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 101150 of 415 papers

TitleStatusHype
GEE! Grammar Error Explanation with Large Language ModelsCode0
GEC-DePenD: Non-Autoregressive Grammatical Error Correction with Decoupled Permutation and DecodingCode0
Towards End-to-End Spoken Grammatical Error Correction0
TLM: Token-Level Masking for TransformersCode0
Beyond Hard Samples: Robust and Effective Grammatical Error Correction with Cycle Self-AugmentingCode0
Evaluation Metrics in the Era of GPT-4: Reliably Evaluating Large Language Models on Sequence to Sequence TasksCode0
Controlled Generation with Prompt Insertion for Natural Language Explanations in Grammatical Error CorrectionCode0
RedPenNet for Grammatical Error Correction: Outputs to Tokens, Attentions to SpansCode0
HTEC: Human Transcription Error Correction0
Minimum Bayes' Risk Decoding for System Combination of Grammatical Error Correction SystemsCode0
Evaluation of really good grammatical error correctionCode0
ChatGPT for Arabic Grammatical Error Correction0
On the (In)Effectiveness of Large Language Models for Chinese Text Correction0
On the application of Large Language Models for language teaching and assessment technology0
Evaluating the Capability of Large-scale Language Models on Chinese Grammatical Error Correction Task0
Leveraging Denoised Abstract Meaning Representation for Grammatical Error Correction0
A Language Model for Grammatical Error Correction in L2 Russian0
Evaluating GPT-3.5 and GPT-4 on Grammatical Error Correction for Brazilian Portuguese0
Synthetic Alone: Exploring the Dark Side of Synthetic Data for Grammatical Error Correction0
Gender-Inclusive Grammatical Error Correction through AugmentationCode0
Byte-Level Grammatical Error Correction Using Synthetic and Curated CorporaCode0
Exploring Effectiveness of GPT-3 in Grammatical Error Correction: A Study on Performance and Controllability in Prompt-Based Methods0
IdEALS: Idiomatic Expressions for Advancement of Language Skills0
Bidirectional Transformer Reranker for Grammatical Error CorrectionCode0
Reducing Sequence Length by Predicting Edit Operations with Large Language Models0
A Low-Resource Approach to the Grammatical Error Correction of UkrainianCode0
Is ChatGPT a Highly Fluent Grammatical Error Correction System? A Comprehensive Evaluation0
A BERT-based Unsupervised Grammatical Error Correction Framework0
Analyzing the Performance of GPT-3.5 and GPT-4 in Grammatical Error Correction0
ChatGPT or Grammarly? Evaluating ChatGPT on Grammatical Error Correction Benchmark0
CSynGEC: Incorporating Constituent-based Syntax for Grammatical Error Correction with a Tailored GEC-Oriented Parser0
Grammatical Error Correction: A Survey of the State of the Art0
From Spelling to Grammar: A New Framework for Chinese Grammatical Error Correction0
Focus Is What You Need For Chinese Grammatical Error Correction0
Text Editing as Imitation GameCode0
IMPARA: Impact-Based Metric for GEC Using Parallel DataCode0
Grammatical Error Correction: Are We There Yet?0
Multi-Perspective Document Revision0
Position Offset Label Prediction for Grammatical Error Correction0
Dynamic Negative Example Construction for Grammatical Error Correction using Contrastive Learning0
Judge a Sentence by Its Content to Generate Grammatical Errors0
Gender Bias and Universal Substitution Adversarial Attacks on Grammatical Error Correction Systems for Automated Assessment0
On Assessing and Developing Spoken ’Grammatical Error Correction’ Systems0
Text Generation with Text-Editing Models0
Developing a Spell and Grammar Checker for Icelandic using an Error Corpus0
ProQE: Proficiency-wise Quality Estimation dataset for Grammatical Error Correction0
Semi-automatically Annotated Learner Corpus for Russian0
Automatic Classification of Russian Learner Errors0
MTee: Open Machine Translation Platform for Estonian Government0
Improving Grammatical Error Correction for Multiword Expressions0
Show:102550
← PrevPage 3 of 9Next →

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