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 251–300 of 415 papers

TitleStatusHype
The Effect of Error Rate in Artificially Generated Data for Automatic Preposition and Determiner Correction—0
The Effect of Learner Corpus Size in Grammatical Error Correction of ESL Writings—0
The Illinois-Columbia System in the CoNLL-2014 Shared Task—0
The LAIX Systems in the BEA-2019 GEC Shared Task—0
The Unbearable Weight of Generating Artificial Errors for Grammatical Error Correction—0
The Write & Improve Corpus 2024: Error-annotated and CEFR-labelled essays by learners of English—0
Tibyan Corpus: Balanced and Comprehensive Error Coverage Corpus Using ChatGPT for Arabic Grammatical Error Correction—0
TMU-NLP System Using BERT-based Pre-trained Model to the NLP-TEA CGED Shared Task 2020—0
TMU Transformer System Using BERT for Re-ranking at BEA 2019 Grammatical Error Correction on Restricted Track—0
Toward More Precision in Correction of Grammatical Errors—0
Towards End-to-End Spoken Grammatical Error Correction—0
Towards Minimal Supervision BERT-based Grammar Error Correction—0
Towards Universal Dependencies for Learner Chinese—0
Towards Unsupervised Grammatical Error Correction using Statistical Machine Translation with Synthetic Comparable Corpus—0
Treelet Probabilities for HPSG Parsing and Error Correction—0
Tuning a Grammar Correction System for Increased Precision—0
UdS at CoNLL 2013 Shared Task—0
UM-Checker: A Hybrid System for English Grammatical Error Correction—0
Uncertainty Determines the Adequacy of the Mode and the Tractability of Decoding in Sequence-to-Sequence Models—0
Ungrammatical-syntax-based In-context Example Selection for Grammatical Error Correction—0
Using Context in Neural Machine Translation Training Objectives—0
UW-Stanford System Description for AESW 2016 Shared Task on Grammatical Error Detection—0
Weakly Supervised Grammatical Error Correction using Iterative Decoding—0
LFG-based Features for Noun Number and Article Grammatical Errors—0
A BERT-based Unsupervised Grammatical Error Correction Framework—0
A Chinese Writing Correction System for Learning Chinese as a Foreign Language—0
A Comparative Study of Synthetic Data Generation Methods for Grammatical Error Correction—0
A Crash Course in Automatic Grammatical Error Correction—0
Adapting Grammatical Error Correction Based on the Native Language of Writers with Neural Network Joint Models—0
Addressing Class Imbalance in Grammatical Error Detection with Evaluation Metric Optimization—0
Adversarial Grammatical Error Correction—0
A Hybrid Model For Grammatical Error Correction—0
A Hybrid System for Chinese Grammatical Error Diagnosis and Correction—0
A Language Model for Grammatical Error Correction in L2 Russian—0
(Almost) Unsupervised Grammatical Error Correction using Synthetic Comparable Corpus—0
A Meta Learning Approach to Grammatical Error Correction—0
Analyzing the Impact of Spelling Errors on POS-Tagging and Chunking in Learner English—0
Analyzing the Performance of GPT-3.5 and GPT-4 in Grammatical Error Correction—0
An Annotated Corpus of Picture Stories Retold by Language Learners—0
An Automatic Error Tagger for German—0
A Nested Attention Neural Hybrid Model for Grammatical Error Correction—0
A New Evaluation Method: Evaluation Data and Metrics for Chinese Grammar Error Correction—0
A Pipeline Approach to Supervised Error Correction for the QALB-2014 Shared Task—0
A POS Tagging Model Adapted to Learner English—0
A Report on the 2017 Native Language Identification Shared Task—0
A Report on the Automatic Evaluation of Scientific Writing Shared Task—0
Artificial Error Generation with Fluency Filtering—0
A Self-Refinement Strategy for Noise Reduction in Grammatical Error Correction—0
A Simple but Effective Classification Model for Grammatical Error Correction—0
A Syntax-Guided Grammatical Error Correction Model with Dependency Tree Correction—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