@InProceedings{shen-yang-deng:2017:EMNLP2017,
  author    = {Shen, Gehui  and  Yang, Yunlun  and  Deng, Zhi-Hong},
  title     = {Inter-Weighted Alignment Network for Sentence Pair Modeling},
  booktitle = {Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing},
  month     = {September},
  year      = {2017},
  address   = {Copenhagen, Denmark},
  publisher = {Association for Computational Linguistics},
  pages     = {1179--1189},
  abstract  = {Sentence pair modeling is a crucial problem in the field of natural language
	processing. 
	In this paper, we propose a model to measure the similarity of a sentence pair
	focusing on the interaction information. We utilize the word level similarity
	matrix to discover fine-grained alignment of two sentences. It should be
	emphasized that each word in a sentence has a different importance from the
	perspective of semantic composition, so we exploit two novel and efficient
	strategies to explicitly calculate a weight for each word. Although the
	proposed model only use a sequential LSTM for sentence modeling without any
	external resource such as syntactic parser tree and additional lexicon
	features, experimental results show that our model achieves state-of-the-art
	performance on three datasets of two tasks.},
  url       = {https://www.aclweb.org/anthology/D17-1122}
}