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 6826–6850 of 10817 papers

TitleStatusHype
UPB at SemEval-2021 Task 8: Extracting Semantic Information on Measurements as Multi-Turn Question Answering—0
FIBER: Fill-in-the-Blanks as a Challenging Video Understanding Evaluation FrameworkCode0
PQA: Perceptual Question AnsweringCode0
Video Question Answering with Phrases via Semantic Roles—0
How Transferable are Reasoning Patterns in VQA?—0
Multimodal Continuous Visual Attention Mechanisms—0
Distantly Supervised Transformers For E-Commerce Product QA—0
A Question-answering Based Framework for Relation Extraction Validation—0
Beyond Question-Based Biases: Assessing Multimodal Shortcut Learning in Visual Question AnsweringCode1
CBench: Towards Better Evaluation of Question Answering Over Knowledge GraphsCode1
Paired Examples as Indirect Supervision in Latent Decision Models—0
Compressing Visual-linguistic Model via Knowledge Distillation—0
Conversational Question Answering over Knowledge Graphs with Transformer and Graph Attention NetworksCode1
ASPER: Attention-based Approach to Extract Syntactic Patterns denoting Semantic Relations in Sentential Context—0
MMBERT: Multimodal BERT Pretraining for Improved Medical VQACode1
Type Prediction Systems—0
VisQA: X-raying Vision and Language Reasoning in TransformersCode1
A View From the Crowd: Evaluation Challenges for Time-Offset Interaction Applications—0
Exploring Neural Language Models via Analysis of Local and Global Self-Attention SpacesCode0
Automatically Generating Cause-and-Effect Questions from PassagesCode1
MultiReQA: A Cross-Domain Evaluation forRetrieval Question Answering ModelsCode1
NLQuAD: A Non-Factoid Long Question Answering Data SetCode1
Retrieval, Re-ranking and Multi-task Learning for Knowledge-Base Question Answering—0
Structural Encoding and Pre-training Matter: Adapting BERT for Table-Based Fact Verification—0
Knowledge Base Question Answering through Recursive Hypergraphs—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