@article{TACL831,
	author = {Yin, Wenpeng  and Schütze, Hinrich  and Xiang, Bing  and Zhou, Bowen },
	title = {ABCNN: Attention-Based Convolutional Neural Network for Modeling Sentence Pairs},
	journal = {Transactions of the Association for Computational Linguistics},
	volume = {4},
	year = {2016},
	keywords = {},
	abstract = {How to model a pair of sentences is a critical issue in manyNLP tasks such as answer selection (AS), paraphrase identification (PI) and textual entailment (TE). Most prior work (i) deals with one individual task by fine-tuning a specific system; (ii) models each sentence's representation separately, rarely considering the impact of the other sentence; or (iii) relies fully on manually designed, task-specific linguistic features. This work presents a general Attention Based Convolutional Neural Network (ABCNN) for modeling a pair of sentences. We make three contributions. (i) The ABCNN can be applied to a wide variety of tasks that require modeling of sentence pairs. (ii) We propose three attention schemes that integrate mutual influence between sentences into CNNs; thus, the representation of eachsentence takes into consideration its counterpart. Theseinterdependent sentence pair representations are morepowerful than isolated sentence representations. (iii)ABCNNs achieve state-of-the-art performance on AS, PI and TE tasks. We release code at: https://github.com/yinwenpeng/Answer_Selection.},
	issn = {2307-387X},
	url = {https://tacl2013.cs.columbia.edu/ojs/index.php/tacl/article/view/831},
	pages = {259--272}
}