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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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TitleStatusHype
Neural Quality Estimation with Multiple Hypotheses for Grammatical Error CorrectionCode1
GECTurk: Grammatical Error Correction and Detection Dataset for TurkishCode1
FCGEC: Fine-Grained Corpus for Chinese Grammatical Error CorrectionCode1
Advancements in Arabic Grammatical Error Detection and Correction: An Empirical InvestigationCode1
Jointly Learning to Label Sentences and TokensCode1
Context is Key: Grammatical Error Detection with Contextual Word RepresentationsCode1
GECTurk WEB: An Explainable Online Platform for Turkish Grammatical Error Detection and CorrectionCode1
Semi-supervised Multitask Learning for Sequence LabelingCode1
Bangla Grammatical Error Detection Using T5 Transformer ModelCode0
Towards a standard evaluation method for grammatical error detection and correctionCode0
Detecting Local Insights from Global Labels: Supervised & Zero-Shot Sequence Labeling via a Convolutional DecompositionCode0
Wronging a Right: Generating Better Errors to Improve Grammatical Error DetectionCode0
The Tembusu Treebank: An English Learner TreebankCode0
Probing for targeted syntactic knowledge through grammatical error detectionCode0
Grammatical Error Detection Using Error- and Grammaticality-Specific Word EmbeddingsCode0
Multi-Class Grammatical Error Detection for Correction: A Tale of Two SystemsCode0
Evaluation of really good grammatical error correctionCode0
Enhancing Grammatical Error Detection using BERT with Cleaned Lang-8 DatasetCode0
Sequence Classification with Human AttentionCode0
Automated Writing Support Using Deep Linguistic Parsers—0
Automated Evaluation of Scientific Writing: AESW Shared Task Proposal—0
A Report on the Automatic Evaluation of Scientific Writing Shared Task—0
Attending to Characters in Neural Sequence Labeling Models—0
Building a TOCFL Learner Corpus for Chinese Grammatical Error Diagnosis—0
Book Reviews: Automated Grammatical Error Detection for Language Learners, Second Edition by Claudia Leacock, Martin Chodorow, Michael Gamon and Joel Tetreault—0
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