{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,15]],"date-time":"2026-05-15T21:17:37Z","timestamp":1778879857189,"version":"3.51.4"},"reference-count":21,"publisher":"Association for Computing Machinery (ACM)","issue":"1","license":[{"start":{"date-parts":[[2025,1,20]],"date-time":"2025-01-20T00:00:00Z","timestamp":1737331200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"Guangdong Philosophy and Social Science Foundation Regular","award":["GD20CWY10"],"award-info":[{"award-number":["GD20CWY10"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Asian Low-Resour. Lang. Inf. Process."],"published-print":{"date-parts":[[2025,1,31]]},"abstract":"<jats:p>\n            Currently, the majority of research in grammatical error correction (GEC) is concentrated on universal languages, such as English and Chinese. Many low-resource languages lack accessible evaluation corpora. How to efficiently construct high-quality evaluation corpora for GEC in low-resource languages has become a significant challenge. To fill these gaps, in this article, we present a framework for constructing GEC corpora. Specifically, we focus on Indonesian as our research language and construct an evaluation corpus for Indonesian GEC using the proposed framework, addressing the limitations of existing evaluation corpora in Indonesian. Furthermore, we investigate the feasibility of utilizing existing large language models (LLMs), such as GPT-3.5-Turbo and GPT-4, to streamline corpus annotation efforts in GEC tasks. The results demonstrate significant potential for enhancing the performance of LLMs in low-resource language settings. Our code and corpus can be obtained from\n            <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" xlink:href=\"https:\/\/github.com\/GKLMIP\/GEC-Construction-Framework\">https:\/\/github.com\/GKLMIP\/GEC-Construction-Framework<\/jats:ext-link>\n            .\n          <\/jats:p>","DOI":"10.1145\/3704264","type":"journal-article","created":{"date-parts":[[2024,11,12]],"date-time":"2024-11-12T11:22:46Z","timestamp":1731410566000},"page":"1-15","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":6,"title":["A Simple Yet Effective Corpus Construction Framework for Indonesian Grammatical Error Correction"],"prefix":"10.1145","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2838-8273","authenticated-orcid":false,"given":"Nankai","family":"Lin","sequence":"first","affiliation":[{"name":"School of Information Science and Technology, Guangdong University of Foreign Studies, Guangzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-9324-8930","authenticated-orcid":false,"given":"Meiyu","family":"Zeng","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Guangdong University of Technology, Guangzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-7533-383X","authenticated-orcid":false,"given":"Wentao","family":"Huang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Guangdong University of Technology, Guangzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6753-474X","authenticated-orcid":false,"given":"Shengyi","family":"Jiang","sequence":"additional","affiliation":[{"name":"School of Information Technology and Engineering, Guangzhou College of Commerce, Guangzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-7320-3333","authenticated-orcid":false,"given":"Lixian","family":"Xiao","sequence":"additional","affiliation":[{"name":"Faculty of Asian Languages and Cultures, Guangdong University of Foreign Studies, Guangzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7671-6150","authenticated-orcid":false,"given":"Aimin","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Guangdong University of Technology, Guangzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2025,1,20]]},"reference":[{"key":"e_1_3_2_2_2","first-page":"1315","article-title":"Comparative study of rule-based approach for grammar checker","volume":"9","author":"Baviskar Swapnali Deelip","year":"2019","unstructured":"Swapnali Deelip Baviskar and Sushant S. Bahekar. 2019. Comparative study of rule-based approach for grammar checker. Int. J. Manage. Technol. Eng. 9 (2019), 1315.","journal-title":"Int. J. Manage. Technol. Eng."},{"key":"e_1_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.1162\/coli_a_00478"},{"key":"e_1_3_2_4_2","unstructured":"Zheng Cai Maosong Cao Haojiong Chen Kai Chen Keyu Chen Xin Chen Xun Chen Zehui Chen Zhi Chen Pei Chu et\u00a0al. 2024. InternLM2 Technical Report. arxiv:2403.17297 [cs.CL] https:\/\/arxiv.org\/abs\/2403.17297"},{"key":"e_1_3_2_5_2","doi-asserted-by":"crossref","unstructured":"Yaxin Fan Feng Jiang Peifeng Li and Haizhou Li. 2023. GrammarGPT: Exploring Open-Source LLMs for Native Chinese Grammatical Error Correction with Supervised Fine-Tuning. arxiv:2307.13923 [cs.CL].","DOI":"10.1007\/978-3-031-44699-3_7"},{"key":"e_1_3_2_6_2","unstructured":"Tao Fang Shu Yang Kaixin Lan Derek F. Wong Jinpeng Hu Lidia S. Chao and Yue Zhang. 2023. Is ChatGPT a Highly Fluent Grammatical Error Correction System? A Comprehensive Evaluation. arxiv:2304.01746 [cs.CL]."},{"key":"e_1_3_2_7_2","doi-asserted-by":"publisher","DOI":"10.3115\/v1\/P14-2029"},{"key":"e_1_3_2_8_2","first-page":"204","volume-title":"Proceedings of the 12th Language Resources and Evaluation Conference","author":"Koyama Aomi","year":"2020","unstructured":"Aomi Koyama, Tomoshige Kiyuna, Kenji Kobayashi, Mio Arai, and Mamoru Komachi. 2020. Construction of an evaluation corpus for grammatical error correction for learners of Japanese as a second language. In Proceedings of the 12th Language Resources and Evaluation Conference. European Language Resources Association, 204\u2013211. https:\/\/aclanthology.org\/2020.lrec-1.26"},{"key":"e_1_3_2_9_2","unstructured":"Sang Yun Kwon Gagan Bhatia El Moatez Billah Nagoud and Muhammad Abdul-Mageed. 2023. ChatGPT for Arabic Grammatical Error Correction. arxiv:2308.04492 [cs.AI]."},{"key":"e_1_3_2_10_2","doi-asserted-by":"publisher","DOI":"10.1145\/3440993"},{"key":"e_1_3_2_11_2","unstructured":"Nankai Lin Hongbin Zhang Menglan Shen Yu Wang Shengyi Jiang and Aimin Yang. 2023. A BERT-based Unsupervised Grammatical Error Correction Framework. arxiv:2303.17367 [cs.CL]."},{"key":"e_1_3_2_12_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2023.bea-1.18"},{"key":"e_1_3_2_13_2","doi-asserted-by":"publisher","DOI":"10.3390\/app122010380"},{"key":"e_1_3_2_14_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/E17-2037"},{"key":"e_1_3_2_15_2","doi-asserted-by":"publisher","DOI":"10.3115\/v1\/W14-1701"},{"key":"e_1_3_2_16_2","unstructured":"OpenAI. 2023. GPT-4 Technical Report. arxiv:2303.08774 [cs.CL]."},{"key":"e_1_3_2_17_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jksuci.2023.101572"},{"key":"e_1_3_2_18_2","first-page":"5565","volume-title":"Proceedings of the 13th Language Resources and Evaluation Conference","author":"Suzuki Daisuke","year":"2022","unstructured":"Daisuke Suzuki, Yujin Takahashi, Ikumi Yamashita, Taichi Aida, Tosho Hirasawa, Michitaka Nakatsuji, Masato Mita, and Mamoru Komachi. 2022. Construction of a quality estimation dataset for automatic evaluation of Japanese grammatical error correction. In Proceedings of the 13th Language Resources and Evaluation Conference. European Language Resources Association, 5565\u20135572. https:\/\/aclanthology.org\/2022.lrec-1.596"},{"key":"e_1_3_2_19_2","unstructured":"Xiangpeng Wei Haoran Wei Huan Lin Tianhao Li Pei Zhang Xingzhang Ren Mei Li Yu Wan Zhiwei Cao Binbin Xie et\u00a0al. 2023. PolyLM: An Open Source Polyglot Large Language Model. arxiv:2307.06018 [cs.CL] https:\/\/arxiv.org\/abs\/2307.06018"},{"key":"e_1_3_2_20_2","first-page":"11941","volume-title":"International Conference on Machine Learning","author":"Yasunaga Michihiro","year":"2021","unstructured":"Michihiro Yasunaga and Percy Liang. 2021. Break-it-fix-it: Unsupervised learning for program repair. In International Conference on Machine Learning. PMLR, 11941\u201311952."},{"key":"e_1_3_2_21_2","article-title":"Few-shot domain adaptation for grammatical error correction via meta-learning","author":"Zhang Shengsheng","year":"2021","unstructured":"Shengsheng Zhang, Yaping Huang, Yun Chen, Liner Yang, Chencheng Wang, and Erhong Yang. 2021. 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