Grammatical Error Detection
17 papers with code • 4 benchmarks • 4 datasets
Grammatical Error Detection (GED) is the task of detecting different kinds of errors in text such as spelling, punctuation, grammatical, and word choice errors. Grammatical error detection (GED) is one of the key component in grammatical error correction (GEC) community.
Benchmarks
These leaderboards are used to track progress in Grammatical Error Detection
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CoNLL
JFLEG
FCGEC
Most implemented papers
Neural Quality Estimation with Multiple Hypotheses for Grammatical Error Correction
Grammatical Error Correction (GEC) aims to correct writing errors and help language learners improve their writing skills.
Probing for targeted syntactic knowledge through grammatical error detection
chrisdavis90/ged-syntax-probing
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Targeted studies testing knowledge of subject-verb agreement (SVA) indicate that pre-trained language models encode syntactic information.
Advancements in Arabic Grammatical Error Detection and Correction: An Empirical Investigation
We also define the task of multi-class Arabic grammatical error detection (GED) and present the first results on multi-class Arabic GED.
Evaluation of really good grammatical error correction
We find that GPT-3 in a few-shot setting by far outperforms previous grammatical error correction systems for Swedish, a language comprising only 0. 11% of its training data.
GECTurk: Grammatical Error Correction and Detection Dataset for Turkish
GGLAB-KU/gecturk
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To encourage further research on Turkish GEC, we release our datasets, baseline models, and the synthetic data generation pipeline at https://github. com/GGLAB-KU/gecturk.
