A Large-Scale Chinese Short-Text Conversation Dataset

• URL: http://arxiv.org/abs/2008.03946v2
• Date: Tue, 26 Apr 2022 07:07:56 GMT
• Title: A Large-Scale Chinese Short-Text Conversation Dataset
• Authors: Yida Wang, Pei Ke, Yinhe Zheng, Kaili Huang, Yong Jiang, Xiaoyan Zhu, and Minlie Huang
• Abstract summary: 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. We also release pre-training dialogue models which are trained on LCCC-base and LCCC-large respectively.
• Score: 77.55813366932313
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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