@inproceedings{zhang-etal-2022-higher,
    title = "Higher-Order Dependency Parsing for Arc-Polynomial Score Functions via Gradient-Based Methods and Genetic Algorithm",
    author = "Zhang, Xudong  and
      Le Roux, Joseph  and
      Charnois, Thierry",
    editor = "He, Yulan  and
      Ji, Heng  and
      Li, Sujian  and
      Liu, Yang  and
      Chang, Chua-Hui",
    booktitle = "Proceedings of the 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
    month = nov,
    year = "2022",
    address = "Online only",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2022.aacl-main.85/",
    doi = "10.18653/v1/2022.aacl-main.85",
    pages = "1158--1171",
    abstract = "We present a novel method for higher-order dependency parsing which takes advantage of the general form of score functions written as arc-polynomials, a general framework which encompasses common higher-order score functions, and includes new ones. This method is based on non-linear optimization techniques, namely coordinate ascent and genetic search where we iteratively update a candidate parse. Updates are formulated as gradient-based operations, and are efficiently computed by auto-differentiation libraries. Experiments show that this method obtains results matching the recent state-of-the-art second order parsers on three standard datasets."
}