@InProceedings{rooshenas-kamath-mccallum:2018:N18-2,
  author    = {Rooshenas, Amirmohammad  and  Kamath, Aishwarya  and  McCallum, Andrew},
  title     = {Training Structured Prediction Energy Networks with Indirect Supervision},
  booktitle = {Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)},
  month     = {June},
  year      = {2018},
  address   = {New Orleans, Louisiana},
  publisher = {Association for Computational Linguistics},
  pages     = {130--135},
  abstract  = {This paper introduces rank-based training of structured prediction energy networks (SPENs). Our method samples from output structures using gradient descent and minimizes the ranking violation of the sampled structures with respect to a scalar scoring function defined with domain knowledge. We have successfully trained SPEN for citation field extraction without any labeled data instances, where the only source of supervision is a simple human-written scoring function. Such scoring functions are often easy to provide; the SPEN then furnishes an efficient structured prediction inference procedure.},
  url       = {http://www.aclweb.org/anthology/N18-2021}
}