@inproceedings{kiyono-etal-2019-empirical,
title = "An Empirical Study of Incorporating Pseudo Data into Grammatical Error Correction",
author = "Kiyono, Shun and
Suzuki, Jun and
Mita, Masato and
Mizumoto, Tomoya and
Inui, Kentaro",
editor = "Inui, Kentaro and
Jiang, Jing and
Ng, Vincent and
Wan, Xiaojun",
booktitle = "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 = nov,
year = "2019",
address = "Hong Kong, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D19-1119",
doi = "10.18653/v1/D19-1119",
pages = "1236--1242",
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.",
}
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%0 Conference Proceedings
%T An Empirical Study of Incorporating Pseudo Data into Grammatical Error Correction
%A Kiyono, Shun
%A Suzuki, Jun
%A Mita, Masato
%A Mizumoto, Tomoya
%A Inui, Kentaro
%Y Inui, Kentaro
%Y Jiang, Jing
%Y Ng, Vincent
%Y Wan, Xiaojun
%S Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong, China
%F kiyono-etal-2019-empirical
%X 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.
%R 10.18653/v1/D19-1119
%U https://aclanthology.org/D19-1119
%U https://doi.org/10.18653/v1/D19-1119
%P 1236-1242
Markdown (Informal)
[An Empirical Study of Incorporating Pseudo Data into Grammatical Error Correction](https://aclanthology.org/D19-1119) (Kiyono et al., EMNLP-IJCNLP 2019)
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.