@InProceedings{zhou-liu-pan:2016:COLING,
  author    = {Zhou, Yao  and  Liu, Cong  and  Pan, Yan},
  title     = {Modelling Sentence Pairs with Tree-structured Attentive Encoder},
  booktitle = {Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers},
  month     = {December},
  year      = {2016},
  address   = {Osaka, Japan},
  publisher = {The COLING 2016 Organizing Committee},
  pages     = {2912--2922},
  abstract  = {We describe an attentive encoder that combines tree-structured recursive neural
	networks and sequential recurrent neural networks for modelling sentence pairs.
	Since existing attentive models exert attention on the sequential structure, we
	propose a way to incorporate attention into the tree topology. Specially, given
	a pair of sentences, our attentive encoder uses the representation of one
	sentence, which generated via an RNN, to guide the structural encoding of the
	other sentence on the dependency parse tree. We evaluate the proposed attentive
	encoder on three tasks: semantic similarity, paraphrase identification and
	true-false question selection. Experimental results show that our encoder
	outperforms all baselines and achieves state-of-the-art results on two tasks.},
  url       = {http://aclweb.org/anthology/C16-1274}
}