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 6651–6675 of 10817 papers

TitleStatusHype
Learning to Select Question-Relevant Relations for Visual Question Answering—0
SPARTQA: A Textual Question Answering Benchmark for Spatial ReasoningCode1
CLEVR\_HYP: A Challenge Dataset and Baselines for Visual Question Answering with Hypothetical Actions over ImagesCode0
NAMER: A Node-Based Multitasking Framework for Multi-Hop Knowledge Base Question AnsweringCode0
Adaptable and Interpretable Neural MemoryOver Symbolic Knowledge—0
MM-AVS: A Full-Scale Dataset for Multi-modal Summarization—0
Breadth First Reasoning Graph for Multi-hop Question Answering—0
MIMOQA: Multimodal Input Multimodal Output Question Answering—0
Discovering Better Model Architectures for Medical Query Understanding—0
AVA: an Automatic eValuation Approach for Question Answering Systems—0
An Emotional Comfort Framework for Improving User Satisfaction in E-Commerce Customer Service Chatbots—0
Improving Zero-Shot Cross-lingual Transfer for Multilingual Question Answering over Knowledge Graph—0
If You Want to Go Far Go Together: Unsupervised Joint Candidate Evidence Retrieval for Multi-hop Question Answering—0
Goodwill Hunting: Analyzing and Repurposing Off-the-Shelf Named Entity Linking Systems—0
RECONSIDER: Improved Re-Ranking using Span-Focused Cross-Attention for Open Domain Question Answering—0
Explainable Multi-hop Verbal Reasoning Through Internal Monologue—0
EaSe: A Diagnostic Tool for VQA based on Answer DiversityCode0
Looking Beyond Sentence-Level Natural Language Inference for Question Answering and Text Summarization—0
DeCEMBERT: Learning from Noisy Instructional Videos via Dense Captions and Entropy MinimizationCode1
Worldly Wise (WoW) - Cross-Lingual Knowledge Fusion for Fact-based Visual Spoken-Question Answering—0
Using Integrated Gradients and Constituency Parse Trees to explain Linguistic Acceptability learnt by BERT—0
Game-theoretic Vocabulary Selection via the Shapley Value and Banzhaf Index—0
What Ingredients Make for an Effective Crowdsourcing Protocol for Difficult NLU Data Collection Tasks?Code0
Parameter-Efficient Neural Question Answering Models via Graph-Enriched Document Representations—0
Question-aware Transformer Models for Consumer Health Question Summarization—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