신경망기계번역의 객관적 평가를 위한 예비연구: 자동평가와 수동평가의 균형점

초록

Neural Machine Translation (NMT) is known to produce better translation than Statistic Machine Translation(SMT). It can be natural that scholars and media show more interest in the emerging translation technology. However, it is difficult to define what makes a translation better than others. Furthermore, the claims about the NMT quality and wild guess about the future of translation are sometimes misleading and even worrisome. This means more objective and reliable diagnosis about the current state of NMT is necessary so that potential users and readers of machine translation can make informed decisions. Hence, this research aims to investigate if the media's approach, mainly driven by automatic evaluation, is valid and to review what has been considered as important aspects for translation evaluation in the previous translation studies. As a next step toward a more objective diagnosis, this research tries to compare three Neural Machine Translation (NMT) services available in Korea and assess their translation quality from various perspectives. In total, eight translation experts conducted a series of quality assessment on Korean to English and English and Korean translation. The analysis of the experts input will provide a few meaningful findings that can help build more objective yet prudent views on neural machine translation.

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