HyperAI超神経

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Grammatical Error Correction On Bea 2019 Test

評価指標

F0.5

評価結果

このベンチマークにおける各モデルのパフォーマンス結果

		Paper Title
Majority-voting ensemble on best 7 models	81.4	Pillars of Grammatical Error Correction: Comprehensive Inspection Of Contemporary Approaches In The Era of Large Language Models
GRECO (voting+ESC)	80.84	System Combination via Quality Estimation for Grammatical Error Correction
ESC	79.90	Frustratingly Easy System Combination for Grammatical Error Correction
RedPenNet	77.60	RedPenNet for Grammatical Error Correction: Outputs to Tokens, Attentions to Spans
clang_large_ft2-gector	77.1	Improved grammatical error correction by ranking elementary edits
Unsupervised GEC + cLang8	76.5	Unsupervised Grammatical Error Correction Rivaling Supervised Methods
DeBERTa + RoBERTa + XLNet	76.05	Ensembling and Knowledge Distilling of Large Sequence Taggers for Grammatical Error Correction
MoECE	74.07	Efficient and Interpretable Grammatical Error Correction with Mixture of Experts
Sequence tagging + token-level transformations + two-stage fine-tuning (+RoBERTa, XLNet)	73.7	GECToR -- Grammatical Error Correction: Tag, Not Rewrite
BEA Combination	73.2	Learning to combine Grammatical Error Corrections
GEC-DI (LM+GED)	73.1	Improving Seq2Seq Grammatical Error Correction via Decoding Interventions
LM-Critic	72.9	LM-Critic: Language Models for Unsupervised Grammatical Error Correction
Sequence tagging + token-level transformations + two-stage fine-tuning (+XLNet)	72.4	GECToR -- Grammatical Error Correction: Tag, Not Rewrite
Transformer + Pre-train with Pseudo Data	70.2	An Empirical Study of Incorporating Pseudo Data into Grammatical Error Correction
Transformer + Pre-train with Pseudo Data (+BERT)	69.8	Encoder-Decoder Models Can Benefit from Pre-trained Masked Language Models in Grammatical Error Correction
Transformer	69.5	Neural Grammatical Error Correction Systems with Unsupervised Pre-training on Synthetic Data
Transformer	69.0	A Neural Grammatical Error Correction System Built On Better Pre-training and Sequential Transfer Learning
VERNet	68.9	Neural Quality Estimation with Multiple Hypotheses for Grammatical Error Correction
Ensemble of models	66.78	The LAIX Systems in the BEA-2019 GEC Shared Task

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