A New NMT Model for Translating Clinical Texts from English to Spanish
This work addresses a clinically important task for healthcare professionals and patients by improving translation of electronic health records, though it is incremental as it builds on existing NMT methods.
The paper tackled the problem of translating clinical texts from English to Spanish by proposing NOOV, a neural machine translation system that reduces the need for parallel-aligned corpora and addresses unknown words, resulting in improved accuracy and fluency in EHR translations.
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.