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 6951–6975 of 10817 papers

TitleStatusHype
Powering COVID-19 community Q&A with Curated Side Information—0
VisualMRC: Machine Reading Comprehension on Document ImagesCode1
Knowledge-driven Natural Language Understanding of English Text and its Applications—0
Muppet: Massive Multi-task Representations with Pre-FinetuningCode0
Representations for Question Answering from Documents with Tables and Text—0
Unanswerable Questions about Images and Texts—0
Meta-Learning for Effective Multi-task and Multilingual ModellingCode0
A2P-MANN: Adaptive Attention Inference Hops Pruned Memory-Augmented Neural Networks—0
Towards Natural Language Question Answering over Earth Observation Linked Data using Attention-based Neural Machine Translation—0
Visual Question Answering based on Local-Scene-Aware Referring Expression Generation—0
Fast Clustering of Short Text Streams Using Efficient Cluster Indexing and Dynamic Similarity Thresholds—0
Towards Confident Machine Reading Comprehension—0
Zero-shot Generalization in Dialog State Tracking through Generative Question Answering—0
Mitigating the Position Bias of Transformer Models in Passage Re-RankingCode1
Incremental Knowledge Based Question Answering—0
Tip of the Tongue Known-Item Retrieval: A Case Study in Movie Identification—0
Understanding in Artificial Intelligence—0
HySTER: A Hybrid Spatio-Temporal Event Reasoner—0
What Makes Good In-Context Examples for GPT-3?Code4
Transformer-Based Models for Question Answering on COVID19—0
Match-Ignition: Plugging PageRank into Transformer for Long-form Text MatchingCode1
Latent Variable Models for Visual Question Answering—0
ComQA:Compositional Question Answering via Hierarchical Graph Neural NetworksCode1
Coarse-grained decomposition and fine-grained interaction for multi-hop question answering—0
Recent Advances in Video Question Answering: A Review of Datasets and Methods—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