@inproceedings{shen-etal-2018-straight,
    title = "Straight to the Tree: Constituency Parsing with Neural Syntactic Distance",
    author = "Shen, Yikang  and
      Lin, Zhouhan  and
      Jacob, Athul Paul  and
      Sordoni, Alessandro  and
      Courville, Aaron  and
      Bengio, Yoshua",
    editor = "Gurevych, Iryna  and
      Miyao, Yusuke",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/P18-1108",
    doi = "10.18653/v1/P18-1108",
    pages = "1171--1180",
    abstract = "In this work, we propose a novel constituency parsing scheme. The model first predicts a real-valued scalar, named syntactic distance, for each split position in the sentence. The topology of grammar tree is then determined by the values of syntactic distances. Compared to traditional shift-reduce parsing schemes, our approach is free from the potentially disastrous compounding error. It is also easier to parallelize and much faster. Our model achieves the state-of-the-art single model F1 score of 92.1 on PTB and 86.4 on CTB dataset, which surpasses the previous single model results by a large margin.",
}