NOOV模型提升英文病历翻译成西班牙语的准确与流畅度
A New NMT Model for Translating Clinical Texts from English to Spanish
- 结合自学习词典与生物医学知识库，缓解未知词问题
- 在少量领域对齐语料下实现翻译准确率与流畅度提升
- 适合医疗文本翻译场景，尤其适用于资源稀缺语言对
将电子健康记录（EHR）文本从英语翻译为西班牙语是临床应用中重要但极具挑战的任务，主要因缺乏平行对齐语料且包含大量未知词。为此，我们提出新型神经机器翻译（NMT）系统NOOV（No OOV），该系统训练所需领域内平行对齐语料极少。NOOV融合从平行语料中自动学习的双语词典与从大型生物医学知识资源中提取的短语查表，有效缓解未知词问题与词汇重复挑战，提升NMT系统的短语生成能力。评估结果表明，NOOV在生成EHR翻译时，准确率与流畅度均显著优于基线模型。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。