ReWE: Regressing Word Embeddings for Regularization of Neural Machine Translation Systems

Inigo Jauregi Unanue, Ehsan Zare Borzeshi, Nazanin Esmaili, Massimo Piccardi


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.
Anthology ID:
N19-1041
Volume:
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)
Month:
June
Year:
2019
Address:
Minneapolis, Minnesota
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
430–436
Language:
URL:
https://www.aclweb.org/anthology/N19-1041
DOI:
10.18653/v1/N19-1041
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PDF:
http://aclanthology.lst.uni-saarland.de/N19-1041.pdf
Supplementary:
 N19-1041.Supplementary.pdf