@InProceedings{he-EtAl:2018:Short1,
  author    = {He, Luheng  and  Lee, Kenton  and  Levy, Omer  and  Zettlemoyer, Luke},
  title     = {Jointly Predicting Predicates and Arguments in Neural Semantic Role Labeling},
  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     = {364--369},
  abstract  = {Recent BIO-tagging-based neural semantic role labeling models are very high performing, but assume gold predicates as part of the input and cannot incorporate span-level features. We propose an end-to-end approach for jointly predicting all predicates, arguments spans, and the relations between them. The model makes independent decisions about what relationship, if any, holds between every possible word-span pair, and learns contextualized span representations that provide rich, shared input features for each decision. Experiments demonstrate that this approach sets a new state of the art on PropBank SRL without gold predicates.},
  url       = {http://www.aclweb.org/anthology/P18-2058}
}