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 6201–6225 of 10817 papers

TitleStatusHype
Raccoons at SemEval-2022 Task 11: Leveraging Concatenated Word Embeddings for Named Entity Recognition—0
Capturing Conversational Interaction for Question Answering via Global History ReasoningCode0
UFRGSent at SemEval-2022 Task 10: Structured Sentiment Analysis using a Question Answering Model—0
On Curriculum Learning for Commonsense ReasoningCode0
Permutation Invariant Strategy Using Transformer Encoders for Table Understanding—0
SemEval-2022 Task 9: R2VQ – Competence-based Multimodal Question Answering—0
Dynamic Multistep Reasoning based on Video Scene Graph for Video Question Answering—0
VGNMN: Video-grounded Neural Module Networks for Video-Grounded Dialogue Systems—0
longhorns at DADC 2022: How many linguists does it take to fool a Question Answering model? A systematic approach to adversarial attacks.—0
Logical Story Representations via FrameNet + Semantic Parsing—0
A New Concept of Knowledge based Question Answering (KBQA) System for Multi-hop Reasoning—0
Multi-Hop Open-Domain Question Answering over Structured and Unstructured Knowledge—0
Transfer Learning and Masked Generation for Answer Verbalization—0
Knowledge Transfer between Structured and Unstructured Sources for Complex Question Answering—0
An Encoder Attribution Analysis for Dense Passage Retriever in Open-Domain Question Answering—0
Samsung Research Poland (SRPOL) at SemEval-2022 Task 9: Hybrid Question Answering Using Semantic Roles—0
PerKGQA: Question Answering over Personalized Knowledge Graphs—0
PINGAN_AI at SemEval-2022 Task 9: Recipe knowledge enhanced model applied in Competence-based Multimodal Question Answering—0
Contrastive Data and Learning for Natural Language Processing—0
HIT&QMUL at SemEval-2022 Task 9: Label-Enclosed Generative Question Answering (LEG-QA)—0
Zero-shot cross-lingual open domain question answering—0
Constraint-based Multi-hop Question Answering with Knowledge Graph—0
Conditional Generation with a Question-Answering Blueprint—0
American == White in Multimodal Language-and-Image AI—0
Seeing the wood for the trees: a contrastive regularization method for the low-resource Knowledge Base Question AnsweringCode0
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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