ACAData: Parallel Dataset of Academic Data for Machine Translation

Abstract

We present ACAData, a high-quality parallel dataset for academic translation, that consists of two subsets: ACAD-Train, which contains approximately 1.5 million human-generated paragraph pairs across 12 languages, and ACAD-Bench, a curated evaluation set of almost 6,000 translations covering 12 directions. To validate its usefulness, we fine-tune two Large Language Models (LLMs) on ACAD-Train and benchmark them on ACAD-Bench against specialized machine-translation systems, general-purpose, open-weight LLMs, and several large-scale proprietary models. Experimental results demonstrate that fine tuning on ACAD-Train leads to improvements in academic translation quality by +6.1 and +12.4 d-BLEU points on average for 7B and 2B models respectively, while also improving long-context translation in a general domain by up to 24.9% when translating out of English. The fine-tuned top-performing model surpasses the best proprietary and open-weight models on the academic translation domain. By releasing ACAD-Train, ACAD-Bench and the fine-tuned models, we provide the community with a valuable resource to advance research in the academic domain and long-context translation.

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