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Attention-Based Bidirectional Long Short-Term Memory Neural Network for Short Answer Scoring

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Machine Learning and Intelligent Communications (MLICOM 2020)

Abstract

The automatic short answer scoring by using computational approaches has been considered the best way to release the workload of human answer raters. In this paper, we designed a novel neural network architecture which is attention-based bidirectional long short-term memory to implement the task of automatic short answer scoring. We evaluate our approach on the Kaggle Short Answer dataset (ASAP-SAS). Our experiment results indicate that our model can scoring short answers more accurately in terms of the quality of the results. Meanwhile, our experiment results demonstrate that our model is more effective and efficient than other baseline methods in most cases.

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Acknowledgement

This work is supported by Engineering Applications of Artificial Intelligence Technology Laboratory of Shenzhen Institute of Information Technology (Number: PT201701), the Guangdong Province higher vocational colleges & schools Pearl River scholar funded scheme (2016), and The Scientific and Technological Projects of Shenzhen (No. JCYJ20190808093001772).

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Authors and Affiliations

  1. Shenzhen Institute of Information Technology, Shenzhen, 518172, China

    Linzhong Xia, Mingxiang Guan, Jun Liu, Xuemei Cao & Dean Luo

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  1. Linzhong Xia
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  2. Mingxiang Guan
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  3. Jun Liu
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  4. Xuemei Cao
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  5. Dean Luo
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Correspondence to Linzhong Xia .

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Editors and Affiliations

  1. Shenzhen Institute of Information Technology, Shenzhen, China

    Mingxiang Guan

  2. Sci & Tech, DianHang Bldg, Rm 321, Dalian Maritime Univ, Sch of Info, Dalian, Liaoning, China

    Zhenyu Na

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Xia, L., Guan, M., Liu, J., Cao, X., Luo, D. (2021). Attention-Based Bidirectional Long Short-Term Memory Neural Network for Short Answer Scoring. In: Guan, M., Na, Z. (eds) Machine Learning and Intelligent Communications. MLICOM 2020. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 342. Springer, Cham. https://doi.org/10.1007/978-3-030-66785-6_12

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  • DOI: https://doi.org/10.1007/978-3-030-66785-6_12

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-66784-9

  • Online ISBN: 978-3-030-66785-6

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