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Constrained Deep Answer Sentence Selection

  • Ahmad AghaebrahimianEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10415)

Abstract

In this paper, we propose Constrained Deep Neural Network (CDNN) a simple deep neural model for answer sentence selection. CDNN makes its predictions based on neural reasoning compound with some symbolic constraints. It integrates pattern matching technique into sentence vector learning. When trained using enough samples, CDNN outperforms regular models. We show how using other sources of training data as a mean of transfer learning can enhance the performance of the network. In a well-studied dataset for answer sentence selection, our network improves the state of the art in answer sentence selection significantly.

Keywords

Deep neural network Sentence selection Transfer learning 

Notes

Acknowledgments

This research was partially funded by the Ministry of Education, Youth and Sports of the Czech Republic under SVV project number 260 453, core research funding, and GAUK 207-10/250098 of Charles University in Prague.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Faculty of Mathematics and Physics, Institute of Formal and Applied LinguisticsCharles University in PraguePraha 1Czech Republic

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