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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 1–50 of 100 papers

TitleStatusHype
FCGEC: Fine-Grained Corpus for Chinese Grammatical Error CorrectionCode1
GECTurk: Grammatical Error Correction and Detection Dataset for TurkishCode1
Semi-supervised Multitask Learning for Sequence LabelingCode1
GECTurk WEB: An Explainable Online Platform for Turkish Grammatical Error Detection and CorrectionCode1
Advancements in Arabic Grammatical Error Detection and Correction: An Empirical InvestigationCode1
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
Probing for targeted syntactic knowledge through grammatical error detectionCode0
Wronging a Right: Generating Better Errors to Improve Grammatical Error DetectionCode0
The Tembusu Treebank: An English Learner TreebankCode0
Detecting Local Insights from Global Labels: Supervised & Zero-Shot Sequence Labeling via a Convolutional DecompositionCode0
Towards a standard evaluation method for grammatical error detection and correctionCode0
Multi-Class Grammatical Error Detection for Correction: A Tale of Two SystemsCode0
Sequence Classification with Human AttentionCode0
Grammatical Error Detection Using Error- and Grammaticality-Specific Word EmbeddingsCode0
Bangla Grammatical Error Detection Using T5 Transformer ModelCode0
Evaluation of really good grammatical error correctionCode0
Enhancing Grammatical Error Detection using BERT with Cleaned Lang-8 DatasetCode0
Bangla Grammatical Error Detection Leveraging Transformer-based Token Classification—0
Bi-LSTM Neural Networks 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
Building a TOCFL Learner Corpus for Chinese Grammatical Error Diagnosis—0
Can Natural Language Processing Become Natural Language Coaching?—0
Capturing Pragmatic Knowledge in Article Usage Prediction using LSTMs—0
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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