@inproceedings{mesham-etal-2023-extended,
title = "An Extended Sequence Tagging Vocabulary for Grammatical Error Correction",
author = "Mesham, Stuart and
Bryant, Christopher and
Rei, Marek and
Yuan, Zheng",
editor = "Vlachos, Andreas and
Augenstein, Isabelle",
booktitle = "Findings of the Association for Computational Linguistics: EACL 2023",
month = may,
year = "2023",
address = "Dubrovnik, Croatia",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.findings-eacl.119",
doi = "10.18653/v1/2023.findings-eacl.119",
pages = "1608--1619",
abstract = "We extend a current sequence-tagging approach to Grammatical Error Correction (GEC) by introducing specialised tags for spelling correction and morphological inflection using the SymSpell and LemmInflect algorithms. Our approach improves generalisation: the proposed new tagset allows a smaller number of tags to correct a larger range of errors. Our results show a performance improvement both overall and in the targeted error categories. We further show that ensembles trained with our new tagset outperform those trained with the baseline tagset on the public BEA benchmark.",
}
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%0 Conference Proceedings
%T An Extended Sequence Tagging Vocabulary for Grammatical Error Correction
%A Mesham, Stuart
%A Bryant, Christopher
%A Rei, Marek
%A Yuan, Zheng
%Y Vlachos, Andreas
%Y Augenstein, Isabelle
%S Findings of the Association for Computational Linguistics: EACL 2023
%D 2023
%8 May
%I Association for Computational Linguistics
%C Dubrovnik, Croatia
%F mesham-etal-2023-extended
%X We extend a current sequence-tagging approach to Grammatical Error Correction (GEC) by introducing specialised tags for spelling correction and morphological inflection using the SymSpell and LemmInflect algorithms. Our approach improves generalisation: the proposed new tagset allows a smaller number of tags to correct a larger range of errors. Our results show a performance improvement both overall and in the targeted error categories. We further show that ensembles trained with our new tagset outperform those trained with the baseline tagset on the public BEA benchmark.
%R 10.18653/v1/2023.findings-eacl.119
%U https://aclanthology.org/2023.findings-eacl.119
%U https://doi.org/10.18653/v1/2023.findings-eacl.119
%P 1608-1619
Markdown (Informal)
[An Extended Sequence Tagging Vocabulary for Grammatical Error Correction](https://aclanthology.org/2023.findings-eacl.119) (Mesham et al., Findings 2023)
ACL