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    <name type="personal">
        <namePart type="given">Hiroyuki</namePart>
        <namePart type="family">Deguchi</namePart>
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    <name type="personal">
        <namePart type="given">Kenji</namePart>
        <namePart type="family">Imamura</namePart>
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    <name type="personal">
        <namePart type="given">Yusuke</namePart>
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    <abstract>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.</abstract>
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%0 Conference Proceedings
%T NAIST-NICT-TIT WMT22 General MT Task Submission
%A Deguchi, Hiroyuki
%A Imamura, Kenji
%A Kaneko, Masahiro
%A Nishida, Yuto
%A Sakai, Yusuke
%A Vasselli, Justin
%A Vu, Huy Hien
%A Watanabe, Taro
%S Proceedings of the Seventh Conference on Machine Translation (WMT)
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates (Hybrid)
%F deguchi-etal-2022-naist
%X 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.
%U https://aclanthology.org/2022.wmt-1.16
%P 244-250

Markdown (Informal)

[NAIST-NICT-TIT WMT22 General MT Task Submission](https://aclanthology.org/2022.wmt-1.16) (Deguchi et al., WMT 2022)

• NAIST-NICT-TIT WMT22 General MT Task Submission (Deguchi et al., WMT 2022)

ACL

• Hiroyuki Deguchi, Kenji Imamura, Masahiro Kaneko, Yuto Nishida, Yusuke Sakai, Justin Vasselli, Huy Hien Vu, and Taro Watanabe. 2022. NAIST-NICT-TIT WMT22 General MT Task Submission. In Proceedings of the Seventh Conference on Machine Translation (WMT), pages 244–250, Abu Dhabi, United Arab Emirates (Hybrid). Association for Computational Linguistics.