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Grammatical Error Correction
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Grammatical Error Correction (GEC) is the task of correcting different kinds of errors in text such as spelling, punctuation, grammatical, and word choice errors.
GEC is typically formulated as a sentence correction task. A GEC system takes a potentially erroneous sentence as input and is expected to transform it to its corrected version. See the example given below:
| Input (Erroneous) | Output (Corrected) |
|---|---|
| She see Tom is catched by policeman in park at last night. | She saw Tom caught by a policeman in the park last night. |
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
IMPARA-GED: Grammatical Error Detection is Boosting Reference-free Grammatical Error Quality Estimator
Scaling and Prompting for Improved End-to-End Spoken Grammatical Error Correction
gec-metrics: A Unified Library for Grammatical Error Correction Evaluation
Exploring the Feasibility of Multilingual Grammatical Error Correction with a Single LLM up to 9B parameters: A Comparative Study of 17 Models
Detecting Spelling and Grammatical Anomalies in Russian Poetry Texts
Enriching the Korean Learner Corpus with Multi-reference Annotations and Rubric-Based Scoring
ARWI: Arabic Write and Improve
Deep Learning Model Deployment in Multiple Cloud Providers: an Exploratory Study Using Low Computing Power Environments
Enhancing Text Editing for Grammatical Error Correction: Arabic as a Case Study
Corrections Meet Explanations: A Unified Framework for Explainable Grammatical Error Correction
Rethinking Evaluation Metrics for Grammatical Error Correction: Why Use a Different Evaluation Process than Human?
Explanation based In-Context Demonstrations Retrieval for Multilingual Grammatical Error Correction
Predicting Compact Phrasal Rewrites with Large Language Models for ASR Post Editing
Loss-Aware Curriculum Learning for Chinese Grammatical Error Correction
LLMCL-GEC: Advancing Grammatical Error Correction with LLM-Driven Curriculum Learning
DSGram: Dynamic Weighting Sub-Metrics for Grammatical Error Correction in the Era of Large Language Models
Improving Explainability of Sentence-level Metrics via Edit-level Attribution for Grammatical Error Correction
Speak & Improve Challenge 2025: Tasks and Baseline Systems
Speak & Improve Corpus 2025: an L2 English Speech Corpus for Language Assessment and Feedback
Enhancing Grammatical Error Detection using BERT with Cleaned Lang-8 Dataset
Bangla Grammatical Error Detection Leveraging Transformer-based Token Classification
Tibyan Corpus: Balanced and Comprehensive Error Coverage Corpus Using ChatGPT for Arabic Grammatical Error Correction
Efficient and Interpretable Grammatical Error Correction with Mixture of Experts
A Simple Yet Effective Corpus Construction Framework for Indonesian Grammatical Error Correction
Multi-head Sequence Tagging Model for Grammatical Error Correction
Grammatical Error Correction for Low-Resource Languages: The Case of Zarma
GECTurk WEB: An Explainable Online Platform for Turkish Grammatical Error Detection and Correction
LLM-based Code-Switched Text Generation for Grammatical Error Correction
Oddballness: universal anomaly detection with language models
Grammatical Error Feedback: An Implicit Evaluation Approach
Zero-shot Cross-Lingual Transfer for Synthetic Data Generation in Grammatical Error Detection
EXCGEC: A Benchmark of Edit-wise Explainable Chinese Grammatical Error Correction
CLEME2.0: Towards More Interpretable Evaluation by Disentangling Edits for Grammatical Error Correction
Improving Grammatical Error Correction via Contextual Data Augmentation
ChatLang-8: An LLM-Based Synthetic Data Generation Framework for Grammatical Error Correction
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