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DOI: 10.14569/IJACSA.2021.0120893
PDF

Grammatical Error Correction with Denoising Autoencoder

Author 1: Krzysztof Pajak
Author 2: Adam Gonczarek

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 12 Issue 8, 2021.

  • Abstract and Keywords
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Abstract: A denoising autoencoder sequence-to-sequence model based on transformer architecture proved to be useful for underlying tasks such as summarization, machine translation, or question answering. This paper investigates the possibilities of using this model type for grammatical error correction and introduces a novel method of remark-based model checkpoint output combining. This approach was evaluated by the BEA 2019 shared task. It was able to achieve state-of-the-art F-score results on the test set 73.90 and development set 56.58. This was done without any GEC-specific pre-training, but only by fine-tuning the autoencoder model and combining checkpoint outputs. This proves that an efficient model solving GEC might be trained in a matter of hours using a single GPU.

Keywords: Denoising autoencoder transformer; sequence-to-sequence; grammatical error correction; model ensembling; error remarks filtering; fine-tuning

Krzysztof Pajak and Adam Gonczarek, “Grammatical Error Correction with Denoising Autoencoder” International Journal of Advanced Computer Science and Applications(IJACSA), 12(8), 2021. http://dx.doi.org/10.14569/IJACSA.2021.0120893

@article{Pajak2021,
title = {Grammatical Error Correction with Denoising Autoencoder},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2021.0120893},
url = {http://dx.doi.org/10.14569/IJACSA.2021.0120893},
year = {2021},
publisher = {The Science and Information Organization},
volume = {12},
number = {8},
author = {Krzysztof Pajak and Adam Gonczarek}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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