corpus, which is hard to access. In
this paper, we present a large-scale cleaned Chinese conversation dataset,
LCCC, which contains a base version (6.8million dialogues) and a large version
(12.0 million dialogues). The quality of our dataset is ensured by a rigorous
data cleaning pipeline, which is built based on a set of rules and a classifier
that is trained on manually annotated 110K dialogue pairs. We also release
pre-training dialogue models which are trained on LCCC-base and LCCC-large
respectively. The cleaned dataset and the pre-training models will facilitate
the research of short-text conversation modeling. All the models and datasets
are available at this https URL

本文介绍了一个大型的、经过清洗的中文对话数据集 LCCC，包含基础版和大型版两种版本，共计 680 万和 1200 万对话。数据集的质量通过一套规则和分类器确保。此外，本文还释放了 LCCC-base 和 LCCC-large 的预训练对话模型，这些数据集和模型将有助于研究短文本对话建模。