@inproceedings{yu-etal-2020-fast,
    title = "Fast and Accurate Non-Projective Dependency Tree Linearization",
    author = "Yu, Xiang  and
      Tannert, Simon  and
      Vu, Ngoc Thang  and
      Kuhn, Jonas",
    editor = "Jurafsky, Dan  and
      Chai, Joyce  and
      Schluter, Natalie  and
      Tetreault, Joel",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2020.acl-main.134/",
    doi = "10.18653/v1/2020.acl-main.134",
    pages = "1451--1462",
    abstract = "We propose a graph-based method to tackle the dependency tree linearization task. We formulate the task as a Traveling Salesman Problem (TSP), and use a biaffine attention model to calculate the edge costs. We facilitate the decoding by solving the TSP for each subtree and combining the solution into a projective tree. We then design a transition system as post-processing, inspired by non-projective transition-based parsing, to obtain non-projective sentences. Our proposed method outperforms the state-of-the-art linearizer while being 10 times faster in training and decoding."
}