An unofficial Java port of ERRANT, the parallel text grammatical error annotator
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Updated
Jul 16, 2022 - Java
An unofficial Java port of ERRANT, the parallel text grammatical error annotator
artificial error generation using various rules for neural grammatical error correction
Source code and data for Project Athena
Code & Data for our Paper "RobustGEC: Robust Grammatical Error Correction Against Subtle Context Perturbation" (EMNLP 2023)
A Python package for working with GEC data in .m2 files
The official code for the "System Combination via Quality Estimation for Grammatical Error Correction" paper, published in EMNLP 2023.
Verificador Gramatical Sociolinguisticamente Orientado - um corretor gramatical amigável, como foco no usuário
Evaluation code and data for "Automatic Correction of Human Translations" [NAACL 2022].
The official code of ALLECS: A Lightweight Language Error Correction System
This is the official code for ``Beyond Hard Samples: Robust and Effective Grammatical Error Correction with Simple Cycle Self-Augmenting``
M.Sc. thesis project at University of Edinburgh, 2017.
Chinese Grammar Error and Spelling Error Correction System - 中文文法錯誤及錯別字校正系統
NMT model for grammatical error correction (GEC)
Scripts used for the preprocessing of the EstGEC-L2 corpus that contains Estonian L2 learner texts error-annotated in the M2 format.
Estonian Grammatical Error Correction (GEC) test and development corpus that contains L2 learner texts error-annotated in the M2 format.
Re-implementation of GLEU, evaluation metric of grammatical error correction
Grammatical-Error-Correction is an NLP-based spelling and grammar correction tool that accepts articles as well as raw text and returns a corrected sentence. Grammatical-Error-Correction is built using Python, powered by data and makes use of core NLP techniques. It is mainly based on AllenNLP and transformers.
AI based conversational language tutor
UNLP 2023 Shared Task on Grammatical Error Correction for Ukrainian
Code for the EACL 2024 paper: "Small Language Models Improve Giants by Rewriting Their Outputs"
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