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Grammatical Error Detection

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.

Papers

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Showing 1–25 of 100 papers

TitleStatusHype
GECTurk WEB: An Explainable Online Platform for Turkish Grammatical Error Detection and CorrectionCode1
GECTurk: Grammatical Error Correction and Detection Dataset for TurkishCode1
Advancements in Arabic Grammatical Error Detection and Correction: An Empirical InvestigationCode1
FCGEC: Fine-Grained Corpus for Chinese Grammatical Error CorrectionCode1
Neural Quality Estimation with Multiple Hypotheses for Grammatical Error CorrectionCode1
Context is Key: Grammatical Error Detection with Contextual Word RepresentationsCode1
Jointly Learning to Label Sentences and TokensCode1
Semi-supervised Multitask Learning for Sequence LabelingCode1
IMPARA-GED: Grammatical Error Detection is Boosting Reference-free Grammatical Error Quality Estimator—0
Detecting Spelling and Grammatical Anomalies in Russian Poetry Texts—0
ARWI: Arabic Write and Improve—0
Enhancing Grammatical Error Detection using BERT with Cleaned Lang-8 DatasetCode0
Bangla Grammatical Error Detection Leveraging Transformer-based Token Classification—0
Oddballness: universal anomaly detection with language models—0
Zero-shot Cross-Lingual Transfer for Synthetic Data Generation in Grammatical Error Detection—0
Evaluation of really good grammatical error correctionCode0
Bangla Grammatical Error Detection Using T5 Transformer ModelCode0
Probing for targeted syntactic knowledge through grammatical error detectionCode0
The Tembusu Treebank: An English Learner TreebankCode0
A Warm Start and a Clean Crawled Corpus - A Recipe for Good Language Models—0
Semi-automatically Annotated Learner Corpus for Russian—0
Approximate Conditional Coverage & Calibration via Neural Model Approximations—0
Improving Chinese Grammatical Error Detection via Data augmentation by Conditional Error Generation—0
Syntax-guided Contrastive Learning for Pre-trained Language Model—0
A Warm Start and a Clean Crawled Corpus -- A Recipe for Good Language Models—0
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