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 51–100 of 415 papers

TitleStatusHype
Beyond English: Evaluating LLMs for Arabic Grammatical Error Correction—0
Unsupervised Grammatical Error Correction Rivaling Supervised MethodsCode0
Grammatical Error Correction via Mixed-Grained Weighted Training—0
Efficient Grammatical Error Correction Via Multi-Task Training and Optimized Training Schedule—0
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 Correction—0
TLM: Token-Level Masking for TransformersCode0
System Combination via Quality Estimation for Grammatical Error CorrectionCode1
Improving Seq2Seq Grammatical Error Correction via Decoding InterventionsCode1
Evaluation Metrics in the Era of GPT-4: Reliably Evaluating Large Language Models on Sequence to Sequence TasksCode0
Beyond Hard Samples: Robust and Effective Grammatical Error Correction with Cycle Self-AugmentingCode0
MixEdit: Revisiting Data Augmentation and Beyond for Grammatical Error CorrectionCode1
RobustGEC: Robust Grammatical Error Correction Against Subtle Context PerturbationCode1
FlaCGEC: A Chinese Grammatical Error Correction Dataset with Fine-grained Linguistic AnnotationCode1
GECTurk: Grammatical Error Correction and Detection Dataset for TurkishCode1
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 Correction—0
Minimum Bayes' Risk Decoding for System Combination of Grammatical Error Correction SystemsCode0
Evaluation of really good grammatical error correctionCode0
ChatGPT for Arabic Grammatical Error Correction—0
GrammarGPT: Exploring Open-Source LLMs for Native Chinese Grammatical Error Correction with Supervised Fine-TuningCode1
On the (In)Effectiveness of Large Language Models for Chinese Text Correction—0
On the application of Large Language Models for language teaching and assessment technology—0
Evaluating the Capability of Large-scale Language Models on Chinese Grammatical Error Correction Task—0
Leveraging Denoised Abstract Meaning Representation for Grammatical Error Correction—0
A Language Model for Grammatical Error Correction in L2 Russian—0
Evaluating GPT-3.5 and GPT-4 on Grammatical Error Correction for Brazilian Portuguese—0
Synthetic Alone: Exploring the Dark Side of Synthetic Data for Grammatical Error Correction—0
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 Methods—0
NaSGEC: a Multi-Domain Chinese Grammatical Error Correction Dataset from Native Speaker TextsCode1
Enhancing Grammatical Error Correction Systems with ExplanationsCode1
Advancements in Arabic Grammatical Error Detection and Correction: An Empirical InvestigationCode1
Are Pre-trained Language Models Useful for Model Ensemble in Chinese Grammatical Error Correction?Code1
IdEALS: Idiomatic Expressions for Advancement of Language Skills—0
Bidirectional Transformer Reranker for Grammatical Error CorrectionCode0
Reducing Sequence Length by Predicting Edit Operations with Large Language Models—0
CLEME: Debiasing Multi-reference Evaluation for Grammatical Error CorrectionCode1
CoEdIT: Text Editing by Task-Specific Instruction TuningCode1
A Low-Resource Approach to the Grammatical Error Correction of UkrainianCode0
Is ChatGPT a Highly Fluent Grammatical Error Correction System? A Comprehensive Evaluation—0
A BERT-based Unsupervised Grammatical Error Correction Framework—0
Analyzing the Performance of GPT-3.5 and GPT-4 in Grammatical Error Correction—0
ChatGPT or Grammarly? Evaluating ChatGPT on Grammatical Error Correction Benchmark—0
An Extended Sequence Tagging Vocabulary for Grammatical Error CorrectionCode4
Improving Iterative Text Revision by Learning Where to Edit from Other Revision TasksCode1
CSynGEC: Incorporating Constituent-based Syntax for Grammatical Error Correction with a Tailored GEC-Oriented Parser—0
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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