SOTAVerified

Question Answering

Question answering can be segmented into domain-specific tasks like community question answering and knowledge-base question answering. Popular benchmark datasets for evaluation question answering systems include SQuAD, HotPotQA, bAbI, TriviaQA, WikiQA, and many others. Models for question answering are typically evaluated on metrics like EM and F1. Some recent top performing models are T5 and XLNet.

( Image credit: SQuAD )

Papers

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Showing 5526–5550 of 10817 papers

TitleStatusHype
Train Flat, Then Compress: Sharpness-Aware Minimization Learns More Compressible Models—0
Investigating Information Inconsistency in Multilingual Open-Domain Question Answering—0
Leveraging QA Datasets to Improve Generative Data AugmentationCode0
Reasoning over Logically Interacted Conditions for Question Answering—0
Re-Examining Calibration: The Case of Question AnsweringCode0
Would You Ask it that Way? Measuring and Improving Question Naturalness for Knowledge Graph Question AnsweringCode1
Guiding Visual Question Answering with Attention Priors—0
FabKG: A Knowledge graph of Manufacturing Science domain utilizing structured and unconventional unstructured knowledge source—0
From Easy to Hard: Two-stage Selector and Reader for Multi-hop Question Answering—0
RetroMAE: Pre-Training Retrieval-oriented Language Models Via Masked Auto-EncoderCode2
mPLUG: Effective and Efficient Vision-Language Learning by Cross-modal Skip-connectionsCode1
Policy Compliance Detection via Expression Tree Inference—0
Reassessing Evaluation Practices in Visual Question Answering: A Case Study on Out-of-Distribution Generalization—0
A Survey on Neural Open Information Extraction: Current Status and Future Directions—0
On Advances in Text Generation from Images Beyond Captioning: A Case Study in Self-Rationalization—0
Community Question Answering Entity Linking via Leveraging Auxiliary DataCode0
On Measuring Social Biases in Prompt-Based Multi-Task LearningCode1
Time-series Transformer Generative Adversarial NetworksCode3
Memory-enriched computation and learning in spiking neural networks through Hebbian plasticityCode0
Prompt Tuning for Discriminative Pre-trained Language ModelsCode1
VQA-GNN: Reasoning with Multimodal Knowledge via Graph Neural Networks for Visual Question Answering—0
StreamingQA: A Benchmark for Adaptation to New Knowledge over Time in Question Answering ModelsCode1
Relphormer: Relational Graph Transformer for Knowledge Graph RepresentationsCode1
Language Models with Image Descriptors are Strong Few-Shot Video-Language LearnersCode1
Interpretable Proof Generation via Iterative Backward ReasoningCode0
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All datasetsSQuAD2.0SQuAD1.1HotpotQAPIQABoolQCOPATriviaQASQuAD1.1 devNatural QuestionsOpenBookQATruthfulQAMultiRC

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1IE-Net (ensemble)EM90.94—Unverified
2FPNet (ensemble)EM90.87—Unverified
3IE-NetV2 (ensemble)EM90.86—Unverified
4SA-Net on Albert (ensemble)EM90.72—Unverified
5SA-Net-V2 (ensemble)EM90.68—Unverified
6FPNet (ensemble)EM90.6—Unverified
7Retro-Reader (ensemble)EM90.58—Unverified
8EntitySpanFocusV2 (ensemble)EM90.52—Unverified
9TransNets + SFVerifier + SFEnsembler (ensemble)EM90.49—Unverified
10EntitySpanFocus+AT (ensemble)EM90.45—Unverified