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Machine reading comprehension model based on query reconstruction technology and deep learning

  • S.I.: Machine Learning and Big Data Analytics for IoT Security and Privacy (SPIoT 2022)
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Abstract

Machine reading comprehension is introduced to improve machines’ readability and understandability of human languages. This sophisticated version of natural language processing is used for testing and improving the machine’s efficiency for reading and responding to the input texts for appropriate queries. In this article, a persistent comprehensive model using query reconstruction is introduced to address the “Cloze Style” issue in text reading. This issue results in multiple output delivery that serves as irrelevant for different input queries. Therefore, the query reconstruction using the possible combination, reducing the aforementioned issue, is introduced in this model. The possible query keywords are replaced using the maximum individual combinations. The combinations are swapped using deep learning through keyword training and substitution processes. This process is persistent until the maximum text output (answer/ response) is obtained from the machine. The output is used for analyzing the understandability of the machine based on which the training intensity is tuned for successive iterations. Therefore, the proposed model scrutinizes output accuracy by reducing errors under controlled combination time.

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Data availability

The data that support the findings of this study are available from the corresponding author upon reasonable request.

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Funding

This work was supported by National Natural Science Foundation of China (62166018, 62266017).

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Authors and Affiliations

  1. School of Data Science and Artificial Intelligence, Wenzhou University of Technology, Wenzhou, 325035, Zhejiang, China

    Pengming Wang

  2. Department of Computer Science, College of Computer and Information Science, Jouf University, Sakaka, Al Jouf, Kingdom of Saudi Arabia

    M. M. Kamruzzaman

  3. School of Psychology, Jiangxi Normal University, Nanchang, 330022, Jiangxi, China

    Qing Chen

Authors
  1. Pengming Wang
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  2. M. M. Kamruzzaman
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  3. Qing Chen
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Correspondence to M. M. Kamruzzaman.

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Wang, P., Kamruzzaman, M.M. & Chen, Q. Machine reading comprehension model based on query reconstruction technology and deep learning. Neural Comput & Applic 36, 2155–2170 (2024). https://doi.org/10.1007/s00521-023-08698-4

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  • DOI: https://doi.org/10.1007/s00521-023-08698-4

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