@InProceedings{he-EtAl:2017:Long3,
  author    = {He, Luheng  and  Lee, Kenton  and  Lewis, Mike  and  Zettlemoyer, Luke},
  title     = {Deep Semantic Role Labeling: What Works and What’s Next},
  booktitle = {Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  month     = {July},
  year      = {2017},
  address   = {Vancouver, Canada},
  publisher = {Association for Computational Linguistics},
  pages     = {473--483},
  abstract  = {We introduce a new deep learning model for semantic role labeling (SRL) that
	significantly improves the state of the art, along with detailed analyses to
	reveal its strengths and limitations. We use a deep highway BiLSTM architecture
	with constrained decoding, while observing a number of recent best practices
	for initialization and regularization. Our 8-layer ensemble model achieves 83.2
	F1 on theCoNLL 2005 test set and 83.4 F1 on CoNLL 2012, roughly a 10% relative
	error reduction over the previous state of the art. Extensive empirical
	analysis of these gains show that (1) deep models excel at recovering
	long-distance dependencies but can still make surprisingly obvious errors, and
	(2) that there is still room for syntactic parsers to improve these results.},
  url       = {http://aclweb.org/anthology/P17-1044}
}