@InProceedings{raiman-miller:2017:EMNLP2017,
  author    = {Raiman, Jonathan  and  Miller, John},
  title     = {Globally Normalized Reader},
  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     = {1059--1069},
  abstract  = {Rapid progress has been made towards question answering (QA) systems that can
	extract answers from text. Existing neural approaches make use of expensive
	bi-directional attention mechanisms or score all possible answer spans,
	limiting scalability. We propose instead to cast extractive QA as an iterative
	search problem: select the answer's sentence, start word, and end word. This
	representation reduces the space of each search step and allows computation to
	be conditionally allocated to promising search paths. We show that globally
	normalizing the decision process and back-propagating through beam search makes
	this representation viable and learning efficient. We empirically demonstrate
	the benefits of this approach using our model, Globally Normalized Reader
	(GNR), which achieves the second highest single model performance on the
	Stanford Question Answering Dataset (68.4 EM, 76.21 F1 dev) and is 24.7x faster
	than bi-attention-flow. We also introduce a data-augmentation method to produce
	semantically valid examples by aligning named entities to a knowledge base and
	swapping them with new entities of the same type. This method  improves the
	performance of all models considered in this work and is of independent
	interest for a variety of NLP tasks.},
  url       = {https://www.aclweb.org/anthology/D17-1111}
}