[4Rin1] Interactive 2

[4Rin1-44] Controlling the Number of Output Sentences in Neural Machine Translation

〇Hitoshi Ito¹, Hideya Mino¹, Isao Goto¹, Ichiro Yamada¹ (1.Japan Broadcasting Corp.)

Keywords:Machine Translation

We study Japanese-English machine translation technology to support NHK's production of English news. NHK Japanese news tends to have long sentences, and English news tends to translate a Japanese sentence into multiple English sentences. Therefore, it is necessary to translate English with an appropriate number of sentences. In this paper, we propose a method to control the number of sentences in a neural machine translation (NMT). The proposed method trains the NMT model by adding a tag indicating the number of English sentences to the top of Japanese sentence. We evaluated our method with Japanese-English bilingual sentences of news, and we confirmed that the proposed method improved the rate of coincidence between the number of the output sentences and the number of the reference sentences from 60.4% to 96.6%.