Question Answering On Wikiqa

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

模型名称	MAP	MRR	Paper Title	Repository
Paragraph vector	0.5110	0.5160	Distributed Representations of Sentences and Documents
DeBERTa-Large + SSP	0.901	0.914	Pre-training Transformer Models with Sentence-Level Objectives for Answer Sentence Selection	-
HyperQA	0.712	0.727	Hyperbolic Representation Learning for Fast and Efficient Neural Question Answering
PWIM	0.7090	0.7234	-	-
Paragraph vector (lexical overlap + dist output)	0.5976	0.6058	Distributed Representations of Sentences and Documents
SWEM-concat	0.6788	0.6908	Baseline Needs More Love: On Simple Word-Embedding-Based Models and Associated Pooling Mechanisms
LSTM (lexical overlap + dist output)	0.682	0.6988	Neural Variational Inference for Text Processing
Bigram-CNN (lexical overlap + dist output)	0.6520	0.6652	Deep Learning for Answer Sentence Selection
TANDA-RoBERTa (ASNQ, WikiQA)	0.920	0.933	TANDA: Transfer and Adapt Pre-Trained Transformer Models for Answer Sentence Selection	-
RE2	0.7452	0.7618	Simple and Effective Text Matching with Richer Alignment Features
PairwiseRank + Multi-Perspective CNN	0.7010	0.7180	Noise Contrastive Estimation and Negative Sampling for Conditional Models: Consistency and Statistical Efficiency	-
RoBERTa-Base + SSP	0.887	0.899	Pre-training Transformer Models with Sentence-Level Objectives for Answer Sentence Selection	-
LSTM	0.6552	0.6747	Neural Variational Inference for Text Processing
AP-CNN	0.6886	0.6957	Attentive Pooling Networks
CNN-Cnt	0.6520	0.6652	-	-
Bigram-CNN	0.6190	0.6281	Deep Learning for Answer Sentence Selection
RLAS-BIABC	0.924	0.908	RLAS-BIABC: A Reinforcement Learning-Based Answer Selection Using the BERT Model Boosted by an Improved ABC Algorithm	-
MMA-NSE attention	0.6811	0.6993	Neural Semantic Encoders
LDC	0.7058	0.7226	Sentence Similarity Learning by Lexical Decomposition and Composition
Comp-Clip + LM + LC	0.764	0.784	A Compare-Aggregate Model with Latent Clustering for Answer Selection	-

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