@InProceedings{peng-chang-yih:2017:EMNLP2017,
  author    = {Peng, Haoruo  and  Chang, Ming-Wei  and  Yih, Wen-tau},
  title     = {Maximum Margin Reward Networks for Learning from Explicit and Implicit Supervision},
  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     = {2368--2378},
  abstract  = {Neural networks have achieved state-of-the-art performance on several
	structured-output prediction tasks, trained in a fully supervised
	fashion.  However, annotated examples in structured domains are often
	costly to obtain, which thus limits the applications of neural
	networks.  In this work, we propose Maximum Margin Reward Networks, a
	neural network-based framework that aims to learn from both explicit
	(full structures) and implicit supervision signals (delayed feedback
	on the correctness of the predicted structure).  On named entity
	recognition and semantic parsing, our model outperforms previous
	systems on the benchmark datasets, CoNLL-2003 and WebQuestionsSP.},
  url       = {https://www.aclweb.org/anthology/D17-1252}
}