Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Quality at a Glance: An Audit of Web-Crawled Multilingual Datasets   With the success of large-scale pre-training and multilingual modeling in
Natural Language Processing (NLP), recent years have seen a proliferation of
large, web-mined text datasets covering hundreds of languages. We manually
audit the quality of 205 language-specific corpora released with five major
public datasets (CCAligned, ParaCrawl, WikiMatrix, OSCAR, mC4). Lower-resource
corpora have systematic issues: At least 15 corpora have no usable text, and a
significant fraction contains less than 50% sentences of acceptable quality. In
addition, many are mislabeled or use nonstandard/ambiguous language codes. We
demonstrate that these issues are easy to detect even for non-proficient
speakers, and supplement the human audit with automatic analyses. Finally, we
recommend techniques to evaluate and improve multilingual corpora and discuss
potential risks that come with low-quality data releases.

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The categories of this paper are: physics
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The categories of this paper are: chao-dyn
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The categories of this paper are: math
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The categories of this paper are: solv-int
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The categories of this paper are: hep-lat
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The categories of this paper are: chem-ph
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The categories of this paper are: math-ph
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The categories of this paper are: stat
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The categories of this paper are: patt-sol
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The categories of this paper are: atom-ph
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The categories of this paper are: alg-geom
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The categories of this paper are: eess
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The categories of this paper are: q-bio
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The categories of this paper are: nlin
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The categories of this paper are: funct-an
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The categories of this paper are: astro-ph