@inproceedings{kocmi-bojar-2018-trivial,
title = "Trivial Transfer Learning for Low-Resource Neural Machine Translation",
author = "Kocmi, Tom and
Bojar, Ond{\v{r}}ej",
booktitle = "Proceedings of the Third Conference on Machine Translation: Research Papers",
month = oct,
year = "2018",
address = "Brussels, Belgium",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W18-6325",
doi = "10.18653/v1/W18-6325",
pages = "244--252",
abstract = "Transfer learning has been proven as an effective technique for neural machine translation under low-resource conditions. Existing methods require a common target language, language relatedness, or specific training tricks and regimes. We present a simple transfer learning method, where we first train a {``}parent{''} model for a high-resource language pair and then continue the training on a low-resource pair only by replacing the training corpus. This {``}child{''} model performs significantly better than the baseline trained for low-resource pair only. We are the first to show this for targeting different languages, and we observe the improvements even for unrelated languages with different alphabets.",
}
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<abstract>Transfer learning has been proven as an effective technique for neural machine translation under low-resource conditions. Existing methods require a common target language, language relatedness, or specific training tricks and regimes. We present a simple transfer learning method, where we first train a “parent” model for a high-resource language pair and then continue the training on a low-resource pair only by replacing the training corpus. This “child” model performs significantly better than the baseline trained for low-resource pair only. We are the first to show this for targeting different languages, and we observe the improvements even for unrelated languages with different alphabets.</abstract>
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%0 Conference Proceedings
%T Trivial Transfer Learning for Low-Resource Neural Machine Translation
%A Kocmi, Tom
%A Bojar, Ondřej
%S Proceedings of the Third Conference on Machine Translation: Research Papers
%D 2018
%8 October
%I Association for Computational Linguistics
%C Brussels, Belgium
%F kocmi-bojar-2018-trivial
%X Transfer learning has been proven as an effective technique for neural machine translation under low-resource conditions. Existing methods require a common target language, language relatedness, or specific training tricks and regimes. We present a simple transfer learning method, where we first train a “parent” model for a high-resource language pair and then continue the training on a low-resource pair only by replacing the training corpus. This “child” model performs significantly better than the baseline trained for low-resource pair only. We are the first to show this for targeting different languages, and we observe the improvements even for unrelated languages with different alphabets.
%R 10.18653/v1/W18-6325
%U https://aclanthology.org/W18-6325
%U https://doi.org/10.18653/v1/W18-6325
%P 244-252
Markdown (Informal)
[Trivial Transfer Learning for Low-Resource Neural Machine Translation](https://aclanthology.org/W18-6325) (Kocmi & Bojar, WMT 2018)
ACL