Grammatical Error Correction On Conll 2014

Metrics

F0.5

Precision

Recall

Results

Performance results of various models on this benchmark

				Paper Title	Repository
Ensembles of best 7 models + GRECO + GTP-rerank	72.8	83.9	47.5	Pillars of Grammatical Error Correction: Comprehensive Inspection Of Contemporary Approaches In The Era of Large Language Models
Majority-voting ensemble on best 7 models	71.8	83.7	45.7	Pillars of Grammatical Error Correction: Comprehensive Inspection Of Contemporary Approaches In The Era of Large Language Models
GRECO (voting+ESC)	71.12	79.6	49.86	System Combination via Quality Estimation for Grammatical Error Correction
GEC-DI (LM+GED)	69.6	79.2	46.8	Improving Seq2Seq Grammatical Error Correction via Decoding Interventions
Unsupervised GEC + cLang8	69.6	75.0	53.8	Unsupervised Grammatical Error Correction Rivaling Supervised Methods	-
ESC	69.51	81.48	43.78	Frustratingly Easy System Combination for Grammatical Error Correction	-
T5	68.87	-	-	A Simple Recipe for Multilingual Grammatical Error Correction
MoECE	67.79	74.29	50.21	Efficient and Interpretable Grammatical Error Correction with Mixture of Experts
SynGEC	67.6	74.7	49.0	SynGEC: Syntax-Enhanced Grammatical Error Correction with a Tailored GEC-Oriented Parser
Sequence tagging + token-level transformations + two-stage fine-tuning (+BERT, RoBERTa, XLNet)	66.5	78.2	41.5	GECToR -- Grammatical Error Correction: Tag, Not Rewrite
LM-Critic	65.8	-	-	LM-Critic: Language Models for Unsupervised Grammatical Error Correction
Sequence tagging + token-level transformations + two-stage fine-tuning (+XLNet)	65.3	77.5	40.1	GECToR -- Grammatical Error Correction: Tag, Not Rewrite
Transformer + Pre-train with Pseudo Data (+BERT)	65.2	-	-	Encoder-Decoder Models Can Benefit from Pre-trained Masked Language Models in Grammatical Error Correction
Transformer + Pre-train with Pseudo Data	65.0	-	-	An Empirical Study of Incorporating Pseudo Data into Grammatical Error Correction
VERNet	63.7	-	-	Neural Quality Estimation with Multiple Hypotheses for Grammatical Error Correction
BART	63.0	69.9	45.1	Stronger Baselines for Grammatical Error Correction Using Pretrained Encoder-Decoder Model
Sequence Labeling with edits using BERT, Faster inference	61.2	-	-	Parallel Iterative Edit Models for Local Sequence Transduction
Copy-augmented Model (4 Ensemble +Denoising Autoencoder)	61.15	71.57	38.65	Improving Grammatical Error Correction via Pre-Training a Copy-Augmented Architecture with Unlabeled Data
Sequence Labeling with edits using BERT, Faster inference (Single Model)	59.7	-	-	Parallel Iterative Edit Models for Local Sequence Transduction
CNN Seq2Seq + Quality Estimation	56.52	-	-	Neural Quality Estimation of Grammatical Error Correction	-

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Grammatical Error Correction On Conll 2014

Metrics

Results