@InProceedings{shen-EtAl:2018:Long2,
  author    = {Shen, Yikang  and  Lin, Zhouhan  and  Jacob, Athul Paul  and  Sordoni, Alessandro  and  Courville, Aaron  and  Bengio, Yoshua},
  title     = {Straight to the Tree: Constituency Parsing with Neural Syntactic Distance},
  booktitle = {Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  month     = {July},
  year      = {2018},
  address   = {Melbourne, Australia},
  publisher = {Association for Computational Linguistics},
  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.},
  url       = {http://www.aclweb.org/anthology/P18-1108}
}