@inproceedings{omote-etal-2019-dependency,
    title = "Dependency-Based Relative Positional Encoding for Transformer {NMT}",
    author = "Omote, Yutaro  and
      Tamura, Akihiro  and
      Ninomiya, Takashi",
    editor = "Mitkov, Ruslan  and
      Angelova, Galia",
    booktitle = "Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)",
    month = sep,
    year = "2019",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/R19-1099/",
    doi = "10.26615/978-954-452-056-4_099",
    pages = "854--861",
    abstract = "This paper proposes a new Transformer neural machine translation model that incorporates syntactic distances between two source words into the relative position representations of the self-attention mechanism. In particular, the proposed model encodes pair-wise relative depths on a source dependency tree, which are differences between the depths of the two source words, in the encoder`s self-attention. The experiments show that our proposed model achieves 0.5 point gain in BLEU on the Asian Scientific Paper Excerpt Corpus Japanese-to-English translation task."
}