@InProceedings{dozat-manning:2018:Short,
  author    = {Dozat, Timothy  and  Manning, Christopher D.},
  title     = {Simpler but More Accurate Semantic Dependency 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     = {484--490},
  abstract  = {While syntactic dependency annotations concentrate on the surface or functional structure of a sentence, semantic depen- dency annotations aim to capture between- word relationships that are more closely related to the meaning of a sentence, using graph-structured representations. We ex- tend the LSTM-based syntactic parser of Dozat and Manning (2017) to train on and generate these graph structures. The re- sulting system on its own achieves state- of-the-art performance, beating the pre- vious, substantially more complex state- of-the-art system by 0.6% labeled F1. Adding linguistically richer input repre- sentations pushes the margin even higher, allowing us to beat it by 1.9% labeled F1.},
  url       = {http://www.aclweb.org/anthology/P18-2077}
}