@InProceedings{yang-mitchell:2017:EMNLP2017,
  author    = {Yang, Bishan  and  Mitchell, Tom},
  title     = {A Joint Sequential and Relational Model for Frame-Semantic Parsing},
  booktitle = {Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing},
  month     = {September},
  year      = {2017},
  address   = {Copenhagen, Denmark},
  publisher = {Association for Computational Linguistics},
  pages     = {1247--1256},
  abstract  = {We introduce a new method for frame-semantic parsing that significantly
	improves the prior state of the art. Our model leverages the advantages of a
	deep bidirectional LSTM network which predicts semantic role labels word by
	word and a relational network which predicts semantic roles for individual text
	expressions in relation to a predicate. The two networks are integrated into a
	single model via knowledge distillation, and a unified graphical model is
	employed to jointly decode frames and semantic roles during inference.
	Experiments on the standard FrameNet data show that our model significantly
	outperforms existing neural and non-neural approaches, achieving a 5.7 F1 gain
	over the current state of the art, for full frame structure extraction.},
  url       = {https://www.aclweb.org/anthology/D17-1128}
}