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Automatic Chinese Short Answer Grading with Deep Autoencoder

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Artificial Intelligence in Education (AIED 2018)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10948))

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Abstract

Short answer question is a common assessment type of teaching and learning. Automatic short answer grading is the task of automatically scoring short natural language responses. Most previous auto-graders mainly rely on target answers given by teachers. However, target answers are not always available. In this paper, a deep autoencoder based algorithm for automatic short answer grading is presented. The proposed algorithm can be built without expressly defining target answers, and learn the lower-dimensional representation of student responses. For the sake of reducing the influence of data imbalance, we introduce the expectation regularization term of label ratio into the model. The experimental results demonstrate the effectiveness of our proposed method.

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Notes

  1. 1.

    https://github.com/fxsjy/jieba.

  2. 2.

    http://scikit-learn.org/stable/index.html.

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Acknowledgements

This work is supported by the National Natural Science Foundation of China (No. 61773361, 61473273), the Youth Innovation Promotion Association CAS 2017146, the China Postdoctoral Science Foundation (No. 2017M610054).

Author information

Authors and Affiliations

  1. Beijing Advanced Innovation Center for Future Education, Beijing Normal University, Beijing, 100875, China

    Xi Yang, Lishan Zhang & Shengquan Yu

  2. Sunny Education Inc., Beijing, 100102, China

    Yuwei Huang

  3. Beijing University of Chemical Technology, Beijing, 100029, China

    Yuwei Huang

  4. Key Lab of Intelligent Information Processing of Chinese Academy of Sciences (CAS), Institute of Computing Technology, CAS, Beijing, 100190, China

    Fuzhen Zhuang

  5. University of Chinese Academy of Sciences, Beijing, 100049, China

    Fuzhen Zhuang

Authors
  1. Xi Yang
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  2. Yuwei Huang
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  3. Fuzhen Zhuang
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  4. Lishan Zhang
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  5. Shengquan Yu
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Corresponding author

Correspondence to Fuzhen Zhuang .

Editor information

Editors and Affiliations

  1. Carnegie Mellon University, Pittsburgh, PA, USA

    Carolyn Penstein Rosé

  2. University of Technology, Sydney, NSW, Australia

    Roberto Martínez-Maldonado

  3. University of Duisburg-Essen , Duisburg, Germany

    H. Ulrich Hoppe

  4. UCL Institute of Education, London, UK

    Rose Luckin

  5. UCL Institute of Education, London, UK

    Manolis Mavrikis

  6. UCL Institute of Education, London, UK

    Kaska Porayska-Pomsta

  7. Carnegie Mellon University, Pittsburgh, PA, USA

    Bruce McLaren

  8. University of Sussex, Brighton, UK

    Benedict du Boulay

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Yang, X., Huang, Y., Zhuang, F., Zhang, L., Yu, S. (2018). Automatic Chinese Short Answer Grading with Deep Autoencoder. In: Penstein Rosé, C., et al. Artificial Intelligence in Education. AIED 2018. Lecture Notes in Computer Science(), vol 10948. Springer, Cham. https://doi.org/10.1007/978-3-319-93846-2_75

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  • DOI: https://doi.org/10.1007/978-3-319-93846-2_75

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-93845-5

  • Online ISBN: 978-3-319-93846-2

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