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

Recently Added Most Hyped Most Active Needs Verification Most Verified

Showing 31–40 of 415 papers

Title	Date	Tasks	Status	Hype
ChatLang-8: An LLM-Based Synthetic Data Generation Framework for Grammatical Error Correction	Jun 5, 2024	Grammatical Error CorrectionSynthetic Data Generation	—Unverified	0
Detection-Correction Structure via General Language Model for Grammatical Error Correction	May 28, 2024	Grammatical Error CorrectionLanguage Modeling	CodeCode Available	1
Organic Data-Driven Approach for Turkish Grammatical Error Correction and LLMs	May 24, 2024	Grammatical Error Correction	CodeCode Available	0
GPT-3.5 for Grammatical Error Correction	May 14, 2024	Grammatical Error CorrectionSentence	—Unverified	0
Comparative study of models trained on synthetic data for Ukrainian grammatical error correction	May 5, 2024	Grammatical Error CorrectionMachine Translation	CodeCode Available	0
Spivavtor: An Instruction Tuned Ukrainian Text Editing Model	Apr 29, 2024	Grammatical Error Correctionmodel	—Unverified	0
Pillars of Grammatical Error Correction: Comprehensive Inspection Of Contemporary Approaches In The Era of Large Language Models	Apr 23, 2024	Grammatical Error Correction	CodeCode Available	1
Grammatical Error Correction for Code-Switched Sentences by Learners of English	Apr 18, 2024	Grammatical Error Correction	CodeCode Available	0
Ungrammatical-syntax-based In-context Example Selection for Grammatical Error Correction	Mar 28, 2024	Grammatical Error CorrectionIn-Context Learning	—Unverified	0
Large Language Models Are State-of-the-Art Evaluator for Grammatical Error Correction	Mar 26, 2024	Grammatical Error CorrectionMachine Translation	—Unverified	0

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All datasets CoNLL-2014 Shared Task BEA-2019 (test)Falko-MERLIN JFLEG UA-GEC CoNLL-2014 Shared Task (10 annotations)Restricted Unrestricted _Restricted_EstGEC-L2 FCGEC MuCGEC

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	Ensembles of best 7 models + GRECO + GTP-rerank	F0.5	72.8	—	Unverified
2	Majority-voting ensemble on best 7 models	F0.5	71.8	—	Unverified
3	GRECO (voting+ESC)	F0.5	71.12	—	Unverified
4	GEC-DI (LM+GED)	F0.5	69.6	—	Unverified
5	Unsupervised GEC + cLang8	F0.5	69.6	—	Unverified
6	ESC	F0.5	69.51	—	Unverified
7	T5	F0.5	68.87	—	Unverified
8	MoECE	F0.5	67.79	—	Unverified
9	SynGEC	F0.5	67.6	—	Unverified
10	Sequence tagging + token-level transformations + two-stage fine-tuning (+BERT, RoBERTa, XLNet)	F0.5	66.5	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Majority-voting ensemble on best 7 models	F0.5	81.4	—	Unverified
2	GRECO (voting+ESC)	F0.5	80.84	—	Unverified
3	ESC	F0.5	79.9	—	Unverified
4	RedPenNet	F0.5	77.6	—	Unverified
5	clang_large_ft2-gector	F0.5	77.1	—	Unverified
6	Unsupervised GEC + cLang8	F0.5	76.5	—	Unverified
7	DeBERTa + RoBERTa + XLNet	F0.5	76.05	—	Unverified
8	MoECE	F0.5	74.07	—	Unverified
9	Sequence tagging + token-level transformations + two-stage fine-tuning (+RoBERTa, XLNet)	F0.5	73.7	—	Unverified
10	BEA Combination	F0.5	73.2	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Llama + 1M BT + gold	F0.5	76.75	—	Unverified
2	mT5-based multimodal MoE	F0.5	76.3	—	Unverified
3	gT5 xxl	F0.5	75.96	—	Unverified
4	Transformer	F0.5	73.71	—	Unverified
5	Transformer - synthetic pretrain only	F0.5	51.41	—	Unverified
6	Multilayer Convolutional Encoder-Decoder	F0.5	43.35	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	VERNet	GLEU	62.1	—	Unverified
2	Transformer + Pre-train with Pseudo Data + BERT	GLEU	62	—	Unverified
3	SMT + BiGRU	GLEU	61.5	—	Unverified
4	Copy-augmented Model (4 Ensemble +Denoising Autoencoder)	GLEU	61	—	Unverified
5	Transformer	GLEU	59.9	—	Unverified
6	CNN Seq2Seq	GLEU	57.47	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Llama + 1M BT + gold	F0.5	74.09	—	Unverified
2	mBART-based model with synthetic data	F0.5	68.17	—	Unverified
3	mT5 large + 10M synth	F0.5	68.09	—	Unverified
4	RedPenNet	F0.5	67.71	—	Unverified
5	ChatGPT (zero-shot)	F0.5	27.4	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	GRECO (vote+ESC)	F0.5	85.21	—	Unverified
2	SMT + BiGRU	F0.5	72.04	—	Unverified
3	CNN Seq2Seq	F0.5	70.14	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	CNN Seq2Seq + Quality Estimation	F0.5	56.52	—	Unverified
2	Transformer	F0.5	55.8	—	Unverified
3	+ BIFI with no critic	F0.5	18.7	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	CNN Seq2Seq + Fluency Boost and inference	GLEU	62.37	—	Unverified
2	CNN Seq2Seq + Fluency Boost	F0.5	61.34	—	Unverified
3	+ BIFI (ours)	F0.5	42.4	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Transformer	GLEU	59.9	—	Unverified
2	CNN Seq2Seq	GLEU	57.47	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Llama + 1M BT + gold	F0.5	69.97	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	STG-Joint	exact match	34.1	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	GEC-DI (LM+GED)	F0.5	48.61	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	RedPenNet	F0.5	77.6	—	Unverified