Grammatical Error Correction On Conll 2014

评估指标

F0.5

Precision

Recall

评测结果

各个模型在此基准测试上的表现结果

模型名称	F0.5	Precision	Recall	Paper Title	Repository
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	-
CNN Seq2Seq + Quality Estimation	56.52	-	-	Neural Quality Estimation of Grammatical Error Correction
Transformer	55.8	-	-	Approaching Neural Grammatical Error Correction as a Low-Resource Machine Translation Task	-
LM-Critic	65.8	-	-	LM-Critic: Language Models for Unsupervised Grammatical Error Correction	-
GEC-DI (LM+GED)	69.6	79.2	46.8	Improving Seq2Seq Grammatical Error Correction via Decoding Interventions	-
Sequence Labeling with edits using BERT, Faster inference	61.2	-	-	Parallel Iterative Edit Models for Local Sequence Transduction	-
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
Transformer + Pre-train with Pseudo Data	65.0	-	-	An Empirical Study of Incorporating Pseudo Data into Grammatical Error Correction	-
SMT + BiGRU	56.25	-	-	Near Human-Level Performance in Grammatical Error Correction with Hybrid Machine Translation	-
BART	63.0	69.9	45.1	Stronger Baselines for Grammatical Error Correction Using Pretrained Encoder-Decoder Model	-
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	-
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 (+XLNet)	65.3	77.5	40.1	GECToR -- Grammatical Error Correction: Tag, Not Rewrite	-
GRECO (voting+ESC)	71.12	79.6	49.86	System Combination via Quality Estimation for Grammatical Error Correction	-
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	-
T5	68.87	-	-	A Simple Recipe for Multilingual Grammatical Error Correction	-
CNN Seq2Seq	54.79	-	-	A Multilayer Convolutional Encoder-Decoder Neural Network for Grammatical Error Correction	-
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	-
Sequence Labeling with edits using BERT, Faster inference (Single Model)	59.7	-	-	Parallel Iterative Edit Models for Local Sequence Transduction	-

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