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Data Augmentation For Chinese Text Classification Using Back-Translation

Journal of physics. Conference series, 2020-11, Vol.1651 (1), p.12039 [Peer Reviewed Journal]

Published under licence by IOP Publishing Ltd ;2020. This work is published under http://creativecommons.org/licenses/by/3.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 1742-6588 ;EISSN: 1742-6596 ;DOI: 10.1088/1742-6596/1651/1/012039

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  • Title:
    Data Augmentation For Chinese Text Classification Using Back-Translation
  • Author: Ma, Jun ; Li, Langlang
  • Subjects: Classification ; Datasets ; Machine learning ; Natural language processing ; Text categorization
  • Is Part Of: Journal of physics. Conference series, 2020-11, Vol.1651 (1), p.12039
  • Description: Text classification is a basic task in natural language processing. When the amount of data is insufficient, the classification accuracy will be greatly affected. We propose to use the back-translation method to expand three Chinese data sets used for text classification, and then train and predict the data sets through deep learning classification model. The results prove that using back-translation to expand the data is particularly helpful on a smaller dataset, it also can reduce the unbalanced distribution of samples and improve the classification performance.
  • Publisher: Bristol: IOP Publishing
  • Language: English
  • Identifier: ISSN: 1742-6588
    EISSN: 1742-6596
    DOI: 10.1088/1742-6596/1651/1/012039
  • Source: IOP Publishing Free Content
    IOPscience (Open Access)
    GFMER Free Medical Journals
    ProQuest Central
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