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    <titleInfo>
        <title>CCNet: Extracting High Quality Monolingual Datasets from Web Crawl Data</title>
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    <name type="personal">
        <namePart type="given">Guillaume</namePart>
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    <name type="personal">
        <namePart type="given">Marie-Anne</namePart>
        <namePart type="family">Lachaux</namePart>
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    <name type="personal">
        <namePart type="given">Alexis</namePart>
        <namePart type="family">Conneau</namePart>
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    <name type="personal">
        <namePart type="given">Vishrav</namePart>
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    <name type="personal">
        <namePart type="given">Francisco</namePart>
        <namePart type="family">Guzmán</namePart>
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    <name type="personal">
        <namePart type="given">Armand</namePart>
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    <name type="personal">
        <namePart type="given">Edouard</namePart>
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            <title>Proceedings of the Twelfth Language Resources and Evaluation Conference</title>
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    <abstract>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.</abstract>
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%0 Conference Proceedings
%T CCNet: Extracting High Quality Monolingual Datasets from Web Crawl Data
%A Wenzek, Guillaume
%A Lachaux, Marie-Anne
%A Conneau, Alexis
%A Chaudhary, Vishrav
%A Guzmán, Francisco
%A Joulin, Armand
%A Grave, Edouard
%S Proceedings of the Twelfth Language Resources and Evaluation Conference
%D 2020
%8 May
%I European Language Resources Association
%C Marseille, France
%@ 979-10-95546-34-4
%G English
%F wenzek-etal-2020-ccnet
%X 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.
%U https://aclanthology.org/2020.lrec-1.494
%P 4003-4012

Markdown (Informal)

[CCNet: Extracting High Quality Monolingual Datasets from Web Crawl Data](https://aclanthology.org/2020.lrec-1.494) (Wenzek et al., LREC 2020)

• CCNet: Extracting High Quality Monolingual Datasets from Web Crawl Data (Wenzek et al., LREC 2020)

ACL

• Guillaume Wenzek, Marie-Anne Lachaux, Alexis Conneau, Vishrav Chaudhary, Francisco Guzmán, Armand Joulin, and Edouard Grave. 2020. CCNet: Extracting High Quality Monolingual Datasets from Web Crawl Data. In Proceedings of the Twelfth Language Resources and Evaluation Conference, pages 4003–4012, Marseille, France. European Language Resources Association.