ABCNN: Attention-Based Convolutional Neural Network for Modeling Sentence Pairs

Ludwig-Maximilians-Universität München · IBM (United States)

Indexed incrossrefdoaj

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

922
total citations
FWCI
166.97
Percentile
100%
References
57
Citations per year

Authors

4

Topics & keywords

Keywords
  • Computer science
  • Sentence
  • Textual entailment
  • Natural language processing
  • Paraphrase
  • Artificial intelligence
  • Representation (politics)
  • Selection (genetic algorithm)
No related works found for this paper.

Funding