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 5201–5250 of 10817 papers

TitleStatusHype
Logical Form Generation via Multi-task Learning for Complex Question Answering over Knowledge BasesCode1
CausalQA: A Benchmark for Causal Question AnsweringCode0
Can We Guide a Multi-Hop Reasoning Language Model to Incrementally Learn at Each Single-Hop?Code0
Can Edge Probing Tests Reveal Linguistic Knowledge in QA Models?—0
Dual Capsule Attention Mask Network with Mutual Learning for Visual Question Answering—0
Wizard of Tasks: A Novel Conversational Dataset for Solving Real-World Tasks in Conversational Settings—0
A Zero-Shot Claim Detection Framework Using Question Answering—0
Noun-MWP: Math Word Problems Meet Noun Answers—0
An Augmented Benchmark Dataset for Geometric Question Answering through Dual Parallel Text Encoding—0
In-the-Wild Video Question Answering—0
Insurance Question Answering via Single-turn Dialogue Modeling—0
Weakly Supervised Formula Learner for Solving Mathematical ProblemsCode0
CORN: Co-Reasoning Network for Commonsense Question Answering—0
Hierarchical Representation-based Dynamic Reasoning Network for Biomedical Question Answering—0
Type-dependent Prompt CycleQAG : Cycle Consistency for Multi-hop Question Generation—0
Competence-based Question Generation—0
MRC-based Medical NER with Multi-task Learning and Multi-strategies—0
Interactive Mongolian Question Answer Matching Model Based on Attention Mechanism in the Law Domain—0
LambdaKG: A Library for Pre-trained Language Model-Based Knowledge Graph EmbeddingsCode2
A Dual-Attention Learning Network with Word and Sentence Embedding for Medical Visual Question AnsweringCode0
Calibrating Sequence likelihood Improves Conditional Language Generation—0
Task Formulation Matters When Learning Continually: A Case Study in Visual Question AnsweringCode0
Medical Question Understanding and Answering with Knowledge Grounding and Semantic Self-SupervisionCode0
Linearly Mapping from Image to Text SpaceCode1
DecAF: Joint Decoding of Answers and Logical Forms for Question Answering over Knowledge BasesCode1
Bidirectional Language Models Are Also Few-shot Learners—0
Using contradictions improves question answering systems—0
ArNLI: Arabic Natural Language Inference for Entailment and Contradiction DetectionCode0
TVLT: Textless Vision-Language TransformerCode1
Downstream Datasets Make Surprisingly Good Pretraining CorporaCode0
Extractive Question Answering on Queries in Hindi and Tamil—0
RepsNet: Combining Vision with Language for Automated Medical Reports—0
On the Impact of Speech Recognition Errors in Passage Retrieval for Spoken Question AnsweringCode0
Towards Explainable 3D Grounded Visual Question Answering: A New Benchmark and Strong BaselineCode1
Variational Open-Domain Question AnsweringCode1
Conversational QA Dataset Generation with Answer Revision—0
XF2T: Cross-lingual Fact-to-Text Generation for Low-Resource Languages—0
Adapting Pretrained Text-to-Text Models for Long Text SequencesCode1
Exploring Modulated Detection Transformer as a Tool for Action Recognition in VideosCode0
Toward 3D Spatial Reasoning for Human-like Text-based Visual Question Answering—0
Continual VQA for Disaster Response SystemsCode0
Dynamic Relevance Graph Network for Knowledge-Aware Question AnsweringCode1
Learn to Explain: Multimodal Reasoning via Thought Chains for Science Question AnsweringCode2
ERNIE-mmLayout: Multi-grained MultiModal Transformer for Document Understanding—0
Overcoming Language Priors in Visual Question Answering via Distinguishing Superficially Similar InstancesCode0
Structured Knowledge Grounding for Question Answering—0
CodeQueries: A Dataset of Semantic Queries over CodeCode1
Selective Token Generation for Few-shot Natural Language GenerationCode0
ScreenQA: Large-Scale Question-Answer Pairs over Mobile App ScreenshotsCode1
Answering Numerical Reasoning Questions in Table-Text Hybrid Contents with Graph-based Encoder and Tree-based DecoderCode1
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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