# A Large-Scale Chinese Short-Text Conversation Dataset Source: [A Large-Scale Chinese Short-Text Conversation Dataset]() Collection index: [llms.txt]() Data updated: 2025-10-26T00:09:48.320Z Authors: Yida Wang, Pei Ke, Yinhe Zheng, Kaili Huang, Yong Jiang, Xiaoyan Zhu, Minlie Huang Published: 2020-08-10T08:12:49.000Z ## Abstract 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 https://github.com/thu-coai/CDial-GPT. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Open-domain Conversation on Chinese open-domain conversation Self-chat (test)](): Open-domain Conversation; Chinese open-domain conversation Self-chat (test); Coherence: 48.4. ## Original sources and implementation [Original paper]() [PDF]()