@inproceedings{pourdamghani-etal-2018-using,
    title = "Using Word Vectors to Improve Word Alignments for Low Resource Machine Translation",
    author = "Pourdamghani, Nima  and
      Ghazvininejad, Marjan  and
      Knight, Kevin",
    editor = "Walker, Marilyn  and
      Ji, Heng  and
      Stent, Amanda",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/add-emnlp-2024-awards/N18-2083/",
    doi = "10.18653/v1/N18-2083",
    pages = "524--528",
    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."
}