A New Massive Multilingual Dataset for High-Performance Language Technologies
: de Gibert, Ona; Nail, Graeme; Arefyev, Nikolay; Bañón, Marta; van der Linde, Jelmer; Ji, Shaoxiong; Zaragoza-Bernabeu, Jaume; Aulamo, Mikko; Ramírez-Sánchez, Gema; Kutuzov, Andrey; Pyysalo, Sampo; Oepen, Stephan; Tiedemann, Jörg
: Calzolari, Nicoletta; Kan, Min-Yen; Hoste, Veronique; Lenci, Alessandro; Sakti, Sakriani; Xue, Nianwen
: Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING)
Publisher: European Language Resources Association (ELRA)
: 2024
: LREC Proceedings
: Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
: 1116
: 1128
: 978-2-493814-10-4
: 2522-2686
: https://aclanthology.org/2024.lrec-main.100
: https://research.utu.fi/converis/portal/detail/Publication/457541413
We present the HPLT (High Performance Language Technologies) language resources, a new massive multilingual dataset including both monolingual and bilingual corpora extracted from CommonCrawl and previously unused web crawls from the Internet Archive. We describe our methods for data acquisition, management and processing of large corpora, which rely on open-source software tools and high-performance computing. Our monolingual collection focuses on low- to medium-resourced languages and covers 75 languages and a total of ≈ 5.6 trillion word tokens de-duplicated on the document level. Our English-centric parallel corpus is derived from its monolingual counterpart and covers 18 language pairs and more than 96 million aligned sentence pairs with roughly 1.4 billion English tokens. The HPLT language resources are one of the largest open text corpora ever released, providing a great resource for language modeling and machine translation training. We publicly release the corpora, the software, and the tools used in this work.
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This project has received funding from the European Union’s Horizon Europe research and innovation programme under Grant agreement No 101070350 and from UK Research and Innovation (UKRI) under the UK government’s Horizon Europe funding guarantee [grant number 10052546].
