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

Showing 10511100 of 10817 papers

TitleStatusHype
Beyond NED: Fast and Effective Search Space Reduction for Complex Question Answering over Knowledge BasesCode1
EgoTextVQA: Towards Egocentric Scene-Text Aware Video Question AnsweringCode1
Expressive Scene Graph Generation Using Commonsense Knowledge Infusion for Visual Understanding and ReasoningCode1
ECoRAG: Evidentiality-guided Compression for Long Context RAGCode1
Editing Factual Knowledge in Language ModelsCode1
ECG-QA: A Comprehensive Question Answering Dataset Combined With ElectrocardiogramCode1
ECBench: Can Multi-modal Foundation Models Understand the Egocentric World? A Holistic Embodied Cognition BenchmarkCode1
Structure-aware Domain Knowledge Injection for Large Language ModelsCode1
EarthVQA: Towards Queryable Earth via Relational Reasoning-Based Remote Sensing Visual Question AnsweringCode1
EAGER: Asking and Answering Questions for Automatic Reward Shaping in Language-guided RLCode1
EA^2E: Improving Consistency with Event Awareness for Document-Level Argument ExtractionCode1
EA^2E: Improving Consistency with Event Awareness for Document-Level Argument ExtractionCode1
EasyTransfer -- A Simple and Scalable Deep Transfer Learning Platform for NLP ApplicationsCode1
Educational Question Generation of Children Storybooks via Question Type Distribution Learning and Event-Centric SummarizationCode1
Dynamic Multimodal Evaluation with Flexible Complexity by Vision-Language BootstrappingCode1
Dynamic Language Binding in Relational Visual ReasoningCode1
Dynamic Relevance Graph Network for Knowledge-Aware Question AnsweringCode1
DyGKT: Dynamic Graph Learning for Knowledge TracingCode1
A Comprehensive Evaluation of GPT-4V on Knowledge-Intensive Visual Question AnsweringCode1
Dynamically Fused Graph Network for Multi-hop ReasoningCode1
Dynamic Semantic Graph Construction and Reasoning for Explainable Multi-hop Science Question AnsweringCode1
Dual Reader-Parser on Hybrid Textual and Tabular Evidence for Open Domain Question AnsweringCode1
Dual-Key Multimodal Backdoors for Visual Question AnsweringCode1
DUAL: Discrete Spoken Unit Adaptive Learning for Textless Spoken Question AnsweringCode1
Attention-Based Context Aware Reasoning for Situation RecognitionCode1
DualVGR: A Dual-Visual Graph Reasoning Unit for Video Question AnsweringCode1
DynaPipe: Optimizing Multi-task Training through Dynamic PipelinesCode1
DRESSing Up LLM: Efficient Stylized Question-Answering via Style Subspace EditingCode1
DREAM: Improving Situational QA by First Elaborating the SituationCode1
Don't Take the Easy Way Out: Ensemble Based Methods for Avoiding Known Dataset BiasesCode1
DrBenchmark: A Large Language Understanding Evaluation Benchmark for French Biomedical DomainCode1
Does Vision-and-Language Pretraining Improve Lexical Grounding?Code1
Does Time Have Its Place? Temporal Heads: Where Language Models Recall Time-specific InformationCode1
DOM-LM: Learning Generalizable Representations for HTML DocumentsCode1
DocVXQA: Context-Aware Visual Explanations for Document Question AnsweringCode1
DocVQA: A Dataset for VQA on Document ImagesCode1
Don't Generate, Discriminate: A Proposal for Grounding Language Models to Real-World EnvironmentsCode1
DrugEHRQA: A Question Answering Dataset on Structured and Unstructured Electronic Health Records For Medicine Related QueriesCode1
Divide and Conquer: Text Semantic Matching with Disentangled Keywords and IntentsCode1
Diversify Question Generation with Retrieval-Augmented Style TransferCode1
DocNLI: A Large-scale Dataset for Document-level Natural Language InferenceCode1
Distinguishing Ignorance from Error in LLM HallucinationsCode1
AtomR: Atomic Operator-Empowered Large Language Models for Heterogeneous Knowledge ReasoningCode1
Ditch the Gold Standard: Re-evaluating Conversational Question AnsweringCode1
DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighterCode1
Distantly-Supervised Evidence Retrieval Enables Question Answering without Evidence AnnotationCode1
Distilled Dual-Encoder Model for Vision-Language UnderstandingCode1
Disfl-QA: A Benchmark Dataset for Understanding Disfluencies in Question AnsweringCode1
A Comparison of Pre-trained Vision-and-Language Models for Multimodal Representation Learning across Medical Images and ReportsCode1
Distantly-Supervised Dense Retrieval Enables Open-Domain Question Answering without Evidence AnnotationCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1IE-Net (ensemble)EM90.94Unverified
2FPNet (ensemble)EM90.87Unverified
3IE-NetV2 (ensemble)EM90.86Unverified
4SA-Net on Albert (ensemble)EM90.72Unverified
5SA-Net-V2 (ensemble)EM90.68Unverified
6FPNet (ensemble)EM90.6Unverified
7Retro-Reader (ensemble)EM90.58Unverified
8EntitySpanFocusV2 (ensemble)EM90.52Unverified
9TransNets + SFVerifier + SFEnsembler (ensemble)EM90.49Unverified
10EntitySpanFocus+AT (ensemble)EM90.45Unverified