Towards a Cleaner Document-Oriented Multilingual Crawled Corpus

Julien Abadji, Pedro Ortiz Suarez, Laurent Romary, Benoît Sagot


Abstract
The need for large corpora raw corpora has dramatically increased in recent years with the introduction of transfer learning and semi-supervised learning methods to Natural Language Processing. And while there have been some recent attempts to manually curate the amount of data necessary to train large language models, the main way to obtain this data is still through automatic web crawling. In this paper we take the existing multilingual web corpus OSCAR and its pipeline Ungoliant that extracts and classifies data from Common Crawl at the line level, and propose a set of improvements and automatic annotations in order to produce a new document-oriented version of OSCAR that could prove more suitable to pre-train large generative language models as well as hopefully other applications in Natural Language Processing and Digital Humanities.
Anthology ID:
2022.lrec-1.463
Volume:
Proceedings of the Thirteenth Language Resources and Evaluation Conference
Month:
June
Year:
2022
Address:
Marseille, France
Venue:
LREC
SIG:
Publisher:
European Language Resources Association
Note:
Pages:
4344–4355
Language:
URL:
https://aclanthology.org/2022.lrec-1.463
DOI:
Bibkey:
Cite (ACL):
Julien Abadji, Pedro Ortiz Suarez, Laurent Romary, and Benoît Sagot. 2022. Towards a Cleaner Document-Oriented Multilingual Crawled Corpus. In Proceedings of the Thirteenth Language Resources and Evaluation Conference, pages 4344–4355, Marseille, France. European Language Resources Association.
Cite (Informal):
Towards a Cleaner Document-Oriented Multilingual Crawled Corpus (Abadji et al., LREC 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.lrec-1.463.pdf
Data
C4CCNetOSCAR