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Neural Question Generation with Semantics of Question Type

  • Xiaozheng Dong
  • Yu HongEmail author
  • Xin Chen
  • Weikang Li
  • Min Zhang
  • Qiaoming Zhu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11109)

Abstract

This paper focuses on automatic question generation (QG) that transforms a narrative sentence into an interrogative sentence. Recently, neural networks have been used in this task due to its extraordinary ability of semantics encoding and decoding. We propose an approach which incorporates semantics of the possible question type. We utilize the Convolutional Neural Network (CNN) for predicting question type of the answer phrases in the narrative sentence. In order to incorporate the question type semantics into the generating process, we classify the question type which the answer phrases refer to. In addition, We use Bidirectional Long Short Term Memory (Bi-LSTM) to construct the question generating model. The experiment results show that our method outperforms the baseline system with the improvement of 1.7% on BLEU-4 score and beyonds the state-of-the-art.

Keywords

Question generation Question type Answer phrases 

Notes

Acknowledgements

This work was supported by the national Natural Science Foundation of China via Nos. 2017YFB1002104, 61672368 and 61672367.

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Xiaozheng Dong
    • 1
  • Yu Hong
    • 1
    Email author
  • Xin Chen
    • 1
  • Weikang Li
    • 1
  • Min Zhang
    • 1
  • Qiaoming Zhu
    • 1
  1. 1.School of Computer Science and Technology of Jiangsu ProvinceSoochow UniversitySuzhouChina

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