인공신경망 특허 기계번역 성능에 관한 연구 - Patent Translate와 WIPO Translate 한영 번역 결과물의 누락과 통사 오류 분석을 중심으로 -

Jieun Lee and Hyoeun Choi (2022). A study on the quality of patent neural machine translation: A comparison of omission and syntactic errors in the Korean-English translations by patent-specialized Patent Translate and WIPO Translate. This paper aims to evaluate the quality of patent translations produced by the two patent-specialized machine translation engines, EPO’s Patent Translate and WIPO’s WIPO Translate. For manual evaluation, four experienced patent translators or patent translation service managers evaluated the quality of 106 English sentences from the translations of 30 Korean patent abstracts by the two MT engines. In the automatic evaluation, Patent Translate slightly outperformed WIPO Translate, whereas in the manual evaluation WIPO Translate outperformed Patent Translate. According to the error annotations provided by the evaluators, WIPO Translate produced more omission errors than Patent Translate but handled the complex syntax of the source text better, while Patent Translate produced more syntactic errors than WIPO Translate. The results indicate that in automatic evaluation, MT outputs with fewer omissions were rated higher, while in manual evaluation, comprehensible and accurate syntactic structures appeared to determine the overall quality evaluation. (Ewha Womans University, Korea)