@inproceedings{dabre-sumita-2019-nicts-supervised,
title = "{NICT}`s Supervised Neural Machine Translation Systems for the {WMT}19 Translation Robustness Task",
author = "Dabre, Raj and
Sumita, Eiichiro",
editor = "Bojar, Ond{\v{r}}ej and
Chatterjee, Rajen and
Federmann, Christian and
Fishel, Mark and
Graham, Yvette and
Haddow, Barry and
Huck, Matthias and
Yepes, Antonio Jimeno and
Koehn, Philipp and
Martins, Andr{\'e} and
Monz, Christof and
Negri, Matteo and
N{\'e}v{\'e}ol, Aur{\'e}lie and
Neves, Mariana and
Post, Matt and
Turchi, Marco and
Verspoor, Karin",
booktitle = "Proceedings of the Fourth Conference on Machine Translation (Volume 2: Shared Task Papers, Day 1)",
month = aug,
year = "2019",
address = "Florence, Italy",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W19-5362/",
doi = "10.18653/v1/W19-5362",
pages = "533--536",
abstract = "In this paper we describe our neural machine translation (NMT) systems for Japanese{\ensuremath{\leftrightarrow}}English translation which we submitted to the translation robustness task. We focused on leveraging transfer learning via fine tuning to improve translation quality. We used a fairly well established domain adaptation technique called Mixed Fine Tuning (MFT) (Chu et. al., 2017) to improve translation quality for Japanese{\ensuremath{\leftrightarrow}}English. We also trained bi-directional NMT models instead of uni-directional ones as the former are known to be quite robust, especially in low-resource scenarios. However, given the noisy nature of the in-domain training data, the improvements we obtained are rather modest."
}
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<abstract>In this paper we describe our neural machine translation (NMT) systems for Japanese\ensuremathłeftrightarrowEnglish translation which we submitted to the translation robustness task. We focused on leveraging transfer learning via fine tuning to improve translation quality. We used a fairly well established domain adaptation technique called Mixed Fine Tuning (MFT) (Chu et. al., 2017) to improve translation quality for Japanese\ensuremathłeftrightarrowEnglish. We also trained bi-directional NMT models instead of uni-directional ones as the former are known to be quite robust, especially in low-resource scenarios. However, given the noisy nature of the in-domain training data, the improvements we obtained are rather modest.</abstract>
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%0 Conference Proceedings
%T NICT‘s Supervised Neural Machine Translation Systems for the WMT19 Translation Robustness Task
%A Dabre, Raj
%A Sumita, Eiichiro
%Y Bojar, Ondřej
%Y Chatterjee, Rajen
%Y Federmann, Christian
%Y Fishel, Mark
%Y Graham, Yvette
%Y Haddow, Barry
%Y Huck, Matthias
%Y Yepes, Antonio Jimeno
%Y Koehn, Philipp
%Y Martins, André
%Y Monz, Christof
%Y Negri, Matteo
%Y Névéol, Aurélie
%Y Neves, Mariana
%Y Post, Matt
%Y Turchi, Marco
%Y Verspoor, Karin
%S Proceedings of the Fourth Conference on Machine Translation (Volume 2: Shared Task Papers, Day 1)
%D 2019
%8 August
%I Association for Computational Linguistics
%C Florence, Italy
%F dabre-sumita-2019-nicts-supervised
%X In this paper we describe our neural machine translation (NMT) systems for Japanese\ensuremathłeftrightarrowEnglish translation which we submitted to the translation robustness task. We focused on leveraging transfer learning via fine tuning to improve translation quality. We used a fairly well established domain adaptation technique called Mixed Fine Tuning (MFT) (Chu et. al., 2017) to improve translation quality for Japanese\ensuremathłeftrightarrowEnglish. We also trained bi-directional NMT models instead of uni-directional ones as the former are known to be quite robust, especially in low-resource scenarios. However, given the noisy nature of the in-domain training data, the improvements we obtained are rather modest.
%R 10.18653/v1/W19-5362
%U https://aclanthology.org/W19-5362/
%U https://doi.org/10.18653/v1/W19-5362
%P 533-536
Markdown (Informal)
[NICT’s Supervised Neural Machine Translation Systems for the WMT19 Translation Robustness Task](https://aclanthology.org/W19-5362/) (Dabre & Sumita, WMT 2019)
ACL