Evaluation of Machine Translation Accuracy Focused on the Adverse Event Terminology for Medical Devices.

Abstract

The purpose of this study was to evaluate the accuracy of deep neural machine translation focused on medical device adverse event terminology. 10 models were obtained, and their English-to-Japanese translation accuracy was evaluated using quantitative and qualitative measures. No significant difference was found in the quantitative index except for a few pairs. In the qualitative evaluation, there was a significant difference and googletrans and GPT-3 were regarded as useful models.