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.
Abstract (translated)
翻译电子健康记录（EHR）叙述从英语到西班牙语是一项临床重要但具有挑战性的任务，由于缺乏平行对齐的语料库和大量未知词汇的存在。为了解决这些挑战，我们提出了**NOOV**（无OOV），这是一种新的神经机器翻译（NMT）系统，它需要很少量的领域内平行对齐的语料库进行训练。NOOV集成了从平行对齐语料库自动学习到的双语词典和从大型生物医学知识资源中提取的短语查找表，以减轻NMT中的未知词汇问题和重复单词挑战，从而增强NMT系统在生成短语方面的表现。评估表明，NOOV能够更准确、流畅地翻译EHR。
URL
https://arxiv.org/abs/2508.18607
