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 8051–8075 of 10817 papers

TitleStatusHype
EDUQA: Educational Domain Question Answering System using Conceptual Network Mapping—0
A Survey on Why-Type Question Answering Systems—0
Open-Ended Visual Question Answering by Multi-Modal Domain Adaptation—0
TANDA: Transfer and Adapt Pre-Trained Transformer Models for Answer Sentence SelectionCode0
Meta Answering for Machine Reading—0
Learning to Order Sub-questions for Complex Question Answering—0
Biomedical Evidence Generation Engine—0
Multimodal Intelligence: Representation Learning, Information Fusion, and Applications—0
Dynamic Neuro-Symbolic Knowledge Graph Construction for Zero-shot Commonsense Question Answering—0
Knowledge Guided Named Entity Recognition for BioMedical Text—0
E-BERT: Efficient-Yet-Effective Entity Embeddings for BERTCode0
Hierarchical Graph Network for Multi-hop Question AnsweringCode0
CommonGen: A Constrained Text Generation Challenge for Generative Commonsense ReasoningCode0
Why Do Masked Neural Language Models Still Need Common Sense Knowledge?—0
Are we asking the right questions in MovieQA?—0
The TechQA DatasetCode0
Negated and Misprimed Probes for Pretrained Language Models: Birds Can Talk, But Cannot FlyCode0
A Comprehensive Comparison of Machine Learning Based Methods Used in Bengali Question Classification—0
Ask to Learn: A Study on Curiosity-driven Question Generation—0
Blockwise Self-Attention for Long Document UnderstandingCode0
Dice Loss for Data-imbalanced NLP TasksCode0
Multi-Paragraph Reasoning with Knowledge-enhanced Graph Neural Network—0
Towards Domain Adaptation from Limited Data for Question Answering Using Deep Neural Networks—0
Learning to Answer by Learning to Ask: Getting the Best of GPT-2 and BERT Worlds—0
Unsupervised Domain Adaptation of Contextual Embeddings for Low-Resource Duplicate Question Detection—0
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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