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A Large-Scale Chinese Short-Text Conversation Dataset

arXiv 2008.03946 ↗First posted 10 Aug 2020cs.CLcited by 77

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Abstract (arXiv)

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.

Key facts. Cited by 77 papers in the map (data through 7 Oct 2026). First posted on arXiv 10 Aug 2020.

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Citing papers per year: 2021: 16; 2022: 20; 2023: 19; 2024: 12; 2025: 6; 2026: 4.

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  10. 2021ProphetNet-X: Large-Scale Pre-training Models for English, Chinese, Multi-lingual, Dialog, and Code Generationcited by 31
  11. 2022EVA2.0: Investigating Open-Domain Chinese Dialogue Systems with Large-Scale Pre-Trainingcited by 31
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  33. 2023Prompt Learning to Mitigate Catastrophic Forgetting in Cross-lingual Transfer for Open-domain Dialogue Generationcited by 4
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