Question Answering On Squad11

Résultats

Résultats de performance de divers modèles sur ce benchmark

Nom du modèle	EM	F1	Paper Title	Repository
SAN (ensemble model)	79.608	86.496	Stochastic Answer Networks for Machine Reading Comprehension
S^3-Net (single model)	71.908	81.023	-	-
RQA (single model)	55.827	65.467	Harvesting and Refining Question-Answer Pairs for Unsupervised QA
PQMN (single model)	68.331	77.783	-	-
BERT - 3 Layers	77.7	85.8	Information Theoretic Representation Distillation
RuBERT	-	84.6	Adaptation of Deep Bidirectional Multilingual Transformers for Russian Language
BERT-uncased (single model)	84.926	91.932	-	-
{ANNA} (single model)	90.622	95.719	-	-
BISAN (single model)	85.314	91.756	-	-
Conductor-net (single model)	74.405	82.742	Phase Conductor on Multi-layered Attentions for Machine Comprehension	-
KACTEIL-MRC(GF-Net+) (single model)	78.664	85.780	-	-
BERT-Large 32k batch size with AdamW	-	91.58	A Large Batch Optimizer Reality Check: Traditional, Generic Optimizers Suffice Across Batch Sizes	-
FusionNet (single model)	75.968	83.900	FusionNet: Fusing via Fully-Aware Attention with Application to Machine Comprehension
WD (single model)	84.402	90.561	-	-
DyREX	-	91.01	DyREx: Dynamic Query Representation for Extractive Question Answering
WAHnGREA	0.000	0.000	-	-
S^3-Net (ensemble)	74.121	82.342	-	-
RaSoR + TR (single model)	75.789	83.261	Contextualized Word Representations for Reading Comprehension
RQA+IDR (single model)	61.145	71.389	Harvesting and Refining Question-Answer Pairs for Unsupervised QA
MEMEN (single model)	78.234	85.344	MEMEN: Multi-layer Embedding with Memory Networks for Machine Comprehension	-

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