Question Answering On Wikiqa

Metrics

MAP

MRR

Results

Performance results of various models on this benchmark

Model Name	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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