@inproceedings{jauregi-unanue-etal-2019-rewe,
title = "{R}e{WE}: Regressing Word Embeddings for Regularization of Neural Machine Translation Systems",
author = "Jauregi Unanue, Inigo and
Zare Borzeshi, Ehsan and
Esmaili, Nazanin and
Piccardi, Massimo",
editor = "Burstein, Jill and
Doran, Christy and
Solorio, Thamar",
booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
month = jun,
year = "2019",
address = "Minneapolis, Minnesota",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/N19-1041",
doi = "10.18653/v1/N19-1041",
pages = "430--436",
abstract = "Regularization of neural machine translation is still a significant problem, especially in low-resource settings. To mollify this problem, we propose regressing word embeddings (ReWE) as a new regularization technique in a system that is jointly trained to predict the next word in the translation (categorical value) and its word embedding (continuous value). Such a joint training allows the proposed system to learn the distributional properties represented by the word embeddings, empirically improving the generalization to unseen sentences. Experiments over three translation datasets have showed a consistent improvement over a strong baseline, ranging between 0.91 and 2.4 BLEU points, and also a marked improvement over a state-of-the-art system.",
}
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<abstract>Regularization of neural machine translation is still a significant problem, especially in low-resource settings. To mollify this problem, we propose regressing word embeddings (ReWE) as a new regularization technique in a system that is jointly trained to predict the next word in the translation (categorical value) and its word embedding (continuous value). Such a joint training allows the proposed system to learn the distributional properties represented by the word embeddings, empirically improving the generalization to unseen sentences. Experiments over three translation datasets have showed a consistent improvement over a strong baseline, ranging between 0.91 and 2.4 BLEU points, and also a marked improvement over a state-of-the-art system.</abstract>
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%0 Conference Proceedings
%T ReWE: Regressing Word Embeddings for Regularization of Neural Machine Translation Systems
%A Jauregi Unanue, Inigo
%A Zare Borzeshi, Ehsan
%A Esmaili, Nazanin
%A Piccardi, Massimo
%Y Burstein, Jill
%Y Doran, Christy
%Y Solorio, Thamar
%S Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)
%D 2019
%8 June
%I Association for Computational Linguistics
%C Minneapolis, Minnesota
%F jauregi-unanue-etal-2019-rewe
%X Regularization of neural machine translation is still a significant problem, especially in low-resource settings. To mollify this problem, we propose regressing word embeddings (ReWE) as a new regularization technique in a system that is jointly trained to predict the next word in the translation (categorical value) and its word embedding (continuous value). Such a joint training allows the proposed system to learn the distributional properties represented by the word embeddings, empirically improving the generalization to unseen sentences. Experiments over three translation datasets have showed a consistent improvement over a strong baseline, ranging between 0.91 and 2.4 BLEU points, and also a marked improvement over a state-of-the-art system.
%R 10.18653/v1/N19-1041
%U https://aclanthology.org/N19-1041
%U https://doi.org/10.18653/v1/N19-1041
%P 430-436
Markdown (Informal)
[ReWE: Regressing Word Embeddings for Regularization of Neural Machine Translation Systems](https://aclanthology.org/N19-1041) (Jauregi Unanue et al., NAACL 2019)
ACL