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 351–375 of 415 papers

Title	Date	Tasks	Status
Grammatical Error Correction in Low-Resource Scenarios	Oct 1, 2019	Grammatical Error CorrectionMachine Translation	CodeCode Available
Reaching Human-level Performance in Automatic Grammatical Error Correction: An Empirical Study	Jul 3, 2018	Grammatical Error CorrectionSentence	CodeCode Available
Comparative study of models trained on synthetic data for Ukrainian grammatical error correction	May 5, 2024	Grammatical Error CorrectionMachine Translation	CodeCode Available
Reassessing the Goals of Grammatical Error Correction: Fluency Instead of Grammaticality	Jan 1, 2016	Grammatical Error CorrectionSentence	CodeCode Available
RedPenNet for Grammatical Error Correction: Outputs to Tokens, Attentions to Spans	Sep 19, 2023	Grammatical Error CorrectionMachine Translation	CodeCode Available
TLM: Token-Level Masking for Transformers	Oct 28, 2023	Data-to-Text GenerationGrammatical Error Correction	CodeCode Available
Type-Driven Multi-Turn Corrections for Grammatical Error Correction	Mar 17, 2022	Data AugmentationGrammatical Error Correction	CodeCode Available
Classifying Syntactic Errors in Learner Language	Oct 21, 2020	ClassificationGeneral Classification	CodeCode Available
Grammatical Error Correction with Contrastive Learning in Low Error Density Domains	Nov 1, 2021	Contrastive LearningGrammatical Error Correction	CodeCode Available
Multi-Class Grammatical Error Detection for Correction: A Tale of Two Systems	Nov 1, 2021	DecoderGrammatical Error Correction	CodeCode Available
Multi-head Sequence Tagging Model for Grammatical Error Correction	Oct 21, 2024	DenoisingGrammatical Error Correction	CodeCode Available
Reference-less Measure of Faithfulness for Grammatical Error Correction	Apr 11, 2018	Grammatical Error Correctionvalid	CodeCode Available
Beyond Hard Samples: Robust and Effective Grammatical Error Correction with Cycle Self-Augmenting	Oct 20, 2023	Adversarial AttackGrammatical Error Correction	CodeCode Available
A Multilayer Convolutional Encoder-Decoder Neural Network for Grammatical Error Correction	Jan 26, 2018	DecoderGrammatical Error Correction	CodeCode Available
Graph-based Filtering of Out-of-Vocabulary Words for Encoder-Decoder Models	May 28, 2018	DecoderGrammatical Error Correction	CodeCode Available
Beyond Grammatical Error Correction: Improving L1-influenced research writing in English using pre-trained encoder-decoder models	Nov 1, 2021	DecoderGrammatical Error Correction	CodeCode Available
Choosing the Right Word: Using Bidirectional LSTM Tagger for Writing Support Systems	Jan 8, 2019	Grammatical Error CorrectionMachine Translation	CodeCode Available
Revisiting Meta-evaluation for Grammatical Error Correction	Mar 5, 2024	Grammatical Error CorrectionSentence	CodeCode Available
Negative language transfer in learner English: A new dataset	Jun 1, 2021	Grammatical Error CorrectionLanguage Acquisition	CodeCode Available
To Err Is Human, but Llamas Can Learn It Too	Mar 8, 2024	Grammatical Error Correction	CodeCode Available
Automatic Metric Validation for Grammatical Error Correction	Apr 30, 2018	Grammatical Error Correction	CodeCode Available
Neural Grammatical Error Correction Systems with Unsupervised Pre-training on Synthetic Data	Aug 1, 2019	Grammatical Error CorrectionUnsupervised Pre-training	CodeCode Available
Adjusting the Precision-Recall Trade-Off with Align-and-Predict Decoding for Grammatical Error Correction	May 1, 2022	Grammatical Error Correction	CodeCode Available
Neural Machine Translation Techniques for Named Entity Transliteration	Jul 1, 2018	Automatic Post-EditingDecoder	CodeCode Available
Neural Network Translation Models for Grammatical Error Correction	Jun 1, 2016	Grammatical Error CorrectionMachine Translation	CodeCode Available

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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