@inproceedings{mao-etal-2020-meta,
title = "Meta Ensemble for {J}apanese-{C}hinese Neural Machine Translation: {K}yoto-{U}+{ECNU} Participation to {WAT} 2020",
author = "Mao, Zhuoyuan and
Shen, Yibin and
Chu, Chenhui and
Kurohashi, Sadao and
Jin, Cheqing",
booktitle = "Proceedings of the 7th Workshop on Asian Translation",
month = dec,
year = "2020",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.wat-1.5",
pages = "64--71",
abstract = "This paper describes the Japanese-Chinese Neural Machine Translation (NMT) system submitted by the joint team of Kyoto University and East China Normal University (Kyoto-U+ECNU) to WAT 2020 (Nakazawa et al.,2020). We participate in APSEC Japanese-Chinese translation task. We revisit several techniques for NMT including various architectures, different data selection and augmentation methods, denoising pre-training, and also some specific tricks for Japanese-Chinese translation. We eventually perform a meta ensemble to combine all of the models into a single model. BLEU results of this meta ensembled model rank the first both on 2 directions of ASPEC Japanese-Chinese translation.",
}
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%0 Conference Proceedings
%T Meta Ensemble for Japanese-Chinese Neural Machine Translation: Kyoto-U+ECNU Participation to WAT 2020
%A Mao, Zhuoyuan
%A Shen, Yibin
%A Chu, Chenhui
%A Kurohashi, Sadao
%A Jin, Cheqing
%S Proceedings of the 7th Workshop on Asian Translation
%D 2020
%8 dec
%I Association for Computational Linguistics
%C Suzhou, China
%F mao-etal-2020-meta
%X This paper describes the Japanese-Chinese Neural Machine Translation (NMT) system submitted by the joint team of Kyoto University and East China Normal University (Kyoto-U+ECNU) to WAT 2020 (Nakazawa et al.,2020). We participate in APSEC Japanese-Chinese translation task. We revisit several techniques for NMT including various architectures, different data selection and augmentation methods, denoising pre-training, and also some specific tricks for Japanese-Chinese translation. We eventually perform a meta ensemble to combine all of the models into a single model. BLEU results of this meta ensembled model rank the first both on 2 directions of ASPEC Japanese-Chinese translation.
%U https://aclanthology.org/2020.wat-1.5
%P 64-71
Markdown (Informal)
[Meta Ensemble for Japanese-Chinese Neural Machine Translation: Kyoto-U+ECNU Participation to WAT 2020](https://aclanthology.org/2020.wat-1.5) (Mao et al., WAT 2020)
ACL