@InProceedings{suzuki-EtAl:2018:Short,
  author    = {Suzuki, Jun  and  Takase, Sho  and  Kamigaito, Hidetaka  and  Morishita, Makoto  and  Nagata, Masaaki},
  title     = {An Empirical Study of Building a Strong Baseline for Constituency Parsing},
  booktitle = {Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)},
  month     = {July},
  year      = {2018},
  address   = {Melbourne, Australia},
  publisher = {Association for Computational Linguistics},
  pages     = {612--618},
  abstract  = {This paper investigates the construction of a strong baseline based on general purpose sequence-to-sequence models for constituency parsing. We incorporate several techniques that were mainly developed in natural language generation tasks, e.g., machine translation and summarization, and demonstrate that the sequence-to-sequence model achieves the current top-notch parsers' performance (almost) without requiring any explicit task-specific knowledge or architecture of constituent parsing.},
  url       = {http://www.aclweb.org/anthology/P18-2097}
}