Predicting and Integrating Expected Answer Types into a Simple Recurrent Neural Network Model for Answer Sentence Selection

Authors

  • Sanjay Kamath LIMSI, CNRS, Université Paris-Saclay, Orsay
  • Brigitte Grau ENSIIE, Université Paris-Saclay, Évry
  • Yue Ma LRI, Univ. Paris-Sud, CNRS, Université Paris-Saclay, Orsay

DOI:

https://doi.org/10.13053/cys-23-3-3241

Keywords:

Question answering, deep learning, answer sentence selection, expected answer types, sentence similarity

Abstract

Since end-to-end deep learning models have started to replace traditional pipeline architectures of question answering systems, features such as expected answer types which are based on the question semantics are seldom used explicitly in the models. In this paper, we propose a convolution neural network model to predict these answer types based on question words and a recurrent neural network model to find sentence similarity scores between question and answer sentences. The proposed model outperforms the current state of the art results on an answer sentence selection task in open domain question answering by 1.88% on MAP and 2.96% on MRR scores.

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Published

2019-09-25

Issue

Vol. 23 No. 3 (2019): Thematic issue: Computational Linguistics (Guest Editor A. Gelbukh)

Section

Articles of the Thematic Issue

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