SOTAVerified

Grammatical Error Correction

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.|

Papers

Showing 201250 of 415 papers

TitleStatusHype
Phrase Structure Annotation and Parsing for Learner English0
Position Offset Label Prediction for Grammatical Error Correction0
POSTECH Grammatical Error Correction System in the CoNLL-2014 Shared Task0
Predicting Compact Phrasal Rewrites with Large Language Models for ASR Post Editing0
Proficiency Matters Quality Estimation in Grammatical Error Correction0
Prompting open-source and commercial language models for grammatical error correction of English learner text0
Proofread Sentence Generation as Multi-Task Learning with Editing Operation Prediction0
ProQE: Proficiency-wise Quality Estimation dataset for Grammatical Error Correction0
Pseudo-Bidirectional Decoding for Local Sequence Transduction0
Pseudo-Error Generation for Grammatical Error Correction Based on Learner’s First Language0
RACAI GEC -- A hybrid approach to Grammatical Error Correction0
Reducing Sequence Length by Predicting Edit Operations with Large Language Models0
Reference-based Metrics can be Replaced with Reference-less Metrics in Evaluating Grammatical Error Correction Systems0
Rethinking the Roles of Large Language Models in Chinese Grammatical Error Correction0
Robust and Effective Grammatical Error Correction with Simple Cycle Self-Augmenting0
Robust Systems for Preposition Error Correction Using Wikipedia Revisions0
Scaling and Prompting for Improved End-to-End Spoken Grammatical Error Correction0
Semantic Parsing for English as a Second Language0
Semi-automatically Annotated Learner Corpus for Russian0
Sentential Paraphrasing as Black-Box Machine Translation0
Sequence-to-Action: Grammatical Error Correction with Action Guided Sequence Generation0
Sequence-to-sequence Pre-training with Data Augmentation for Sentence Rewriting0
Speak & Improve Challenge 2025: Tasks and Baseline Systems0
Speak & Improve Corpus 2025: an L2 English Speech Corpus for Language Assessment and Feedback0
Spivavtor: An Instruction Tuned Ukrainian Text Editing Model0
Stronger Baselines for Grammatical Error Correction Using a Pretrained Encoder-Decoder Model0
Synthetic Alone: Exploring the Dark Side of Synthetic Data for Grammatical Error Correction0
System Combination for Grammatical Error Correction0
Tense and Aspect Error Correction for ESL Learners Using Global Context0
Text Generation with Text-Editing Models0
The AIP-Tohoku System at the BEA-2019 Shared Task0
The AMU System in the CoNLL-2014 Shared Task: Grammatical Error Correction by Data-Intensive and Feature-Rich Statistical Machine Translation0
The BEA-2019 Shared Task on Grammatical Error Correction0
The BLCU System in the BEA 2019 Shared Task0
The Columbia System in the QALB-2014 Shared Task on Arabic Error Correction0
The CoNLL-2013 Shared Task on Grammatical Error Correction0
The CUED's Grammatical Error Correction Systems for BEA-20190
The Effect of Error Rate in Artificially Generated Data for Automatic Preposition and Determiner Correction0
The Effect of Learner Corpus Size in Grammatical Error Correction of ESL Writings0
The Illinois-Columbia System in the CoNLL-2014 Shared Task0
The LAIX Systems in the BEA-2019 GEC Shared Task0
The Unbearable Weight of Generating Artificial Errors for Grammatical Error Correction0
The Write & Improve Corpus 2024: Error-annotated and CEFR-labelled essays by learners of English0
Tibyan Corpus: Balanced and Comprehensive Error Coverage Corpus Using ChatGPT for Arabic Grammatical Error Correction0
TMU-NLP System Using BERT-based Pre-trained Model to the NLP-TEA CGED Shared Task 20200
TMU Transformer System Using BERT for Re-ranking at BEA 2019 Grammatical Error Correction on Restricted Track0
Toward More Precision in Correction of Grammatical Errors0
Towards End-to-End Spoken Grammatical Error Correction0
Towards Minimal Supervision BERT-based Grammar Error Correction0
Towards Universal Dependencies for Learner Chinese0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Ensembles of best 7 models + GRECO + GTP-rerankF0.572.8Unverified
2Majority-voting ensemble on best 7 modelsF0.571.8Unverified
3GRECO (voting+ESC)F0.571.12Unverified
4GEC-DI (LM+GED)F0.569.6Unverified
5Unsupervised GEC + cLang8F0.569.6Unverified
6ESCF0.569.51Unverified
7T5F0.568.87Unverified
8MoECEF0.567.79Unverified
9SynGECF0.567.6Unverified
10Sequence tagging + token-level transformations + two-stage fine-tuning (+BERT, RoBERTa, XLNet)F0.566.5Unverified
#ModelMetricClaimedVerifiedStatus
1Majority-voting ensemble on best 7 modelsF0.581.4Unverified
2GRECO (voting+ESC)F0.580.84Unverified
3ESCF0.579.9Unverified
4RedPenNetF0.577.6Unverified
5clang_large_ft2-gectorF0.577.1Unverified
6Unsupervised GEC + cLang8F0.576.5Unverified
7DeBERTa + RoBERTa + XLNetF0.576.05Unverified
8MoECEF0.574.07Unverified
9Sequence tagging + token-level transformations + two-stage fine-tuning (+RoBERTa, XLNet)F0.573.7Unverified
10BEA CombinationF0.573.2Unverified
#ModelMetricClaimedVerifiedStatus
1Llama + 1M BT + goldF0.576.75Unverified
2mT5-based multimodal MoEF0.576.3Unverified
3gT5 xxlF0.575.96Unverified
4TransformerF0.573.71Unverified
5Transformer - synthetic pretrain onlyF0.551.41Unverified
6Multilayer Convolutional Encoder-DecoderF0.543.35Unverified
#ModelMetricClaimedVerifiedStatus
1VERNetGLEU62.1Unverified
2Transformer + Pre-train with Pseudo Data + BERTGLEU62Unverified
3SMT + BiGRUGLEU61.5Unverified
4Copy-augmented Model (4 Ensemble +Denoising Autoencoder)GLEU61Unverified
5TransformerGLEU59.9Unverified
6CNN Seq2SeqGLEU57.47Unverified
#ModelMetricClaimedVerifiedStatus
1Llama + 1M BT + goldF0.574.09Unverified
2mBART-based model with synthetic dataF0.568.17Unverified
3mT5 large + 10M synthF0.568.09Unverified
4RedPenNetF0.567.71Unverified
5ChatGPT (zero-shot)F0.527.4Unverified
#ModelMetricClaimedVerifiedStatus
1GRECO (vote+ESC)F0.585.21Unverified
2SMT + BiGRUF0.572.04Unverified
3CNN Seq2SeqF0.570.14Unverified
#ModelMetricClaimedVerifiedStatus
1CNN Seq2Seq + Quality EstimationF0.556.52Unverified
2TransformerF0.555.8Unverified
3+ BIFI with no criticF0.518.7Unverified
#ModelMetricClaimedVerifiedStatus
1CNN Seq2Seq + Fluency Boost and inferenceGLEU62.37Unverified
2CNN Seq2Seq + Fluency BoostF0.561.34Unverified
3+ BIFI (ours)F0.542.4Unverified
#ModelMetricClaimedVerifiedStatus
1TransformerGLEU59.9Unverified
2CNN Seq2SeqGLEU57.47Unverified
#ModelMetricClaimedVerifiedStatus
1Llama + 1M BT + goldF0.569.97Unverified
#ModelMetricClaimedVerifiedStatus
1STG-Jointexact match34.1Unverified
#ModelMetricClaimedVerifiedStatus
1GEC-DI (LM+GED)F0.548.61Unverified
#ModelMetricClaimedVerifiedStatus
1RedPenNetF0.577.6Unverified