@inproceedings{imamura-sumita-2019-long,
title = "Long Warm-up and Self-Training: Training Strategies of {NICT}-2 {NMT} System at {WAT}-2019",
author = "Imamura, Kenji and
Sumita, Eiichiro",
editor = "Nakazawa, Toshiaki and
Ding, Chenchen and
Dabre, Raj and
Kunchukuttan, Anoop and
Doi, Nobushige and
Oda, Yusuke and
Bojar, Ond{\v{r}}ej and
Parida, Shantipriya and
Goto, Isao and
Mino, Hidaya",
booktitle = "Proceedings of the 6th Workshop on Asian Translation",
month = nov,
year = "2019",
address = "Hong Kong, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D19-5217",
doi = "10.18653/v1/D19-5217",
pages = "141--146",
abstract = "This paper describes the NICT-2 neural machine translation system at the 6th Workshop on Asian Translation. This system employs the standard Transformer model but features the following two characteristics. One is the long warm-up strategy, which performs a longer warm-up of the learning rate at the start of the training than conventional approaches. Another is that the system introduces self-training approaches based on multiple back-translations generated by sampling. We participated in three tasks{---}ASPEC.en-ja, ASPEC.ja-en, and TDDC.ja-en{---}using this system.",
}
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%0 Conference Proceedings
%T Long Warm-up and Self-Training: Training Strategies of NICT-2 NMT System at WAT-2019
%A Imamura, Kenji
%A Sumita, Eiichiro
%Y Nakazawa, Toshiaki
%Y Ding, Chenchen
%Y Dabre, Raj
%Y Kunchukuttan, Anoop
%Y Doi, Nobushige
%Y Oda, Yusuke
%Y Bojar, Ondřej
%Y Parida, Shantipriya
%Y Goto, Isao
%Y Mino, Hidaya
%S Proceedings of the 6th Workshop on Asian Translation
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong, China
%F imamura-sumita-2019-long
%X This paper describes the NICT-2 neural machine translation system at the 6th Workshop on Asian Translation. This system employs the standard Transformer model but features the following two characteristics. One is the long warm-up strategy, which performs a longer warm-up of the learning rate at the start of the training than conventional approaches. Another is that the system introduces self-training approaches based on multiple back-translations generated by sampling. We participated in three tasks—ASPEC.en-ja, ASPEC.ja-en, and TDDC.ja-en—using this system.
%R 10.18653/v1/D19-5217
%U https://aclanthology.org/D19-5217
%U https://doi.org/10.18653/v1/D19-5217
%P 141-146
Markdown (Informal)
[Long Warm-up and Self-Training: Training Strategies of NICT-2 NMT System at WAT-2019](https://aclanthology.org/D19-5217) (Imamura & Sumita, WAT 2019)
ACL