{"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/wronging-a-right-generating-better-errors-to-1","title":"Wronging a Right: Generating Better Errors to Improve Grammatical Error Detection","arxiv_id":"1810.00668","date":"2018-09-26","proceeding":"EMNLP 2018 10","authors":["Kasewa Sudhanshu","Stenetorp Pontus","Riedel Sebastian"],"abstract":"Grammatical error correction, like other machine learning tasks, greatly\nbenefits from large quantities of high quality training data, which is\ntypically expensive to produce. While writing a program to automatically\ngenerate realistic grammatical errors would be difficult, one could learn the\ndistribution of naturallyoccurring errors and attempt to introduce them into\nother datasets. Initial work on inducing errors in this way using statistical\nmachine translation has shown promise; we investigate cheaply constructing\nsynthetic samples, given a small corpus of human-annotated data, using an\noff-the-rack attentive sequence-to-sequence model and a straight-forward\npost-processing procedure. Our approach yields error-filled artificial data\nthat helps a vanilla bi-directional LSTM to outperform the previous state of\nthe art at grammatical error detection, and a previously introduced model to\ngain further improvements of over 5% $F_{0.5}$ score. When attempting to\ndetermine if a given sentence is synthetic, a human annotator at best achieves\n39.39 $F_1$ score, indicating that our model generates mostly human-like\ninstances.","url_abs":"http://arxiv.org/abs/1810.00668v1","url_pdf":"http://arxiv.org/pdf/1810.00668v1.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":"wronging-a-right-generating-better-errors-to-1","repo_url":"https://github.com/skasewa/wronging","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"tf","reach":null}],"tasks":[{"task_slug":"grammatical-error-correction","task_name":"Grammatical Error Correction"},{"task_slug":"grammatical-error-detection","task_name":"Grammatical Error Detection"},{"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=1810.00668","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}