@inproceedings{dabre-chakrabarty-2020-nicts,
title = "{NICT}{`}s Submission To {WAT} 2020: How Effective Are Simple Many-To-Many Neural Machine Translation Models?",
author = "Dabre, Raj and
Chakrabarty, Abhisek",
editor = "Nakazawa, Toshiaki and
Nakayama, Hideki and
Ding, Chenchen and
Dabre, Raj and
Kunchukuttan, Anoop and
Pa, Win Pa and
Bojar, Ond{\v{r}}ej and
Parida, Shantipriya and
Goto, Isao and
Mino, Hidaya and
Manabe, Hiroshi and
Sudoh, Katsuhito and
Kurohashi, Sadao and
Bhattacharyya, Pushpak",
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-1.9",
doi = "10.18653/v1/2020.wat-1.9",
pages = "98--102",
abstract = "In this paper we describe our team{`}s (NICT-5) Neural Machine Translation (NMT) models whose translations were submitted to shared tasks of the 7th Workshop on Asian Translation. We participated in the Indic language multilingual sub-task as well as the NICT-SAP multilingual multi-domain sub-task. We focused on naive many-to-many NMT models which gave reasonable translation quality despite their simplicity. Our observations are twofold: (a.) Many-to-many models suffer from a lack of consistency where the translation quality for some language pairs is very good but for some others it is terrible when compared against one-to-many and many-to-one baselines. (b.) Oversampling smaller corpora does not necessarily give the best translation quality for the language pair associated with that pair.",
}
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<abstract>In this paper we describe our team‘s (NICT-5) Neural Machine Translation (NMT) models whose translations were submitted to shared tasks of the 7th Workshop on Asian Translation. We participated in the Indic language multilingual sub-task as well as the NICT-SAP multilingual multi-domain sub-task. We focused on naive many-to-many NMT models which gave reasonable translation quality despite their simplicity. Our observations are twofold: (a.) Many-to-many models suffer from a lack of consistency where the translation quality for some language pairs is very good but for some others it is terrible when compared against one-to-many and many-to-one baselines. (b.) Oversampling smaller corpora does not necessarily give the best translation quality for the language pair associated with that pair.</abstract>
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%0 Conference Proceedings
%T NICT‘s Submission To WAT 2020: How Effective Are Simple Many-To-Many Neural Machine Translation Models?
%A Dabre, Raj
%A Chakrabarty, Abhisek
%Y Nakazawa, Toshiaki
%Y Nakayama, Hideki
%Y Ding, Chenchen
%Y Dabre, Raj
%Y Kunchukuttan, Anoop
%Y Pa, Win Pa
%Y Bojar, Ondřej
%Y Parida, Shantipriya
%Y Goto, Isao
%Y Mino, Hidaya
%Y Manabe, Hiroshi
%Y Sudoh, Katsuhito
%Y Kurohashi, Sadao
%Y Bhattacharyya, Pushpak
%S Proceedings of the 7th Workshop on Asian Translation
%D 2020
%8 December
%I Association for Computational Linguistics
%C Suzhou, China
%F dabre-chakrabarty-2020-nicts
%X In this paper we describe our team‘s (NICT-5) Neural Machine Translation (NMT) models whose translations were submitted to shared tasks of the 7th Workshop on Asian Translation. We participated in the Indic language multilingual sub-task as well as the NICT-SAP multilingual multi-domain sub-task. We focused on naive many-to-many NMT models which gave reasonable translation quality despite their simplicity. Our observations are twofold: (a.) Many-to-many models suffer from a lack of consistency where the translation quality for some language pairs is very good but for some others it is terrible when compared against one-to-many and many-to-one baselines. (b.) Oversampling smaller corpora does not necessarily give the best translation quality for the language pair associated with that pair.
%R 10.18653/v1/2020.wat-1.9
%U https://aclanthology.org/2020.wat-1.9
%U https://doi.org/10.18653/v1/2020.wat-1.9
%P 98-102
Markdown (Informal)
[NICT‘s Submission To WAT 2020: How Effective Are Simple Many-To-Many Neural Machine Translation Models?](https://aclanthology.org/2020.wat-1.9) (Dabre & Chakrabarty, WAT 2020)
ACL