@InProceedings{chen-EtAl:2017:RepEval,
  author    = {Chen, Qian  and  Zhu, Xiaodan  and  Ling, Zhen-Hua  and  Wei, Si  and  Jiang, Hui  and  Inkpen, Diana},
  title     = {Recurrent Neural Network-Based Sentence Encoder with Gated Attention for Natural Language Inference},
  booktitle = {Proceedings of the 2nd Workshop on Evaluating Vector Space Representations for NLP},
  month     = {September},
  year      = {2017},
  address   = {Copenhagen, Denmark},
  publisher = {Association for Computational Linguistics},
  pages     = {36--40},
  abstract  = {The RepEval 2017 Shared Task aims to evaluate natural language understanding
	models for sentence representation, in which a sentence is represented as a
	fixed-length vector with neural networks and the quality of the representation
	is tested with a natural language inference task. This paper describes our
	system (alpha) that is ranked among the top in the Shared Task, on both the
	in-domain test set (obtaining a 74.9% accuracy) and on the cross-domain test
	set (also attaining a 74.9% accuracy), demonstrating that the model generalizes
	well to the cross-domain data. Our model is equipped with intra-sentence
	gated-attention composition which helps achieve a better performance. In
	addition to submitting our model to the Shared Task, we have also tested it on
	the Stanford Natural Language Inference (SNLI) dataset. We obtain an accuracy
	of 85.5%, which is the best reported result on SNLI when cross-sentence
	attention is not allowed, the same condition enforced in RepEval 2017.},
  url       = {http://www.aclweb.org/anthology/W17-5307}
}