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Question Answering On Trecqa

Métriques

MAP

MRR

Résultats

Résultats de performance de divers modèles sur ce benchmark

Nom du modèle	MAP	MRR	Paper Title	Repository
Contextual DeBERTa-V3-Large + SSP	0.919	0.945	Context-Aware Transformer Pre-Training for Answer Sentence Selection	-
CNN	0.711	0.785	Deep Learning for Answer Sentence Selection
NLP-Capsule	0.7773	0.7416	Towards Scalable and Reliable Capsule Networks for Challenging NLP Applications
DeBERTa-V3-Large + SSP	0.923	0.946	Pre-training Transformer Models with Sentence-Level Objectives for Answer Sentence Selection	-
aNMM	0.750	0.811	aNMM: Ranking Short Answer Texts with Attention-Based Neural Matching Model
HyperQA	0.770	0.825	Hyperbolic Representation Learning for Fast and Efficient Neural Question Answering
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	-
PWIN	0.7588	0.8219	-	-
TANDA DeBERTa-V3-Large + ALL	0.954	0.984	Structural Self-Supervised Objectives for Transformers	-
Comp-Clip + LM + LC	0.868	0.928	A Compare-Aggregate Model with Latent Clustering for Answer Selection	-
TANDA-RoBERTa (ASNQ, TREC-QA)	0.943	0.974	TANDA: Transfer and Adapt Pre-Trained Transformer Models 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	-

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Question Answering On Trecqa | SOTA | HyperAI