  How to model a pair of sentences is a critical issue in many NLP 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) 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 CNN;
thus, the representation of each sentence takes into consideration its
counterpart. These interdependent sentence pair representations are more
powerful than isolated sentence representations. (iii) ABCNN achieves
state-of-the-art performance on AS, PI and TE tasks.
