Yuto Nishida
2022
NAIST-NICT-TIT WMT22 General MT Task Submission
Hiroyuki Deguchi
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Kenji Imamura
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Masahiro Kaneko
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Yuto Nishida
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Yusuke Sakai
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Justin Vasselli
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Huy Hien Vu
|
Taro Watanabe
Proceedings of the Seventh Conference on Machine Translation (WMT)
In this paper, we describe our NAIST-NICT-TIT submission to the WMT22 general machine translation task. We participated in this task for the English ↔ Japanese language pair.Our system is characterized as an ensemble of Transformer big models, k-nearest-neighbor machine translation (kNN-MT) (Khandelwal et al., 2021), and reranking.In our translation system, we construct the datastore for kNN-MT from back-translated monolingual data and integrate kNN-MT into the ensemble model. We designed a reranking system to select a translation from the n-best translation candidates generated by the translation system. We also use a context-aware model to improve the document-level consistency of the translation.
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Co-authors
- Hiroyuki Deguchi 1
- Kenji Imamura 1
- Masahiro Kaneko 1
- Yusuke Sakai 1
- Justin Vasselli 1
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- wmt1