ASCAT: Arabic Scientific Benchmark for Advanced Translation Evaluation

Le résumé fourni par la source

We present ASCAT (Arabic Scientific Corpus for Advanced Translation), a high-quality English-Arabic parallel benchmark corpus designed for scientific translation evaluation constructed through a systematic multi-engine translation and expert post-editing pipeline.Unlike existing Arabic-English corpora that rely on short sentences or single-domain text, ASCAT targets full scientific abstracts averaging 125.3 words (English) and 111.78 words (Arabic), drawn from five scientific domains: physics, mathematics, computer science, quantum mechanics, and artificial intelligence.Each abstract was translated using three complementary architectures: generative AI (Gemini), transformer-based models (Hugging Face quickmt-en-ar), and commercial MT API (Google Translate), and subsequently post-edited by domain experts at the lexical, syntactic, and semantic levels.The resulting corpus contains 67,293 English tokens and 60,026 Arabic tokens, with an Arabic vocabulary of 17,604 unique words reflecting the morphological richness of the language.We benchmark three state-of-the-art LLMs on the corpus: GPT-4o-mini (BLEU: 37.07), Gemini-3.0-Flash-Preview(BLEU: 30.44), and Qwen3-235B-A22B (BLEU: 23.68) demonstrating its discriminative power as an evaluation benchmark.ASCAT addresses a critical gap in scientific MT resources for Arabic and is designed to support rigorous evaluation of scientific translation quality and training of domain-specific translation models.

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