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CCNet: Extracting High Quality Monolingual Datasets from Web Crawl Data

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arxiv 1911.00359 v2 pith:M6RYFXKD submitted 2019-11-01 cs.CL cs.IRcs.LGstat.ML

Guillaume Wenzek , Marie-Anne Lachaux , Alexis Conneau , Vishrav Chaudhary , Francisco Guzmán , Armand Joulin , Edouard Grave This is my paper
classification cs.CLcs.IRcs.LGstat.ML
keywords qualitypipelinecorporacrawldatadatasetsdocumentshigh
verification ladder T0 review T1 audit T2 compute T3 formal
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Pre-training text representations have led to significant improvements in many areas of natural language processing. The quality of these models benefits greatly from the size of the pretraining corpora as long as its quality is preserved. In this paper, we describe an automatic pipeline to extract massive high-quality monolingual datasets from Common Crawl for a variety of languages. Our pipeline follows the data processing introduced in fastText (Mikolov et al., 2017; Grave et al., 2018), that deduplicates documents and identifies their language. We augment this pipeline with a filtering step to select documents that are close to high quality corpora like Wikipedia.

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Cited by 5 Pith papers

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