@inproceedings{mcdonald-chiang-2021-syntax,
    title = "Syntax-Based Attention Masking for Neural Machine Translation",
    author = "McDonald, Colin  and
      Chiang, David",
    editor = "Durmus, Esin  and
      Gupta, Vivek  and
      Liu, Nelson  and
      Peng, Nanyun  and
      Su, Yu",
    booktitle = "Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Student Research Workshop",
    month = jun,
    year = "2021",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/landing_page/2021.naacl-srw.7/",
    doi = "10.18653/v1/2021.naacl-srw.7",
    pages = "47--52",
    abstract = "We present a simple method for extending transformers to source-side trees. We define a number of masks that limit self-attention based on relationships among tree nodes, and we allow each attention head to learn which mask or masks to use. On translation from English to various low-resource languages, and translation in both directions between English and German, our method always improves over simple linearization of the source-side parse tree and almost always improves over a sequence-to-sequence baseline, by up to +2.1 BLEU."
}