TransBERT: A Framework for Synthetic Translation in Domain-Specific Language Modeling
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Computer Science > Computation and Language
Title:TransBERT: A Framework for Synthetic Translation in Domain-Specific Language Modeling
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.
| Comments: | 17 pages |
| Subjects: | Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG) |
| ACM classes: | I.2.7 |
| Cite as: | arXiv:2609.26347 [cs.CL] |
| (or arXiv:2609.26347v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2609.26347
arXiv-issued DOI via DataCite (pending registration)
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| Journal reference: | Findings of the Association for Computational Linguistics: EMNLP 2025, pages 19338-19354 |
| Related DOI: | https://doi.org/10.18653/v1/2025.findings-emnlp.1053
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