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Grammatical Error Correction for Vietnamese Using Machine Translation

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Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1215))

Abstract

Correction of Vietnamese grammatical errors plays an important role in Natural Language Processing. In this paper, we propose a new method using Machine Translation. We consider the grammatical error correction problem like machine translation problem with source language as grammatical wrong text and target language as grammatical right texts, respectively. Additionally, we carry out pre-processing step with grammatical wrong text using spelling checker such as MS Word spelling tool before using Machine translation model.

Our experiments based on the state-of-the-art Machine Translation systems combining with pre-processing step. Experimental results achieved 84.32 BLEU score with Vietnamese grammatical error correct based on SMT architecture and 88.71 BLEU score system based on NMT architecture, which indicates that our method achieves promising results.

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Notes

  1. 1.

    http://statmt.org/moses/.

  2. 2.

    https://github.com/OpenNMT/OpenNMT-py.

References

  1. Fu, K., Huang, J., Duan, Y.: Youdao’s winning solution to the NLPCC-2018 task 2 challenge: a neural machine translation approach to Chinese grammatical error correction. In: Inproceedings (2018)

    Google Scholar 

  2. Grundkiewicz, R., Junczys-Dowmunt, M.: Near human-level performance in grammatical error correction with hybrid machine translation. In: Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers) (2018)

    Google Scholar 

  3. Kim, Y. Deng, Y., Senellart, J., Klein, G., Rush, A.M.: OpenNMT: open-source toolkit for neural machine translation. arXiv preprint arXiv:1701.02810 (2017)

  4. Koehn, P.: Statistical Machine Translation. Cambridge University Press, Cambridge (2010)

    MATH  Google Scholar 

  5. Pham, H., Luong, M.-T., Manning, C.D.: Effective approaches to attention-based neural machine translation. arXiv preprint arXiv:1508.04025 (2015)

  6. Pham, H., Luong, M.-T., Manning, C.D.: Effective approaches to attention-based neural machine translation. In: Proceedings of EMNLP (2015)

    Google Scholar 

  7. Napoles, C., Callison-Burch, C.: Systematically adapting machine translation for grammatical error correction. In: Proceedings of the 12th Workshop on Innovative Use of NLP for Building Educational Applications, Copenhagen, Denmark, 8 September 2017, pp. 345–356. Association for Computational Linguistics (2017)

    Google Scholar 

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Acknowledgments

This work is funded by the project: Building a machine translation system to support translation of documents between Vietnamese and Japanese to help managers and businesses in Hanoi approach Japanese market, under grant number TC.02-2016-03 and the project of VNU University of Engineering and Technology, Hanoi, Vietnam.

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Correspondence to Nghia Luan Pham .

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Pham, N.L., Nguyen, T.H., Nguyen, V.V. (2020). Grammatical Error Correction for Vietnamese Using Machine Translation. In: Nguyen, LM., Phan, XH., Hasida, K., Tojo, S. (eds) Computational Linguistics. PACLING 2019. Communications in Computer and Information Science, vol 1215. Springer, Singapore. https://doi.org/10.1007/978-981-15-6168-9_41

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  • DOI: https://doi.org/10.1007/978-981-15-6168-9_41

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-6167-2

  • Online ISBN: 978-981-15-6168-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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