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

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Showing 201–250 of 415 papers

TitleStatusHype
New Dataset and Strong Baselines for the Grammatical Error Correction of Russian—0
Noisy Channel for Low Resource Grammatical Error Correction—0
Non-Autoregressive Grammatical Error Correction Toward a Writing Support System—0
Numerically Grounded Language Models for Semantic Error Correction—0
NUS at the HOO 2012 Shared Task—0
On Assessing and Developing Spoken ’Grammatical Error Correction’ Systems—0
On the application of Large Language Models for language teaching and assessment technology—0
On the (In)Effectiveness of Large Language Models for Chinese Text Correction—0
Overview of NLP-TEA 2016 Shared Task for Chinese Grammatical Error Diagnosis—0
Overview of NLPTEA-2018 Share Task Chinese Grammatical Error Diagnosis—0
Overview of the NLP-TEA 2015 Shared Task for Chinese Grammatical Error Diagnosis—0
Personalizing Grammatical Error Correction: Adaptation to Proficiency Level and L1—0
Phrase-based Machine Translation is State-of-the-Art for Automatic Grammatical Error Correction—0
Phrase Structure Annotation and Parsing for Learner English—0
Position Offset Label Prediction for Grammatical Error Correction—0
POSTECH Grammatical Error Correction System in the CoNLL-2014 Shared Task—0
Predicting Compact Phrasal Rewrites with Large Language Models for ASR Post Editing—0
Proficiency Matters Quality Estimation in Grammatical Error Correction—0
Prompting open-source and commercial language models for grammatical error correction of English learner text—0
Proofread Sentence Generation as Multi-Task Learning with Editing Operation Prediction—0
ProQE: Proficiency-wise Quality Estimation dataset for Grammatical Error Correction—0
Pseudo-Bidirectional Decoding for Local Sequence Transduction—0
Pseudo-Error Generation for Grammatical Error Correction Based on Learner’s First Language—0
RACAI GEC -- A hybrid approach to Grammatical Error Correction—0
Reducing Sequence Length by Predicting Edit Operations with Large Language Models—0
Reference-based Metrics can be Replaced with Reference-less Metrics in Evaluating Grammatical Error Correction Systems—0
Rethinking the Roles of Large Language Models in Chinese Grammatical Error Correction—0
Robust and Effective Grammatical Error Correction with Simple Cycle Self-Augmenting—0
Robust Systems for Preposition Error Correction Using Wikipedia Revisions—0
Scaling and Prompting for Improved End-to-End Spoken Grammatical Error Correction—0
Semantic Parsing for English as a Second Language—0
Semi-automatically Annotated Learner Corpus for Russian—0
Sentential Paraphrasing as Black-Box Machine Translation—0
Sequence-to-Action: Grammatical Error Correction with Action Guided Sequence Generation—0
Sequence-to-sequence Pre-training with Data Augmentation for Sentence Rewriting—0
Speak & Improve Challenge 2025: Tasks and Baseline Systems—0
Speak & Improve Corpus 2025: an L2 English Speech Corpus for Language Assessment and Feedback—0
Spivavtor: An Instruction Tuned Ukrainian Text Editing Model—0
Stronger Baselines for Grammatical Error Correction Using a Pretrained Encoder-Decoder Model—0
Synthetic Alone: Exploring the Dark Side of Synthetic Data for Grammatical Error Correction—0
System Combination for Grammatical Error Correction—0
Tense and Aspect Error Correction for ESL Learners Using Global Context—0
Text Generation with Text-Editing Models—0
The AIP-Tohoku System at the BEA-2019 Shared Task—0
The AMU System in the CoNLL-2014 Shared Task: Grammatical Error Correction by Data-Intensive and Feature-Rich Statistical Machine Translation—0
The BEA-2019 Shared Task on Grammatical Error Correction—0
The BLCU System in the BEA 2019 Shared Task—0
The Columbia System in the QALB-2014 Shared Task on Arabic Error Correction—0
The CoNLL-2013 Shared Task on Grammatical Error Correction—0
The CUED's Grammatical Error Correction Systems for BEA-2019—0
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All datasetsCoNLL-2014 Shared TaskBEA-2019 (test)Falko-MERLINJFLEGUA-GECCoNLL-2014 Shared Task (10 annotations)RestrictedUnrestricted_Restricted_EstGEC-L2FCGECMuCGEC

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Ensembles of best 7 models + GRECO + GTP-rerankF0.572.8—Unverified
2Majority-voting ensemble on best 7 modelsF0.571.8—Unverified
3GRECO (voting+ESC)F0.571.12—Unverified
4GEC-DI (LM+GED)F0.569.6—Unverified
5Unsupervised GEC + cLang8F0.569.6—Unverified
6ESCF0.569.51—Unverified
7T5F0.568.87—Unverified
8MoECEF0.567.79—Unverified
9SynGECF0.567.6—Unverified
10Sequence tagging + token-level transformations + two-stage fine-tuning (+BERT, RoBERTa, XLNet)F0.566.5—Unverified
#ModelMetricClaimedVerifiedStatus
1Majority-voting ensemble on best 7 modelsF0.581.4—Unverified
2GRECO (voting+ESC)F0.580.84—Unverified
3ESCF0.579.9—Unverified
4RedPenNetF0.577.6—Unverified
5clang_large_ft2-gectorF0.577.1—Unverified
6Unsupervised GEC + cLang8F0.576.5—Unverified
7DeBERTa + RoBERTa + XLNetF0.576.05—Unverified
8MoECEF0.574.07—Unverified
9Sequence tagging + token-level transformations + two-stage fine-tuning (+RoBERTa, XLNet)F0.573.7—Unverified
10BEA CombinationF0.573.2—Unverified
#ModelMetricClaimedVerifiedStatus
1Llama + 1M BT + goldF0.576.75—Unverified
2mT5-based multimodal MoEF0.576.3—Unverified
3gT5 xxlF0.575.96—Unverified
4TransformerF0.573.71—Unverified
5Transformer - synthetic pretrain onlyF0.551.41—Unverified
6Multilayer Convolutional Encoder-DecoderF0.543.35—Unverified
#ModelMetricClaimedVerifiedStatus
1VERNetGLEU62.1—Unverified
2Transformer + Pre-train with Pseudo Data + BERTGLEU62—Unverified
3SMT + BiGRUGLEU61.5—Unverified
4Copy-augmented Model (4 Ensemble +Denoising Autoencoder)GLEU61—Unverified
5TransformerGLEU59.9—Unverified
6CNN Seq2SeqGLEU57.47—Unverified
#ModelMetricClaimedVerifiedStatus
1Llama + 1M BT + goldF0.574.09—Unverified
2mBART-based model with synthetic dataF0.568.17—Unverified
3mT5 large + 10M synthF0.568.09—Unverified
4RedPenNetF0.567.71—Unverified
5ChatGPT (zero-shot)F0.527.4—Unverified
#ModelMetricClaimedVerifiedStatus
1GRECO (vote+ESC)F0.585.21—Unverified
2SMT + BiGRUF0.572.04—Unverified
3CNN Seq2SeqF0.570.14—Unverified
#ModelMetricClaimedVerifiedStatus
1CNN Seq2Seq + Quality EstimationF0.556.52—Unverified
2TransformerF0.555.8—Unverified
3+ BIFI with no criticF0.518.7—Unverified
#ModelMetricClaimedVerifiedStatus
1CNN Seq2Seq + Fluency Boost and inferenceGLEU62.37—Unverified
2CNN Seq2Seq + Fluency BoostF0.561.34—Unverified
3+ BIFI (ours)F0.542.4—Unverified
#ModelMetricClaimedVerifiedStatus
1TransformerGLEU59.9—Unverified
2CNN Seq2SeqGLEU57.47—Unverified
#ModelMetricClaimedVerifiedStatus
1Llama + 1M BT + goldF0.569.97—Unverified
#ModelMetricClaimedVerifiedStatus
1STG-Jointexact match34.1—Unverified
#ModelMetricClaimedVerifiedStatus
1GEC-DI (LM+GED)F0.548.61—Unverified
#ModelMetricClaimedVerifiedStatus
1RedPenNetF0.577.6—Unverified