{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/comparative-study-of-models-trained-on","title":"Comparative study of models trained on synthetic data for Ukrainian grammatical error correction","arxiv_id":null,"date":"2024-05-05","proceeding":"EACL 2024 5","authors":["Maksym Bondarenko","Artem Yushko","Andrii Shportko","Andrii Fedorych"],"abstract":"The task of Grammatical Error Correction (GEC) has been extensively studied for the English language. However, its application to low-resource languages, such as Ukrainian, remains an open challenge. In this paper, we develop sequence tagging and neural machine translation models for the Ukrainian language as well as a set of algorithmic correction rules to augment those systems. We also develop synthetic data generation techniques for the Ukrainian language to create high-quality human-like errors. Finally, we determine the best combination of synthetically generated data to augment the existing UA-GEC corpus and achieve the state-of-the-art results of 0.663 F0. 5 score on the newly established UA-GEC benchmark. The code and trained models will be made publicly available on GitHub and HuggingFace.","url_abs":"https://aclanthology.org/2023.unlp-1.13/","url_pdf":"https://aclanthology.org/2023.unlp-1.13.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"comparative-study-of-models-trained-on","repo_url":"https://github.com/pravopysnyk-ai/unlp","is_official":0,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"grammatical-error-correction","task_name":"Grammatical Error Correction"},{"task_slug":"machine-translation","task_name":"Machine Translation"},{"task_slug":"synthetic-data-generation","task_name":"Synthetic Data Generation"}],"methods":[{"method_slug":"set","method_name":"SET"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/grammatical-error-correction-on-ua-gec","task":"Grammatical Error Correction","dataset":"UA-GEC","model":"mBART-based model with synthetic\ndata","rank_in_archive_order":2,"of":5,"metrics":{"F0.5":"68.17"},"uses_additional_data":true}],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}