@inproceedings{madhyastha-espana-bonet-2017-learning,
title = "Learning Bilingual Projections of Embeddings for Vocabulary Expansion in Machine Translation",
author = "Madhyastha, Pranava Swaroop and
Espa{\~n}a-Bonet, Cristina",
booktitle = "Proceedings of the 2nd Workshop on Representation Learning for {NLP}",
month = aug,
year = "2017",
address = "Vancouver, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W17-2617",
doi = "10.18653/v1/W17-2617",
pages = "139--145",
abstract = "We propose a simple log-bilinear softmax-based model to deal with vocabulary expansion in machine translation. Our model uses word embeddings trained on significantly large unlabelled monolingual corpora and learns over a fairly small, word-to-word bilingual dictionary. Given an out-of-vocabulary source word, the model generates a probabilistic list of possible translations in the target language using the trained bilingual embeddings. We integrate these translation options into a standard phrase-based statistical machine translation system and obtain consistent improvements in translation quality on the English{--}Spanish language pair. When tested over an out-of-domain testset, we get a significant improvement of 3.9 BLEU points.",
}
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%0 Conference Proceedings
%T Learning Bilingual Projections of Embeddings for Vocabulary Expansion in Machine Translation
%A Madhyastha, Pranava Swaroop
%A España-Bonet, Cristina
%S Proceedings of the 2nd Workshop on Representation Learning for NLP
%D 2017
%8 aug
%I Association for Computational Linguistics
%C Vancouver, Canada
%F madhyastha-espana-bonet-2017-learning
%X We propose a simple log-bilinear softmax-based model to deal with vocabulary expansion in machine translation. Our model uses word embeddings trained on significantly large unlabelled monolingual corpora and learns over a fairly small, word-to-word bilingual dictionary. Given an out-of-vocabulary source word, the model generates a probabilistic list of possible translations in the target language using the trained bilingual embeddings. We integrate these translation options into a standard phrase-based statistical machine translation system and obtain consistent improvements in translation quality on the English–Spanish language pair. When tested over an out-of-domain testset, we get a significant improvement of 3.9 BLEU points.
%R 10.18653/v1/W17-2617
%U https://aclanthology.org/W17-2617
%U https://doi.org/10.18653/v1/W17-2617
%P 139-145
Markdown (Informal)
[Learning Bilingual Projections of Embeddings for Vocabulary Expansion in Machine Translation](https://aclanthology.org/W17-2617) (Madhyastha & España-Bonet, 2017)
ACL