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        <title>Using Word Vectors to Improve Word Alignments for Low Resource Machine Translation</title>
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    <abstract>We present a method for improving word alignments using word similarities. This method is based on encouraging common alignment links between semantically similar words. We use word vectors trained on monolingual data to estimate similarity. Our experiments on translating fifteen languages into English show consistent BLEU score improvements across the languages.</abstract>
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%0 Conference Proceedings
%T Using Word Vectors to Improve Word Alignments for Low Resource Machine Translation
%A Pourdamghani, Nima
%A Ghazvininejad, Marjan
%A Knight, Kevin
%Y Walker, Marilyn
%Y Ji, Heng
%Y Stent, Amanda
%S Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)
%D 2018
%8 June
%I Association for Computational Linguistics
%C New Orleans, Louisiana
%F pourdamghani-etal-2018-using
%X We present a method for improving word alignments using word similarities. This method is based on encouraging common alignment links between semantically similar words. We use word vectors trained on monolingual data to estimate similarity. Our experiments on translating fifteen languages into English show consistent BLEU score improvements across the languages.
%R 10.18653/v1/N18-2083
%U https://aclanthology.org/N18-2083/
%U https://doi.org/10.18653/v1/N18-2083
%P 524-528

Markdown (Informal)

[Using Word Vectors to Improve Word Alignments for Low Resource Machine Translation](https://aclanthology.org/N18-2083/) (Pourdamghani et al., NAACL 2018)

• Using Word Vectors to Improve Word Alignments for Low Resource Machine Translation (Pourdamghani et al., NAACL 2018)

ACL

• Nima Pourdamghani, Marjan Ghazvininejad, and Kevin Knight. 2018. Using Word Vectors to Improve Word Alignments for Low Resource Machine Translation. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers), pages 524–528, New Orleans, Louisiana. Association for Computational Linguistics.