@InProceedings{salton-ross-kelleher:2017:I17-1,
  author    = {Salton, Giancarlo  and  Ross, Robert  and  Kelleher, John},
  title     = {Attentive Language Models},
  booktitle = {Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)},
  month     = {November},
  year      = {2017},
  address   = {Taipei, Taiwan},
  publisher = {Asian Federation of Natural Language Processing},
  pages     = {441--450},
  abstract  = {In this paper, we extend Recurrent Neural Network Language Models (RNN-LMs)
	with an attention mechanism. We show that an “attentive” RNN-LM (with 11M
	parameters) achieves a better perplexity than larger RNN-LMs (with 66M
	parameters) and achieves performance comparable to an ensemble of 10 similar
	sized RNN-LMs. We also show that an "attentive" RNN-LM needs less contextual
	information to achieve similar results to the state-of-the-art on the wikitext2
	dataset.
	Author{2}{Affiliation}},
  url       = {http://www.aclweb.org/anthology/I17-1045}
}