Erroneous data generation for Grammatical Error Correction

Shuyao Xu, Jiehao Zhang, Jin Chen, Long Qin


Abstract
It has been demonstrated that the utilization of a monolingual corpus in neural Grammatical Error Correction (GEC) systems can significantly improve the system performance. The previous state-of-the-art neural GEC system is an ensemble of four Transformer models pretrained on a large amount of Wikipedia Edits. The Singsound GEC system follows a similar approach but is equipped with a sophisticated erroneous data generating component. Our system achieved an F0:5 of 66.61 in the BEA 2019 Shared Task: Grammatical Error Correction. With our novel erroneous data generating component, the Singsound neural GEC system yielded an M2 of 63.2 on the CoNLL-2014 benchmark (8.4% relative improvement over the previous state-of-the-art system).
Anthology ID:
W19-4415
Volume:
Proceedings of the Fourteenth Workshop on Innovative Use of NLP for Building Educational Applications
Month:
August
Year:
2019
Address:
Florence, Italy
Venues:
ACL | BEA | WS
SIG:
SIGEDU
Publisher:
Association for Computational Linguistics
Note:
Pages:
149–158
Language:
URL:
https://aclanthology.org/W19-4415
DOI:
10.18653/v1/W19-4415
Bibkey:
Cite (ACL):
Shuyao Xu, Jiehao Zhang, Jin Chen, and Long Qin. 2019. Erroneous data generation for Grammatical Error Correction. In Proceedings of the Fourteenth Workshop on Innovative Use of NLP for Building Educational Applications, pages 149–158, Florence, Italy. Association for Computational Linguistics.
Cite (Informal):
Erroneous data generation for Grammatical Error Correction (Xu et al., 2019)
Copy Citation:
PDF:
https://aclanthology.org/W19-4415.pdf
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