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This paper elaborates the Adversarial Temporal Graph Convolution Model (AT-GCM), which combines the capabilities of MT-5, adversarial learning, and temporal graph convolutional neural network (t-GCN) to achieve accurate progress in multilingual grammatical error correction. The inherent capability of MT-5 to process multiple languages simultaneously serves as a powerful embedding generator for the purpose of multilingual error correction. The t-GCN is employed for the purpose of navigating the temporal context and interdependencies present within words. The assumption that modeling the dynamic interactions among words within the context of temporal relationships improves precision, particularly in languages with complex sentence structures, is supported by research. The utilization of adversarial learning techniques can enhance the generalization capabilities of the model across various language pairings, effectively addressing the challenges associated with low-resource languages. A comprehensive analysis is carried out on a diverse, multilingual dataset comprising various languages, viz. English, Russian, German, Czech, Arabic, and Romanian. The experimental results present significant improvements in grammatical error correction performance compared to state-of-the-art models. Our approach effectively resolves grammatical errors in various linguistic contexts by utilizing a combination of MT-5, adversarial learning, and t-GCN.<\/jats:p>","DOI":"10.1145\/3696106","type":"journal-article","created":{"date-parts":[[2024,10,4]],"date-time":"2024-10-04T09:32:27Z","timestamp":1728034347000},"page":"1-15","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":6,"title":["Context-Aware Adversarial Graph-Based Learning for Multilingual Grammatical Error Correction"],"prefix":"10.1145","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7802-0026","authenticated-orcid":false,"given":"Naresh","family":"Kumar","sequence":"first","affiliation":[{"name":"Computer Science, University of Nizwa, Nizwa, Oman"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5089-2965","authenticated-orcid":false,"given":"Parveen","family":"Kumar","sequence":"additional","affiliation":[{"name":"Computer Engineering, National Institute of Technology Kurukshetra, Kurukshetra, India"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2488-4520","authenticated-orcid":false,"given":"Sushreeta","family":"Tripathy","sequence":"additional","affiliation":[{"name":"Computer Application, Siksha O Anusandhan University Institute of Technical Education and Research, Bhubaneswar, India"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1026-5227","authenticated-orcid":false,"given":"Neelamani","family":"Samal","sequence":"additional","affiliation":[{"name":"Computer Science &amp; Engineering, Chandigarh University, Mohali, India"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9079-1074","authenticated-orcid":false,"given":"Debasis","family":"Gountia","sequence":"additional","affiliation":[{"name":"School of Computer Sciences, Odisha University of Technology and Research, Bhubaneswar, India"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8042-8685","authenticated-orcid":false,"given":"Praveen","family":"Gatla","sequence":"additional","affiliation":[{"name":"Department of Linguistics, Faculty of Arts, BHU, Varanasi, India"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8050-5639","authenticated-orcid":false,"given":"Teekam","family":"Singh","sequence":"additional","affiliation":[{"name":"Computer Science and Engineering, Graphic Era Deemed to be University, Dehradun, India"}]}],"member":"320","published-online":{"date-parts":[[2024,11,23]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2023.3294979"},{"key":"e_1_3_2_3_2","doi-asserted-by":"crossref","first-page":"176","DOI":"10.18653\/v1\/W19-4418","volume-title":"Proceedings of the Fourteenth Workshop on Innovative use of NLP for Building Educational Applications","author":"Asano Hiroki","year":"2019","unstructured":"Hiroki Asano, Masato Mita, Tomoya Mizumoto, and Jun Suzuki. 2019. 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