{"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/grammatical-error-correction-in-low-resource","title":"Grammatical Error Correction in Low-Resource Scenarios","arxiv_id":"1910.00353","date":"2019-10-01","proceeding":"WS 2019 11","authors":["Jakub Náplava","Milan Straka"],"abstract":"Grammatical error correction in English is a long studied problem with many existing systems and datasets. However, there has been only a limited research on error correction of other languages. In this paper, we present a new dataset AKCES-GEC on grammatical error correction for Czech. We then make experiments on Czech, German and Russian and show that when utilizing synthetic parallel corpus, Transformer neural machine translation model can reach new state-of-the-art results on these datasets. AKCES-GEC is published under CC BY-NC-SA 4.0 license at https://hdl.handle.net/11234/1-3057 and the source code of the GEC model is available at https://github.com/ufal/low-resource-gec-wnut2019.","url_abs":"https://arxiv.org/abs/1910.00353v3","url_pdf":"https://arxiv.org/pdf/1910.00353v3.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":"grammatical-error-correction-in-low-resource","repo_url":"https://github.com/ufal/low-resource-gec-wnut2019","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"grammatical-error-correction","task_name":"Grammatical Error Correction"},{"task_slug":"machine-translation","task_name":"Machine Translation"},{"task_slug":"translation","task_name":"Translation"}],"methods":[{"method_slug":"absolute-position-encodings","method_name":"Absolute Position Encodings"},{"method_slug":"adam","method_name":"Adam"},{"method_slug":"attention","method_name":"Attention"},{"method_slug":"bpe","method_name":"BPE"},{"method_slug":"dense-connections","method_name":"Dense Connections"},{"method_slug":"dropout","method_name":"Dropout"},{"method_slug":"label-smoothing","method_name":"Label Smoothing"},{"method_slug":"layer-normalization","method_name":"Layer Normalization"},{"method_slug":"linear-layer","method_name":"Linear Layer"},{"method_slug":"multi-head-attention","method_name":"Multi-Head Attention"},{"method_slug":"position-wise-feed-forward-layer","method_name":"Position-Wise Feed-Forward Layer"},{"method_slug":"relu","method_name":"ReLU"},{"method_slug":"residual-connection","method_name":"Residual Connection"},{"method_slug":"softmax","method_name":"Softmax"},{"method_slug":"transformer","method_name":"Transformer"}],"datasets_introduced":[{"slug":"akces-gec","name":"AKCES-GEC","full_name":""}],"methods_introduced":[],"results":[{"leaderboard":"/sota/grammatical-error-correction-on-falko-merlin","task":"Grammatical Error Correction","dataset":"Falko-MERLIN","model":"Transformer","rank_in_archive_order":4,"of":6,"metrics":{"F0.5":"73.71"},"uses_additional_data":true},{"leaderboard":"/sota/grammatical-error-correction-on-falko-merlin","task":"Grammatical Error Correction","dataset":"Falko-MERLIN","model":"Transformer - synthetic pretrain only","rank_in_archive_order":5,"of":6,"metrics":{"F0.5":"51.41"},"uses_additional_data":true}],"syntology":{"syntology_url":"https://syntology.ai/paper/1910.00353","atlas_url":"https://app.syntology.ai/?focus=1910.00353","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}