{"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/neural-network-translation-models-for","title":"Neural Network Translation Models for Grammatical Error Correction","arxiv_id":"1606.00189","date":"2016-06-01","proceeding":null,"authors":["Shamil Chollampatt","Kaveh Taghipour","Hwee Tou Ng"],"abstract":"Phrase-based statistical machine translation (SMT) systems have previously\nbeen used for the task of grammatical error correction (GEC) to achieve\nstate-of-the-art accuracy. The superiority of SMT systems comes from their\nability to learn text transformations from erroneous to corrected text, without\nexplicitly modeling error types. However, phrase-based SMT systems suffer from\nlimitations of discrete word representation, linear mapping, and lack of global\ncontext. In this paper, we address these limitations by using two different yet\ncomplementary neural network models, namely a neural network global lexicon\nmodel and a neural network joint model. These neural networks can generalize\nbetter by using continuous space representation of words and learn non-linear\nmappings. Moreover, they can leverage contextual information from the source\nsentence more effectively. By adding these two components, we achieve\nstatistically significant improvement in accuracy for grammatical error\ncorrection over a state-of-the-art GEC system.","url_abs":"http://arxiv.org/abs/1606.00189v1","url_pdf":"http://arxiv.org/pdf/1606.00189v1.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":"neural-network-translation-models-for","repo_url":"https://github.com/seaweiqing/image2story","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"unanswered"}},{"paper_slug":"neural-network-translation-models-for","repo_url":"https://github.com/seaweiqing/neuraltalk_plus_charcnn","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"unanswered"}}],"tasks":[{"task_slug":"grammatical-error-correction","task_name":"Grammatical Error Correction"},{"task_slug":"machine-translation","task_name":"Machine Translation"},{"task_slug":"sentence","task_name":"Sentence"},{"task_slug":"translation","task_name":"Translation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1606.00189","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}