Neural machine translation system using a content-equivalently translated parallel corpus for the newswire translation tasks at WAT 2019

Abstract

This paper describes NHK and NHK Engineering System (NHK-ES)'s submission to the newswire translation tasks of WAT 2019 in both directions of Japanese→English and English→Japanese. In addition to the JIJI Corpus that was officially provided by the task organizer, we developed a corpus of 0.22M sentence pairs by manually, translating Japanese news sentences into English content-equivalently. The content-equivalent corpus was effective for improving translation quality, and our systems achieved the best human evaluation scores in the newswire translation tasks at WAT 2019.