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The Science and Information (SAI) Organization publishes open-access peer-reviewed journals in computer science and artificial intelligence.

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Research Article | Open Access |

Can Ready-to-Use RNNs Generate “Good” Text Training Data?

Author 1: Jia Hui Feng
International Journal of Advanced Computer Science and Applications (IJACSA) · Vol. 13, No. 3 · Published 2022

DOI: https://doi.org/10.14569/IJACSA.2022.0130306

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Abstract

There is much research on the state-of-the-art techniques for generating training data through neural networks. However, many of these techniques are not easily implemented or available due to factors such as copyright of their research code. Meanwhile, there are other neural network codes currently available that are easily accessible for individuals to generate text data; this paper explores the quality of the text data generated by these ready-to-use neural networks for classification tasks. This paper’s experiment showed that using the text data generated by a default configured RNN to train a classification model can match closely with baseline accuracy.

Keywords

Neural networks machine learning text genera-tion classification natural language processing data augmenta-tion artificial intelligence

How to Cite this Article

Feng, J. H. (2022). Can Ready-to-Use RNNs Generate “Good” Text Training Data?. International Journal of Advanced Computer Science and Applications, 13(3). https://doi.org/10.14569/IJACSA.2022.0130306

Feng, Jia Hui. "Can Ready-to-Use RNNs Generate “Good” Text Training Data?." International Journal of Advanced Computer Science and Applications, vol. 13, no. 3, 2022, https://doi.org/10.14569/IJACSA.2022.0130306.

@article{Feng2022,
  title     = {Can Ready-to-Use RNNs Generate “Good” Text Training Data?},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {13},
  number    = {3},
  year      = {2022},
  publisher = {The Science and Information Organization},
  author    = {Jia Hui Feng},
  doi       = {10.14569/IJACSA.2022.0130306},
  url       = {https://doi.org/10.14569/IJACSA.2022.0130306}
}

Open Access — licensed under a Creative Commons Attribution 4.0 International License. Unrestricted use, distribution, and reproduction in any medium, even commercially, as long as the original work is properly cited.

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