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Learning Parameter Analysis for Machine Reading Comprehension

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Simulation Tools and Techniques (SIMUtools 2020)

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Abstract

Machine reading comprehension is a classic issue artificial intelligence. It is a key technology in the next generation search engine and intelligent interactive service. The traditional methods usually work in a small scale of data sets. The traditional system cannot meet the emerging demand. Deep learning and cloud computation have ability to deal with the large scale data sets. In real scene, the parameters affect the performance of machine reading comprehension task. In this paper, we analyze how the parameters of deep neural network affect the machine reading comprehension. The experiment results show that the performance is only sensitive to a few parameters which should be key point for engineers.

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Acknowledgements

This work is partly supported by Jiangsu major natural science research project of College and University (No. 19KJA470002).

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Authors and Affiliations

  1. Jiangsu Province Key Laboratory of Intelligent Industry Control Technology, Xuzhou University of Technology, Xuzhou, 221018, China

    Xuekui Li, Lei Chen, Yi Shi & Ping Cui

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  1. Xuekui Li
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  2. Lei Chen
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  3. Yi Shi
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  4. Ping Cui
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Correspondence to Lei Chen .

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Editors and Affiliations

  1. Embry-Riddle Aeronautical University, Daytona Beach, FL, USA

    Houbing Song

  2. School of Astronautics and Aeronautic, UESTC, Chengdu, China

    Dingde Jiang

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Li, X., Chen, L., Shi, Y., Cui, P. (2021). Learning Parameter Analysis for Machine Reading Comprehension. In: Song, H., Jiang, D. (eds) Simulation Tools and Techniques. SIMUtools 2020. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 369. Springer, Cham. https://doi.org/10.1007/978-3-030-72792-5_39

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  • DOI: https://doi.org/10.1007/978-3-030-72792-5_39

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-72791-8

  • Online ISBN: 978-3-030-72792-5

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