Pith. sign in

REVIEW 1 cited by

Quality at a Glance: An Audit of Web-Crawled Multilingual Datasets

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2103.12028 v4 pith:KXAFCM2V submitted 2021-03-22 cs.CL cs.AI

Julia Kreutzer , Isaac Caswell , Lisa Wang , Ahsan Wahab , Daan van Esch , Nasanbayar Ulzii-Orshikh , Allahsera Tapo , Nishant Subramani
show 44 more authors
Artem Sokolov Claytone Sikasote Monang Setyawan Supheakmungkol Sarin Sokhar Samb Benoît Sagot Clara Rivera Annette Rios Isabel Papadimitriou Salomey Osei Pedro Ortiz Suarez Iroro Orife Kelechi Ogueji Andre Niyongabo Rubungo Toan Q. Nguyen Mathias Müller André Müller Shamsuddeen Hassan Muhammad Nanda Muhammad Ayanda Mnyakeni Jamshidbek Mirzakhalov Tapiwanashe Matangira Colin Leong Nze Lawson Sneha Kudugunta Yacine Jernite Mathias Jenny Orhan Firat Bonaventure F. P. Dossou Sakhile Dlamini Nisansa de Silva Sakine Çabuk Ball{i} Stella Biderman Alessia Battisti Ahmed Baruwa Ankur Bapna Pallavi Baljekar Israel Abebe Azime Ayodele Awokoya Duygu Ataman Orevaoghene Ahia Oghenefego Ahia Sweta Agrawal Mofetoluwa Adeyemi
This is my paper · ORCID
classification cs.CLcs.AI
keywords corporaauditdatasetsmultilingualqualityissueslanguagetext
verification ladder T0 review T1 audit T2 compute T3 formal

Signed reviews

No signed human review yet.

0 comments
read the original abstract

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.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Bridging the Data Provenance Gap Across Text, Speech and Video

    cs.AI 2024-12 conditional novelty 6.0 of 10

    A manual audit of nearly 4,000 text, speech, and video datasets finds AI training data increasingly comes from web and social media sources, carries hidden non-commercial restrictions, and remains Western-centric with...

Pith tools