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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
Chinese Grammatical Error Detection Based on BERT Model—0
Chinese Grammatical Error Diagnosis Using Ensemble Learning—0
Chinese Grammatical Error Diagnosis Using Single Word Embedding—0
Chinese Grammatical Errors Diagnosis System Based on BERT at NLPTEA-2020 CGED Shared Task—0
Collecting fluency corrections for spoken learner English—0
Combining GCN and Transformer for Chinese Grammatical Error Detection—0
Compositional Sequence Labeling Models for Error Detection in Learner Writing—0
Condition Random Fields-based Grammatical Error Detection for Chinese as Second Language—0
Correcting Comma Errors in Learner Essays, and Restoring Commas in Newswire Text—0
Data Driven Grammatical Error Detection in Transcripts of Children's Speech—0
Detecting and Correcting Learner Korean Particle Omission Errors—0
Detecting English Grammatical Errors based on Machine Translation—0
Detecting Spelling and Grammatical Anomalies in Russian Poetry Texts—0
Detecting Word Usage Errors in Chinese Sentences for Learning Chinese as a Foreign Language—0
Detection of Chinese Word Usage Errors for Non-Native Chinese Learners with Bidirectional LSTM—0
Exploiting Unlabeled Data for Neural Grammatical Error Detection—0
Exploring Grammatical Error Correction with Not-So-Crummy Machine Translation—0
Exploring the Capacity of a Large-scale Masked Language Model to Recognize Grammatical Errors—0
Factored Statistical Machine Translation for Grammatical Error Correction—0
Generation of a Spanish Artificial Collocation Error Corpus—0
Grammatical Error Annotation for Korean Learners of Spoken English—0
Grammatical-Error-Aware Incorrect Example Retrieval System for Learners of Japanese as a Second Language—0
Grammatical Error Correction as Multiclass Classification with Single Model—0
Grammatical Error Detection and Correction using a Single Maximum Entropy Model—0
Grammatical Error Detection Based on Machine Learning for Mandarin as Second Language Learning—0
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