HyperAI超神经

Question Answering On Trecqa

评估指标

MAP

MRR

评测结果

各个模型在此基准测试上的表现结果

			Paper Title
TANDA DeBERTa-V3-Large + ALL	0.954	0.984	Structural Self-Supervised Objectives for Transformers
TANDA-RoBERTa (ASNQ, TREC-QA)	0.943	0.974	TANDA: Transfer and Adapt Pre-Trained Transformer Models for Answer Sentence Selection
DeBERTa-V3-Large + SSP	0.923	0.946	Pre-training Transformer Models with Sentence-Level Objectives for Answer Sentence Selection
Contextual DeBERTa-V3-Large + SSP	0.919	0.945	Context-Aware Transformer Pre-Training for Answer Sentence Selection
RLAS-BIABC	0.913	0.998	RLAS-BIABC: A Reinforcement Learning-Based Answer Selection Using the BERT Model Boosted by an Improved ABC Algorithm
RoBERTa-Base Joint + MSPP	0.911	0.952	Paragraph-based Transformer Pre-training for Multi-Sentence Inference
RoBERTa-Base + PSD	0.903	0.951	Pre-training Transformer Models with Sentence-Level Objectives for Answer Sentence Selection
Comp-Clip + LM + LC	0.868	0.928	A Compare-Aggregate Model with Latent Clustering for Answer Selection
NLP-Capsule	0.7773	0.7416	Towards Scalable and Reliable Capsule Networks for Challenging NLP Applications
HyperQA	0.770	0.825	Hyperbolic Representation Learning for Fast and Efficient Neural Question Answering
PWIN	0.7588	0.8219	-
aNMM	0.750	0.811	aNMM: Ranking Short Answer Texts with Attention-Based Neural Matching Model
CNN	0.711	0.785	Deep Learning for Answer Sentence Selection

0 of 13 row(s) selected.

HyperAI超神经

Question Answering On Trecqa

评估指标

MAP

MRR

评测结果

各个模型在此基准测试上的表现结果

			Paper Title
TANDA DeBERTa-V3-Large + ALL	0.954	0.984	Structural Self-Supervised Objectives for Transformers
TANDA-RoBERTa (ASNQ, TREC-QA)	0.943	0.974	TANDA: Transfer and Adapt Pre-Trained Transformer Models for Answer Sentence Selection
DeBERTa-V3-Large + SSP	0.923	0.946	Pre-training Transformer Models with Sentence-Level Objectives for Answer Sentence Selection
Contextual DeBERTa-V3-Large + SSP	0.919	0.945	Context-Aware Transformer Pre-Training for Answer Sentence Selection
RLAS-BIABC	0.913	0.998	RLAS-BIABC: A Reinforcement Learning-Based Answer Selection Using the BERT Model Boosted by an Improved ABC Algorithm
RoBERTa-Base Joint + MSPP	0.911	0.952	Paragraph-based Transformer Pre-training for Multi-Sentence Inference
RoBERTa-Base + PSD	0.903	0.951	Pre-training Transformer Models with Sentence-Level Objectives for Answer Sentence Selection
Comp-Clip + LM + LC	0.868	0.928	A Compare-Aggregate Model with Latent Clustering for Answer Selection
NLP-Capsule	0.7773	0.7416	Towards Scalable and Reliable Capsule Networks for Challenging NLP Applications
HyperQA	0.770	0.825	Hyperbolic Representation Learning for Fast and Efficient Neural Question Answering
PWIN	0.7588	0.8219	-
aNMM	0.750	0.811	aNMM: Ranking Short Answer Texts with Attention-Based Neural Matching Model
CNN	0.711	0.785	Deep Learning for Answer Sentence Selection

0 of 13 row(s) selected.

Question Answering On Trecqa | SOTA | HyperAI超神经