@InProceedings{dhingra-EtAl:2017:Long2,
  author    = {Dhingra, Bhuwan  and  Liu, Hanxiao  and  Yang, Zhilin  and  Cohen, William  and  Salakhutdinov, Ruslan},
  title     = {Gated-Attention Readers for Text Comprehension},
  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     = {1832--1846},
  abstract  = {In this paper we study the problem of answering cloze-style questions over
	documents. Our model, the Gated-Attention (GA) Reader, integrates a multi-hop
	architecture with a novel attention mechanism, which is based on multiplicative
	interactions between the query embedding and the intermediate states of a
	recurrent neural network document reader. This enables the reader to build
	query-specific representations of tokens in the document for accurate answer
	selection. The GA Reader obtains state-of-the-art results on three benchmarks
	for this task--the CNN \& Daily Mail news stories and the Who Did What dataset.
	The effectiveness of multiplicative interaction is demonstrated by an ablation
	study, and by comparing to alternative compositional operators for implementing
	the gated-attention.},
  url       = {http://aclweb.org/anthology/P17-1168}
}