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Introducing a Massive New Multilingual Dataset for Machine Translation

6 / 5 / 2024

A recent paper by researchers from several European universities unveiled the HPLT (High Performance Language Technologies) dataset for language modeling and machine translation. This dataset incorporates monolingual and bilingual corpora sourced from web crawls by the Internet Archive and CommonCrawl, a first at this scale.

According to the team, these resources are among the largest open text corpora ever released, providing invaluable material for language modeling and machine translation training.


They detailed their acquisition and processing methods, leveraging open-source software tools and high-performance computing, and have made everything publicly available on GitHub as a model for others.

Covering 75 languages, including many low- to medium-resourced ones, the monolingual collection comprises a whopping 5.25 billion documents. Additionally, the parallel corpus, mainly focused on English, offers 18 language pairs with over 96 million aligned sentence pairs, with a specific emphasis on low-resource languages to bolster MT development.

Moreover, the researchers created a synthetic dataset by pivoting existing parallel datasets through English, totaling 171 language pairs and 157 million sentence pairs. All datasets include metadata for user filtering. In addition to the datasets, the researchers have released initial MT models and large language models (LLMs) along with their training pipelines.

This development highlights the changing world of translation. Machine learning is taking center stage, boosting translation services and paving the way for the future of this art form.

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