@inproceedings{song-etal-2025-unified,
title = "Unified Automated Essay Scoring and Grammatical Error Correction",
author = "Song, SeungWoo and
Yuk, Junghun and
Choi, ChangSu and
Yoo, HanGyeol and
Lim, HyeonSeok and
Lim, KyungTae and
Park, Jungyeul",
editor = "Chiruzzo, Luis and
Ritter, Alan and
Wang, Lu",
booktitle = "Findings of the Association for Computational Linguistics: NAACL 2025",
month = apr,
year = "2025",
address = "Albuquerque, New Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.findings-naacl.250/",
doi = "10.18653/v1/2025.findings-naacl.250",
pages = "4412--4426",
ISBN = "979-8-89176-195-7",
abstract = "This study explores the integration of automated writing evaluation (AWE) and grammatical error correction (GEC) through multitask learning, demonstrating how combining these distinct tasks can enhance performance in both areas. By leveraging a shared learning framework, we show that models trained jointly on AWE and GEC outperform those trained on each task individually. To support this effort, we introduce a dataset specifically designed for multitask learning using AWE and GEC. Our experiments reveal significant synergies between tasks, leading to improvements in both writing assessment accuracy and error correction precision. This research represents a novel approach for optimizing language learning tools by unifying writing evaluation and correction tasks, offering insights into the potential of multitask learning in educational applications."
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<abstract>This study explores the integration of automated writing evaluation (AWE) and grammatical error correction (GEC) through multitask learning, demonstrating how combining these distinct tasks can enhance performance in both areas. By leveraging a shared learning framework, we show that models trained jointly on AWE and GEC outperform those trained on each task individually. To support this effort, we introduce a dataset specifically designed for multitask learning using AWE and GEC. Our experiments reveal significant synergies between tasks, leading to improvements in both writing assessment accuracy and error correction precision. This research represents a novel approach for optimizing language learning tools by unifying writing evaluation and correction tasks, offering insights into the potential of multitask learning in educational applications.</abstract>
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%0 Conference Proceedings
%T Unified Automated Essay Scoring and Grammatical Error Correction
%A Song, SeungWoo
%A Yuk, Junghun
%A Choi, ChangSu
%A Yoo, HanGyeol
%A Lim, HyeonSeok
%A Lim, KyungTae
%A Park, Jungyeul
%Y Chiruzzo, Luis
%Y Ritter, Alan
%Y Wang, Lu
%S Findings of the Association for Computational Linguistics: NAACL 2025
%D 2025
%8 April
%I Association for Computational Linguistics
%C Albuquerque, New Mexico
%@ 979-8-89176-195-7
%F song-etal-2025-unified
%X This study explores the integration of automated writing evaluation (AWE) and grammatical error correction (GEC) through multitask learning, demonstrating how combining these distinct tasks can enhance performance in both areas. By leveraging a shared learning framework, we show that models trained jointly on AWE and GEC outperform those trained on each task individually. To support this effort, we introduce a dataset specifically designed for multitask learning using AWE and GEC. Our experiments reveal significant synergies between tasks, leading to improvements in both writing assessment accuracy and error correction precision. This research represents a novel approach for optimizing language learning tools by unifying writing evaluation and correction tasks, offering insights into the potential of multitask learning in educational applications.
%R 10.18653/v1/2025.findings-naacl.250
%U https://aclanthology.org/2025.findings-naacl.250/
%U https://doi.org/10.18653/v1/2025.findings-naacl.250
%P 4412-4426
Markdown (Informal)
[Unified Automated Essay Scoring and Grammatical Error Correction](https://aclanthology.org/2025.findings-naacl.250/) (Song et al., Findings 2025)
ACL
- SeungWoo Song, Junghun Yuk, ChangSu Choi, HanGyeol Yoo, HyeonSeok Lim, KyungTae Lim, and Jungyeul Park. 2025. Unified Automated Essay Scoring and Grammatical Error Correction. In Findings of the Association for Computational Linguistics: NAACL 2025, pages 4412–4426, Albuquerque, New Mexico. Association for Computational Linguistics.