@inproceedings{wang-etal-2025-refined,
title = "Refined Evaluation for End-to-End Grammatical Error Correction Using an Alignment-Based Approach",
author = "Wang, Junrui and
Qiu, Mengyang and
Gu, Yang and
Huang, Zihao and
Park, Jungyeul",
editor = "Rambow, Owen and
Wanner, Leo and
Apidianaki, Marianna and
Al-Khalifa, Hend and
Eugenio, Barbara Di and
Schockaert, Steven",
booktitle = "Proceedings of the 31st International Conference on Computational Linguistics",
month = jan,
year = "2025",
address = "Abu Dhabi, UAE",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.coling-main.52/",
pages = "774--785",
abstract = "We propose a refined alignment-based method to assess end-to-end grammatical error correction (GEC) systems, aiming to reproduce and improve results from existing evaluation tools, such as errant, even when applied to raw text input{---}reflecting real-world language learners' writing scenarios. Our approach addresses challenges arising from sentence boundary detection deviations in text preprocessing, a factor overlooked by current GEC evaluation metrics. We demonstrate its effectiveness by replicating results through a re-implementation of errant, utilizing stanza for error annotation and simulating end-to-end evaluation from raw text. Additionally, we propose a potential multilingual errant, presenting Chinese and Korean GEC results. Previously, Chinese and Korean errant were implemented independently for each language, with different annotation formats. Our approach generates consistent error annotations across languages, establishing a basis for standardized grammatical error annotation and evaluation in multilingual GEC contexts."
}
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%0 Conference Proceedings
%T Refined Evaluation for End-to-End Grammatical Error Correction Using an Alignment-Based Approach
%A Wang, Junrui
%A Qiu, Mengyang
%A Gu, Yang
%A Huang, Zihao
%A Park, Jungyeul
%Y Rambow, Owen
%Y Wanner, Leo
%Y Apidianaki, Marianna
%Y Al-Khalifa, Hend
%Y Eugenio, Barbara Di
%Y Schockaert, Steven
%S Proceedings of the 31st International Conference on Computational Linguistics
%D 2025
%8 January
%I Association for Computational Linguistics
%C Abu Dhabi, UAE
%F wang-etal-2025-refined
%X We propose a refined alignment-based method to assess end-to-end grammatical error correction (GEC) systems, aiming to reproduce and improve results from existing evaluation tools, such as errant, even when applied to raw text input—reflecting real-world language learners’ writing scenarios. Our approach addresses challenges arising from sentence boundary detection deviations in text preprocessing, a factor overlooked by current GEC evaluation metrics. We demonstrate its effectiveness by replicating results through a re-implementation of errant, utilizing stanza for error annotation and simulating end-to-end evaluation from raw text. Additionally, we propose a potential multilingual errant, presenting Chinese and Korean GEC results. Previously, Chinese and Korean errant were implemented independently for each language, with different annotation formats. Our approach generates consistent error annotations across languages, establishing a basis for standardized grammatical error annotation and evaluation in multilingual GEC contexts.
%U https://aclanthology.org/2025.coling-main.52/
%P 774-785
Markdown (Informal)
[Refined Evaluation for End-to-End Grammatical Error Correction Using an Alignment-Based Approach](https://aclanthology.org/2025.coling-main.52/) (Wang et al., COLING 2025)
ACL