@inproceedings{cui-etal-2020-university,
title = "{U}niversity of {T}sukuba{'}s Machine Translation System for {IWSLT}20 Open Domain Translation Task",
author = "Cui, Hongyi and
Wei, Yizhen and
Iida, Shohei and
Utsuro, Takehito and
Nagata, Masaaki",
editor = {Federico, Marcello and
Waibel, Alex and
Knight, Kevin and
Nakamura, Satoshi and
Ney, Hermann and
Niehues, Jan and
St{\"u}ker, Sebastian and
Wu, Dekai and
Mariani, Joseph and
Yvon, Francois},
booktitle = "Proceedings of the 17th International Conference on Spoken Language Translation",
month = jul,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.iwslt-1.17",
doi = "10.18653/v1/2020.iwslt-1.17",
pages = "145--148",
abstract = "In this paper, we introduce University of Tsukuba{'}s submission to the IWSLT20 Open Domain Translation Task. We participate in both Chinese→Japanese and Japanese→Chinese directions. For both directions, our machine translation systems are based on the Transformer architecture. Several techniques are integrated in order to boost the performance of our models: data filtering, large-scale noised training, model ensemble, reranking and postprocessing. Consequently, our efforts achieve 33.0 BLEU scores for Chinese→Japanese translation and 32.3 BLEU scores for Japanese→Chinese translation.",
}
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%0 Conference Proceedings
%T University of Tsukuba’s Machine Translation System for IWSLT20 Open Domain Translation Task
%A Cui, Hongyi
%A Wei, Yizhen
%A Iida, Shohei
%A Utsuro, Takehito
%A Nagata, Masaaki
%Y Federico, Marcello
%Y Waibel, Alex
%Y Knight, Kevin
%Y Nakamura, Satoshi
%Y Ney, Hermann
%Y Niehues, Jan
%Y Stüker, Sebastian
%Y Wu, Dekai
%Y Mariani, Joseph
%Y Yvon, Francois
%S Proceedings of the 17th International Conference on Spoken Language Translation
%D 2020
%8 July
%I Association for Computational Linguistics
%C Online
%F cui-etal-2020-university
%X In this paper, we introduce University of Tsukuba’s submission to the IWSLT20 Open Domain Translation Task. We participate in both Chinese→Japanese and Japanese→Chinese directions. For both directions, our machine translation systems are based on the Transformer architecture. Several techniques are integrated in order to boost the performance of our models: data filtering, large-scale noised training, model ensemble, reranking and postprocessing. Consequently, our efforts achieve 33.0 BLEU scores for Chinese→Japanese translation and 32.3 BLEU scores for Japanese→Chinese translation.
%R 10.18653/v1/2020.iwslt-1.17
%U https://aclanthology.org/2020.iwslt-1.17
%U https://doi.org/10.18653/v1/2020.iwslt-1.17
%P 145-148
Markdown (Informal)
[University of Tsukuba’s Machine Translation System for IWSLT20 Open Domain Translation Task](https://aclanthology.org/2020.iwslt-1.17) (Cui et al., IWSLT 2020)
ACL