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arXiv · 2609.26347

TransBERT: A Framework for Synthetic Translation in Domain-Specific Language Modeling

Julien Knafou·Luc Mottin·Anaïs Mottaz·Alexandre Flament·Patrick Ruch
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Abstract

The scarcity of non-English language data in specialized domains significantly limits the development of effective Natural Language Processing (NLP) tools. We present TransBERT, a novel framework for pre-training language models using exclusively synthetically translated text, and introduce TransCorpus, a scalable translation toolkit. Focusing on the life sciences domain in French, our approach demonstrates that state-of-the-art performance on various downstream tasks can be achieved solely by leveraging synthetically translated data. We release the TransCorpus toolkit, the TransCorpus-bio-fr corpus (36.4GB of French life sciences text), TransBERT-bio-fr, its associated pre-trained language model and reproducible code for both pre-training and fine-tuning. Our results highlight the viability of synthetic translation in a high-resource translation direction for building high-quality NLP resources in low-resource language/domain pairs.

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Julien Knafou, Luc Mottin, Anaïs Mottaz, Alexandre Flament, Patrick Ruch. 2026-09-22. TransBERT: A Framework for Synthetic Translation in Domain-Specific Language Modeling. https://doi.org/10.18653/v1%2F2025.findings-emnlp.1053

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