ABCNN: Attention-Based Convolutional Neural Network for Modeling Sentence Pairs
Ludwig-Maximilians-Universität München · IBM (United States)
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) The ABCNN can be applied to a wide variety of tasks that require modeling of sentence pairs. (ii) We propose three attention schemes…
Citation impact
- FWCI
- 166.97
- Percentile
- 100%
- References
- 57
Authors
4Topics & keywords
- Computer science
- Sentence
- Textual entailment
- Natural language processing
- Paraphrase
- Artificial intelligence
- Representation (politics)
- Selection (genetic algorithm)