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Grammatical Error Detection
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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.
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Detecting Spelling and Grammatical Anomalies in Russian Poetry Texts
ARWI: Arabic Write and Improve
Enhancing Grammatical Error Detection using BERT with Cleaned Lang-8 Dataset
Bangla Grammatical Error Detection Leveraging Transformer-based Token Classification
GECTurk WEB: An Explainable Online Platform for Turkish Grammatical Error Detection and Correction
Oddballness: universal anomaly detection with language models
Zero-shot Cross-Lingual Transfer for Synthetic Data Generation in Grammatical Error Detection
GECTurk: Grammatical Error Correction and Detection Dataset for Turkish
Evaluation of really good grammatical error correction
Advancements in Arabic Grammatical Error Detection and Correction: An Empirical Investigation
Bangla Grammatical Error Detection Using T5 Transformer Model
Probing for targeted syntactic knowledge through grammatical error detection
FCGEC: Fine-Grained Corpus for Chinese Grammatical Error Correction
Approximate Conditional Coverage & Calibration via Neural Model Approximations
A Warm Start and a Clean Crawled Corpus -- A Recipe for Good Language Models
Exploring the Capacity of a Large-scale Masked Language Model to Recognize Grammatical Errors
Combining GCN and Transformer for Chinese Grammatical Error Detection
Neural Quality Estimation with Multiple Hypotheses for Grammatical Error Correction
Context is Key: Grammatical Error Detection with Contextual Word Representations
Detecting Local Insights from Global Labels: Supervised & Zero-Shot Sequence Labeling via a Convolutional Decomposition
Multi-Head Multi-Layer Attention to Deep Language Representations for Grammatical Error Detection
Jointly Learning to Label Sentences and Tokens
Wronging a Right: Generating Better Errors to Improve Grammatical Error Detection
Neural Multi-task Learning in Automated Assessment
Auxiliary Objectives for Neural Error Detection Models
Artificial Error Generation with Machine Translation and Syntactic Patterns
Semi-supervised Multitask Learning for Sequence Labeling
Exploiting Unlabeled Data for Neural Grammatical Error Detection
Attending to Characters in Neural Sequence Labeling Models
Compositional Sequence Labeling Models for Error Detection in Learner Writing
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