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Parallel Corpora of Scholarly Documents for English-French Machine Translation

The growing ability of large language models (LLMs) to process long-range context opens new perspectives for document-level machine translation (MT), especially in scholarly communication. In fact, translating scholarly texts requires to integrate both local and long-range contextual information to ensure the consistency and coherence across the full document. However, document-level parallel corpora for such text types remain scarce, limiting both evaluation and domain adaptation of MT systems for this task. To address this gap, we introduce ParaEPS (Earth and Planetary Sciences Bilingual Corpus) and ParaNLP (Natural Language Processing Bilingual Corpus), two new parallel corpora covering 14k abstracts and 103 full-length articles in two scientific domains to be used for fine-tuning and evaluation purposes. We compare the performance of eight MT systems on these test sets and find that fine-tuning on document-level data closes the gap between open systems based on Large Language Models (LLMs) and commercial systems. We also find that the performance of recent LLMs can worsen when translating full articles instead of translating them on a per paragraph basisfine-tuning. These experiments underscore the need for corpora such as ParaEPS and ParaNLP.