@inproceedings{fernandez-gonzalez-gomez-rodriguez-2019-left,
    title = "Left-to-Right Dependency Parsing with Pointer Networks",
    author = "Fern{\'a}ndez-Gonz{\'a}lez, Daniel  and
      G{\'o}mez-Rodr{\'i}guez, Carlos",
    editor = "Burstein, Jill  and
      Doran, Christy  and
      Solorio, Thamar",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/N19-1076/",
    doi = "10.18653/v1/N19-1076",
    pages = "710--716",
    abstract = "We propose a novel transition-based algorithm that straightforwardly parses sentences from left to right by building n attachments, with n being the length of the input sentence. Similarly to the recent stack-pointer parser by Ma et al. (2018), we use the pointer network framework that, given a word, can directly point to a position from the sentence. However, our left-to-right approach is simpler than the original top-down stack-pointer parser (not requiring a stack) and reduces transition sequence length in half, from 2n-1 actions to n. This results in a quadratic non-projective parser that runs twice as fast as the original while achieving the best accuracy to date on the English PTB dataset (96.04{\%} UAS, 94.43{\%} LAS) among fully-supervised single-model dependency parsers, and improves over the former top-down transition system in the majority of languages tested."
}