@inproceedings{chen-etal-2020-improving-efficiency,
title = "Improving the Efficiency of Grammatical Error Correction with Erroneous Span Detection and Correction",
author = "Chen, Mengyun and
Ge, Tao and
Zhang, Xingxing and
Wei, Furu and
Zhou, Ming",
editor = "Webber, Bonnie and
Cohn, Trevor and
He, Yulan and
Liu, Yang",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.emnlp-main.581",
doi = "10.18653/v1/2020.emnlp-main.581",
pages = "7162--7169",
abstract = "We propose a novel language-independent approach to improve the efficiency for Grammatical Error Correction (GEC) by dividing the task into two subtasks: Erroneous Span Detection (ESD) and Erroneous Span Correction (ESC). ESD identifies grammatically incorrect text spans with an efficient sequence tagging model. Then, ESC leverages a seq2seq model to take the sentence with annotated erroneous spans as input and only outputs the corrected text for these spans. Experiments show our approach performs comparably to conventional seq2seq approaches in both English and Chinese GEC benchmarks with less than 50{\%} time cost for inference.",
}
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<abstract>We propose a novel language-independent approach to improve the efficiency for Grammatical Error Correction (GEC) by dividing the task into two subtasks: Erroneous Span Detection (ESD) and Erroneous Span Correction (ESC). ESD identifies grammatically incorrect text spans with an efficient sequence tagging model. Then, ESC leverages a seq2seq model to take the sentence with annotated erroneous spans as input and only outputs the corrected text for these spans. Experiments show our approach performs comparably to conventional seq2seq approaches in both English and Chinese GEC benchmarks with less than 50% time cost for inference.</abstract>
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%0 Conference Proceedings
%T Improving the Efficiency of Grammatical Error Correction with Erroneous Span Detection and Correction
%A Chen, Mengyun
%A Ge, Tao
%A Zhang, Xingxing
%A Wei, Furu
%A Zhou, Ming
%Y Webber, Bonnie
%Y Cohn, Trevor
%Y He, Yulan
%Y Liu, Yang
%S Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F chen-etal-2020-improving-efficiency
%X We propose a novel language-independent approach to improve the efficiency for Grammatical Error Correction (GEC) by dividing the task into two subtasks: Erroneous Span Detection (ESD) and Erroneous Span Correction (ESC). ESD identifies grammatically incorrect text spans with an efficient sequence tagging model. Then, ESC leverages a seq2seq model to take the sentence with annotated erroneous spans as input and only outputs the corrected text for these spans. Experiments show our approach performs comparably to conventional seq2seq approaches in both English and Chinese GEC benchmarks with less than 50% time cost for inference.
%R 10.18653/v1/2020.emnlp-main.581
%U https://aclanthology.org/2020.emnlp-main.581
%U https://doi.org/10.18653/v1/2020.emnlp-main.581
%P 7162-7169
Markdown (Informal)
[Improving the Efficiency of Grammatical Error Correction with Erroneous Span Detection and Correction](https://aclanthology.org/2020.emnlp-main.581) (Chen et al., EMNLP 2020)
ACL