{"ID":2877858,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2508.18607","arxiv_id":"2508.18607","title":"A New NMT Model for Translating Clinical Texts from English to Spanish","abstract":"Translating electronic health record (EHR) narratives from English to Spanish is a clinically important yet challenging task due to the lack of a parallel-aligned corpus and the abundant unknown words contained. To address such challenges, we propose \\textbf{NOOV} (for No OOV), a new neural machine translation (NMT) system that requires little in-domain parallel-aligned corpus for training. NOOV integrates a bilingual lexicon automatically learned from parallel-aligned corpora and a phrase look-up table extracted from a large biomedical knowledge resource, to alleviate both the unknown word problem and the word-repeat challenge in NMT, enhancing better phrase generation of NMT systems. Evaluation shows that NOOV is able to generate better translation of EHR with improvement in both accuracy and fluency.","short_abstract":"Translating electronic health record (EHR) narratives from English to Spanish is a clinically important yet challenging task due to the lack of a parallel-aligned corpus and the abundant unknown words contained. To address such challenges, we propose \\textbf{NOOV} (for No OOV), a new neural machine translation (NMT) sy...","url_abs":"https://arxiv.org/abs/2508.18607","url_pdf":"https://arxiv.org/pdf/2508.18607v1","authors":"[\"Rumeng Li\",\"Xun Wang\",\"Hong Yu\"]","published":"2025-08-26T02:24:38Z","proceeding":"cs.CL","tasks":"[\"cs.CL\"]","methods":"[]","has_code":false}