@InProceedings{munkhdalai-yu:2017:EACLlong1,
  author    = {Munkhdalai, Tsendsuren  and  Yu, Hong},
  title     = {Neural Tree Indexers for Text Understanding},
  booktitle = {Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers},
  month     = {April},
  year      = {2017},
  address   = {Valencia, Spain},
  publisher = {Association for Computational Linguistics},
  pages     = {11--21},
  abstract  = {Recurrent neural networks (RNNs) process
	input text sequentially and model the
	conditional transition between word tokens.
	In contrast, the advantages of recursive
	networks include that they explicitly
	model the compositionality and the recursive
	structure of natural language. However,
	the current recursive architecture is
	limited by its dependence on syntactic
	tree. In this paper, we introduce a robust
	syntactic parsing-independent tree structured
	model, Neural Tree Indexers (NTI)
	that provides a middle ground between the
	sequential RNNs and the syntactic treebased
	recursive models. NTI constructs a
	full n-ary tree by processing the input text
	with its node function in a bottom-up fashion.
	Attention mechanism can then be applied
	to both structure and node function.
	We implemented and evaluated a binary tree
	model of NTI, showing the model
	achieved the state-of-the-art performance
	on three different NLP tasks: natural language
	inference, answer sentence selection,
	and sentence classification, outperforming
	state-of-the-art recurrent and recursive
	neural networks.},
  url       = {http://www.aclweb.org/anthology/E17-1002}
}