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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
Grammatical error detection in transcriptions of spoken English—0
Grammatical Error Detection Using Tagger Disagreement—0
IMPARA-GED: Grammatical Error Detection is Boosting Reference-free Grammatical Error Quality Estimator—0
Improving Chinese Grammatical Error Detection via Data augmentation by Conditional Error Generation—0
Joint English Spelling Error Correction and POS Tagging for Language Learners Writing—0
Language Family Relationship Preserved in Non-native English—0
LinggleWrite: a Coaching System for Essay Writing—0
Model Combination for Correcting Preposition Selection Errors—0
Multi-Head Multi-Layer Attention to Deep Language Representations for Grammatical Error Detection—0
NAIST at the HOO 2012 Shared Task—0
NAIST at the NLI 2013 Shared Task—0
Neural Models for Predicting Celtic Mutations—0
Neural Multi-task Learning in Automated Assessment—0
Neural sequence modelling for learner error prediction—0
Oddballness: universal anomaly detection with language models—0
Overview of the NLP-TEA 2015 Shared Task for Chinese Grammatical Error Diagnosis—0
Precision Isn't Everything: A Hybrid Approach to Grammatical Error Detection—0
Problems in Evaluating Grammatical Error Detection Systems—0
RACAI GEC -- A hybrid approach to Grammatical Error Correction—0
Semi-automatically Annotated Learner Corpus for Russian—0
Shallow Semantic Analysis of Interactive Learner Sentences—0
Shallow Semantic Reasoning from an Incomplete Gold Standard for Learner Language—0
Syntax-guided Contrastive Learning for Pre-trained Language Model—0
The AIP-Tohoku System at the BEA-2019 Shared Task—0
The Construction of a Chinese Collocational Knowledge Resource and Its Application for Second Language Acquisition—0
The NTNU-YZU System in the AESW Shared Task: Automated Evaluation of Scientific Writing Using a Convolutional Neural Network—0
UM-Checker: A Hybrid System for English Grammatical Error Correction—0
UW-Stanford System Description for AESW 2016 Shared Task on Grammatical Error Detection—0
Word Order Sensitive Embedding Features/Conditional Random Field-based Chinese Grammatical Error Detection—0
WriteAhead2: Mining Lexical Grammar Patterns for Assisted Writing—0
Addressing Class Imbalance in Grammatical Error Detection with Evaluation Metric Optimization—0
Zero-shot Cross-Lingual Transfer for Synthetic Data Generation in Grammatical Error Detection—0
A Grammar Sparrer for Norwegian—0
A Hybrid Approach Combining Statistical Knowledge with Conditional Random Fields for Chinese Grammatical Error Detection—0
A Light Rule-based Approach to English Subject-Verb Agreement Errors on the Third Person Singular Forms—0
Approximate Conditional Coverage & Calibration via Neural Model Approximations—0
A Report on the Automatic Evaluation of Scientific Writing Shared Task—0
Artificial Error Generation with Machine Translation and Syntactic Patterns—0
ARWI: Arabic Write and Improve—0
A Sentence Judgment System for Grammatical Error Detection—0
Assessing Grammatical Correctness in Language Learning—0
A Tree Transducer Model for Grammatical Error Correction—0
Attending to Characters in Neural Sequence Labeling Models—0
Automated Evaluation of Scientific Writing: AESW Shared Task Proposal—0
Automated Writing Support Using Deep Linguistic Parsers—0
Automatic Grammatical Error Detection for Chinese based on Conditional Random Field—0
Automatic morphological analysis of learner Hungarian—0
Auxiliary Objectives for Neural Error Detection Models—0
A Warm Start and a Clean Crawled Corpus -- A Recipe for Good Language Models—0
A Warm Start and a Clean Crawled Corpus - A Recipe for Good Language Models—0
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