An Empirical Study of Incorporating Pseudo Data into Grammatical Error Correction

Shun Kiyono, Jun Suzuki, Masato Mita, Tomoya Mizumoto, Kentaro Inui

Abstract
The incorporation of pseudo data in the training of grammatical error correction models has been one of the main factors in improving the performance of such models. However, consensus is lacking on experimental configurations, namely, choosing how the pseudo data should be generated or used. In this study, these choices are investigated through extensive experiments, and state-of-the-art performance is achieved on the CoNLL-2014 test set (F0.5=65.0) and the official test set of the BEA-2019 shared task (F0.5=70.2) without making any modifications to the model architecture.
Anthology ID:
D19-1119
Volume:
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
Month:
November
Year:
2019
Address:
Hong Kong, China
Editors:
Kentaro Inui, Jing Jiang, Vincent Ng, Xiaojun Wan
Venues:
EMNLP | IJCNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
1236–1242
Language:
URL:
https://aclanthology.org/D19-1119
DOI:
10.18653/v1/D19-1119
Bibkey:
Cite (ACL):
Shun Kiyono, Jun Suzuki, Masato Mita, Tomoya Mizumoto, and Kentaro Inui. 2019. An Empirical Study of Incorporating Pseudo Data into Grammatical Error Correction. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 1236–1242, Hong Kong, China. Association for Computational Linguistics.
Cite (Informal):
An Empirical Study of Incorporating Pseudo Data into Grammatical Error Correction (Kiyono et al., EMNLP-IJCNLP 2019)
Copy Citation:
PDF:
https://preview.aclanthology.org/ml4al-ingestion/D19-1119.pdf
Attachment:
 D19-1119.Attachment.pdf
Code
 butsugiri/gec-pseudodata
Data
CoNLL, CoNLL-2014 Shared Task: Grammatical Error Correction, JFLEG, WI-LOCNESS
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