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 251–275 of 415 papers

Title	Date	Tasks	Status
Improving Precision of Grammatical Error Correction with a Cheat Sheet	Aug 1, 2019	Grammatical Error CorrectionMachine Translation	—Unverified
Neural Grammatical Error Correction Systems with Unsupervised Pre-training on Synthetic Data	Aug 1, 2019	Grammatical Error CorrectionUnsupervised Pre-training	CodeCode Available
Erroneous data generation for Grammatical Error Correction	Aug 1, 2019	Grammatical Error Correction	—Unverified
Neural and FST-based approaches to grammatical error correction	Aug 1, 2019	Grammatical Error CorrectionMachine Translation	—Unverified
(Almost) Unsupervised Grammatical Error Correction using Synthetic Comparable Corpus	Aug 1, 2019	Grammatical Error CorrectionMachine Translation	—Unverified
TMU Transformer System Using BERT for Re-ranking at BEA 2019 Grammatical Error Correction on Restricted Track	Aug 1, 2019	Grammatical Error CorrectionRe-Ranking	—Unverified
The LAIX Systems in the BEA-2019 GEC Shared Task	Aug 1, 2019	ClassificationGeneral Classification	—Unverified
The BLCU System in the BEA 2019 Shared Task	Aug 1, 2019	Grammatical Error Correction	—Unverified
The BEA-2019 Shared Task on Grammatical Error Correction	Aug 1, 2019	Grammatical Error Correction	—Unverified
The AIP-Tohoku System at the BEA-2019 Shared Task	Aug 1, 2019	Grammatical Error CorrectionGrammatical Error Detection	—Unverified
Artificial Error Generation with Fluency Filtering	Aug 1, 2019	Grammatical Error Correction	—Unverified
Towards Unsupervised Grammatical Error Correction using Statistical Machine Translation with Synthetic Comparable Corpus	Jul 23, 2019	Grammatical Error CorrectionMachine Translation	—Unverified
The Unbearable Weight of Generating Artificial Errors for Grammatical Error Correction	Jul 21, 2019	Grammatical Error Correction	—Unverified
A Neural Grammatical Error Correction System Built On Better Pre-training and Sequential Transfer Learning	Jul 2, 2019	Grammatical Error CorrectionTransfer Learning	CodeCode Available
Controlling Grammatical Error Correction Using Word Edit Rate	Jul 1, 2019	Grammatical Error CorrectionSentence	—Unverified
Automatic Grammatical Error Correction for Sequence-to-sequence Text Generation: An Empirical Study	Jul 1, 2019	Formality Style TransferGrammatical Error Correction	—Unverified
Cross-Sentence Grammatical Error Correction	Jul 1, 2019	DecoderGrammatical Error Correction	CodeCode Available
The CUED's Grammatical Error Correction Systems for BEA-2019	Jun 29, 2019	Grammatical Error CorrectionMachine Translation	—Unverified
Learning to combine Grammatical Error Corrections	Jun 10, 2019	Grammatical Error Correction	CodeCode Available
The Unreasonable Effectiveness of Transformer Language Models in Grammatical Error Correction	Jun 4, 2019	Grammatical Error CorrectionLanguage Modeling	CodeCode Available
Corpora Generation for Grammatical Error Correction	Apr 10, 2019	Grammatical Error CorrectionMachine Translation	—Unverified
Cross-Corpora Evaluation and Analysis of Grammatical Error Correction Models --- Is Single-Corpus Evaluation Enough?	Apr 5, 2019	Grammatical Error CorrectionNMT	—Unverified
Generate, Filter, and Rank: Grammaticality Classification for Production-Ready NLG Systems	Apr 5, 2019	ClassificationGeneral Classification	CodeCode Available
Grammatical Error Correction and Style Transfer via Zero-shot Monolingual Translation	Mar 27, 2019	Grammatical Error CorrectionStyle Transfer	—Unverified
Neural Grammatical Error Correction with Finite State Transducers	Mar 25, 2019	Grammatical Error CorrectionLanguage Modeling	—Unverified

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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