人工知能学会全国大会論文集
33rd Annual Conference, 2019
セッションID: 3B4-E-2-04
会議情報

Application of Unsupervised NMT Technique to Japanese--Chinese Machine Translation
*Yuting ZHAOLongtu ZHANGMamoru KOMACHI
著者情報
キーワード: NLP
会議録・要旨集 フリー

詳細
抄録

Neural machine translation (NMT) often suffers in low-resource scenarios where sufficiently large-scale parallel corpora cannot be obtained. Therefore, a recent line of unsupervised NMT models based on monolingual corpus is emerging. In this work, we perform three sets of experiments that analyze the application of unsupervised NMT model in Japanese--Chinese machine translation. We report 30.13 BLEU points for ZH--JA and 23.42 BLEU points for JA--ZH.

著者関連情報
© 2019 The Japanese Society for Artificial Intelligence
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