Journal of Shanghai Jiao Tong University

Journal of Shanghai Jiaotong University >

2022 , Vol. 56 >Issue 11: 1554 - 1560

DOI: https://doi.org/10.16183/j.cnki.jsjtu.2021.079

Electronic Information and Electrical Engineering

Grammatical Error Correction by Transferring Learning Based on Pre-Trained Language Model

Expand
  • School of Information Management and Engineering, Shanghai University of Finance and Economics, Shanghai 200433, China

Received date: 2021-03-16

  Online published: 2022-12-02

Fold

Abstract

Grammatical error correction (GEC) is a low-resource task, which requires annotations with high costs and is time consuming in training. In this paper, the MASS-GEC is proposed to solve this problem by transferring learning from a pre-trained language generation model, and masked sequence is proposed to sequence pre-training for language generation (MASS). In addition, specific preprocessing and postprocessing strategies are applied to improve the performance of the GEC system. Finally, this system is evaluated on two public datasets and a competitive performance is achieved compared with the state-of-the-art work with limited resources. Specifically, this system achieves 57.9 in terms of F0.5 score which emphasizes more on precision on the CoNLL2014 task. On the JFLEG task, the MASS-GEC achieves 59.1 in terms of GLEU score which measures the n-gram coincidence between the output of the model and the correct answer manually annotated. This paper provides a new perspective that the low-resource problem in GEC can be solved well by transferring the general language knowledge from the self-supervised pre-trained language model.

Key words： grammatical error correction (GEC); natural language generation; sequence to sequence

Cite this article

HAN Mingyue, WANG Yinglin . Grammatical Error Correction by Transferring Learning Based on Pre-Trained Language Model[J]. Journal of Shanghai Jiaotong University, 2022 , 56(11) : 1554 -1560 . DOI: 10.16183/j.cnki.jsjtu.2021.079

References

[1] NG H T, WU S M, WU Y, et al. The CoNLL-2013 shared task on grammatical error correction[C]∥Proceedings of the Seventeenth Conference on Computational Natural Language Learning: Shared Task. Sofia, Bulgaria: Association for Computational Linguistics, 2013: 1-12.
[2] NG H T, WU S M, BRISCOE T, et al. The CoNLL-2014 shared task on grammatical error correction[C]∥Proceedings of the Eighteenth Conference on Computational Natural Language Learning: Shared Task. Baltimore, Maryland, USA: Association for Computational Linguistics, 2014: 1-14.
[3] WU J C, YEN T H, CHANG J, et al. NTHU at the CoNLL-2014 shared task[C]∥Proceedings of the Eighteenth Conference on Computational Natural Language Learning: Shared Task. Baltimore, Maryland, USA: Association for Computational Linguistics, 2014: 91-95.
[4] AWASTHI A, SARAWAGI S, GOYAL R, et al. Parallel iterative edit models for local sequence transduction[C]∥Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing. Hong Kong, China: Association for Computational Linguistics, 2019: 4251-4261.
[5] OMELIANCHUK K, ATRASEVYCH V, CHERNODUB A, et al. GECToR-grammatical error correction: Tag, not rewrite[C]∥Proceedings of the Fifteenth Workshop on Innovative Use of NLP for Building Educational Applications. Seattle, WA, USA: Association for Computational Linguistics, 2020: 163-170.
[6] JUNCZYS-DOWMUNT M, GRUNDKIEWICZ R, GUHA S, et al. Approaching neural grammatical error correction as a low-resource machine translation task[C]∥Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics:Human Language Technologies. New Orleans, Louisiana, USA: Association for Computational Linguistics, 2018: 595-606.
[7] ZHAO W, WANG L, SHEN K W, et al. Improving grammatical error correction via pre-training a copy-augmented architecture with unlabeled data[C]∥Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics:Human Language Technologies. Minneapolis, Minnesota, USA: Association for Computational Linguistics, 2019: 156-165.
[8] FLACHS S, LACROIX O, S?GAARD A. Noisy channel for low resource grammatical error correction[C]∥Proceedings of the Fourteenth Workshop on Innovative Use of NLP for Building Educational Applications. Florence, Italy: Association for Computational Linguistics, 2019: 191-196.
[9] FELICE M, YUAN Z, ANDERSEN ? E, et al. Grammatical error correction using hybrid systems and type filtering[C]∥Proceedings of the Eighteenth Conference on Computational Natural Language Learning: Shared Task. Baltimore, Maryland, USA: Association for Computational Linguistics, 2014: 15-24.
[10] LIU Z R, LIU Y. Exploiting unlabeled data for neural grammatical error detection[J]. Journal of Computer Science and Technology, 2017, 32(4): 758-767.
[11] GRUNDKIEWICZ R, JUNCZYS-DOWMUNT M, HEAFIELD K. Neural grammatical error correction systems with unsupervised pre-training on synthetic data[C]∥Proceedings of the Fourteenth Workshop on Innovative Use of NLP for Building Educational Applications. Florence, Italy: Association for Computational Linguistics, 2019: 252-263.
[12] DEVLIN J, CHANG M W, LEE K, et al. BERT: pre-training of deep bidirectional transformers for language understanding[C]∥Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics:Human Language Technologies. Minneapolis, Minnesota, USA: Association for Computational Linguistics, 2019: 4171-4186.
[13] LIU Y H, OTT M, GOYAL N, et al. RoBERTa: A robustly optimized BERT pretraining approach[EB/OL]. (2019-07-26) [2021-03-16]. https:∥arxiv.org/abs/1907.11692.
[14] SONG K T, TAN X, QIN T, et al. MASS: Masked sequence to sequence pre-training for language generation[C]∥Proceedings of the 36th International Conference on Machine Learning. Los Angeles, USA: PMLR, 2019: 5926-5936.
[15] DAHLMEIER D, NG H T, WU S M. Building a large annotated corpus of learner English: The NUS corpus of learner English[C]∥Proceedings of the Eighth Workshop on Innovative Use of NLP for Building Educational Applications. Atlanta, Georgia, USA: Association for Computational Linguistics, 2013: 22-31.
[16] TAJIRI T, KOMACHI M, MATSUMOTO Y. Tense and aspect error correction for ESL learners using global context[C]∥Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics. Jeju Island, Korea: Association for Computational Linguistics, 2012: 198-202.
[17] YANNAKOUDAKIS H, BRISCOE T, MEDLOCK B. A new dataset and method for automatically grading ESOL texts[C]∥Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics:Human Language Technologies. Portland, Oregon, USA: Association for Computational Linguistics, 2011: 180-189.
[18] BRYANT C, FELICE M, ANDERSEN ? E, et al. The BEA-2019 shared task on grammatical error correction[C]∥Proceedings of the Fourteenth Workshop on Innovative Use of NLP for Building Educational Applications. Florence, Italy: Association for Computational Linguistics, 2019: 52-75.
[19] GE T, WEI F R, ZHOU M. Fluency boost learning and inference for neural grammatical error correction[C]∥Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics. Melbourne, Australia: Association for Computational Linguistics, 2018: 1055-1065.
[20] XIE Z, GENTHIAL G, XIE S, et al. Noising and denoising natural language: Diverse backtranslation for grammar correction[C]∥Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics:Human Language Technologies. New Orleans, Louisiana, USA: Association for Computational Linguistics, 2018: 619-628.
[21] CHOLLAMPATT S, WANG W Q, NG H T. Cross-sentence grammatical error correction[C]∥Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. Florence, Italy: Association for Computational Linguistics, 2019: 435-445.
[22] KANEKO M, HOTATE K, KATSUMATA S, et al. TMU transformer system using BERT for re-ranking at BEA 2019 grammatical error correction on restricted track[C]∥Proceedings of the Fourteenth Workshop on Innovative Use of NLP for Building Educational Applications. Florence, Italy: Association for Computational Linguistics, 2019: 207-212.
[23] ALIKANIOTIS D, RAHEJA V. The unreasonable effectiveness of transformer language models in grammatical error correction[C]∥Proceedings of the Fourteenth Workshop on Innovative Use of NLP for Building Educational Applications. Florence, Italy: Association for Computational Linguistics, 2019: 127-133.
[24] KANEKO M, MITA M, KIYONO S, et al. Encoder-decoder models can benefit from pre-trained masked language models in grammatical error correction[C]∥Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. Seattle, Washington, USA: Association for Computational Linguistics, 2020: 4248-4254.
[25] CHOE Y J, HAM J, PARK K, et al. A neural grammatical error correction system built on better pre-training and sequential transfer learning[C]∥Proceedings of the Fourteenth Workshop on Innovative Use of NLP for Building Educational Applications. Florence, Italy: Association for Computational Linguistics, 2019: 213-227.
[26] NAPOLES C, SAKAGUCHI K, TETREAULT J. JFLEG: A fluency corpus and benchmark for grammatical error correction[C]∥Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics. Valencia, Spain: Association for Computational Linguistics, 2017: 229-234.
[27] KIYONO S, SUZUKI J, MITA M, et al. An empirical study of incorporating pseudo data into grammatical error correction[C]∥Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing. Hong Kong, China: Association for Computational Linguistics, 2019: 1236-1242.
[28] CHOLLAMPATT S, NG H T. A multilayer convolutional encoder-decoder neural network for grammatical error correction[C]∥Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence. New Orleans, USA: AAAI Press, 2018: 5755-5762.
[29] GRUNDKIEWICZ R, JUNCZYS-DOWMUNT M. Near human-level performance in grammatical error correction with hybrid machine translation[C]∥Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics:Human Language Technologies. New Orleans, Louisiana, USA: Association for Computational Linguistics, 2018: 284-290.
[30] LICHTARGE J, ALBERTI C, KUMAR S, et al. Corpora generation for grammatical error correction[C]∥Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics:Human Language Technologies. Minneapolis, Minnesota, USA: Association for Computational Linguistics, 2019: 3291-3301.
Options
Outlines

/

〈 〉