ABCNN: Attention-Based Convolutional Neural Network for Modeling Sentence Pairs
Wenpeng Yin, Hinrich Schütze, Bing Xiang, Bo-Wen Zhou
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- github.com/yinwenpeng/Answer_SelectionOfficialIn papernone★ 0
- github.com/galsang/ABCNNtf★ 0
- github.com/codykala/ABCNNpytorch★ 0
- github.com/shamalwinchurkar/question-classificationtf★ 0
- github.com/sunsiqi26/Entailment-with-TensorFlowtf★ 0
- github.com/kinimod23/ATS_Projecttf★ 0
- github.com/Leputa/CIKM-AnalytiCup-2018tf★ 0
- github.com/jastfkjg/semantic-matchingtf★ 0
Abstract
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.