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 20012050 of 10817 papers

TitleStatusHype
UCD-CS at W-NUT 2020 Shared Task-3: A Text to Text Approach for COVID-19 Event Extraction on Social MediaCode1
Can questions summarize a corpus? Using question generation for characterizing COVID-19 researchCode1
MUTANT: A Training Paradigm for Out-of-Distribution Generalization in Visual Question AnsweringCode1
Generation-Augmented Retrieval for Open-domain Question AnsweringCode1
Multi-modal Summarization for Video-containing DocumentsCode1
Leveraging Semantic Parsing for Relation Linking over Knowledge BasesCode1
FILTER: An Enhanced Fusion Method for Cross-lingual Language UnderstandingCode1
QED: A Framework and Dataset for Explanations in Question AnsweringCode1
KILT: a Benchmark for Knowledge Intensive Language TasksCode1
A Comparison of Pre-trained Vision-and-Language Models for Multimodal Representation Learning across Medical Images and ReportsCode1
Text Modular Networks: Learning to Decompose Tasks in the Language of Existing ModelsCode1
AllenAct: A Framework for Embodied AI ResearchCode1
HittER: Hierarchical Transformers for Knowledge Graph EmbeddingsCode1
A Dataset and Baselines for Visual Question Answering on ArtCode1
Example-Based Named Entity RecognitionCode1
DeVLBert: Learning Deconfounded Visio-Linguistic RepresentationsCode1
Location-aware Graph Convolutional Networks for Video Question AnsweringCode1
Question and Answer Test-Train Overlap in Open-Domain Question Answering DatasetsCode1
Trojaning Language Models for Fun and ProfitCode1
MKQA: A Linguistically Diverse Benchmark for Multilingual Open Domain Question AnsweringCode1
NeuralQA: A Usable Library for Question Answering (Contextual Query Expansion + BERT) on Large DatasetsCode1
Big Bird: Transformers for Longer SequencesCode1
Semantic Equivalent Adversarial Data Augmentation for Visual Question AnsweringCode1
Knowledge-Based Video Question Answering with Unsupervised Scene DescriptionsCode1
Learning to Discretely Compose Reasoning Module Networks for Video CaptioningCode1
Visual Relation Grounding in VideosCode1
Advances of Transformer-Based Models for News Headline GenerationCode1
KQA Pro: A Dataset with Explicit Compositional Programs for Complex Question Answering over Knowledge BaseCode1
Leveraging Passage Retrieval with Generative Models for Open Domain Question AnsweringCode1
Project PIAF: Building a Native French Question-Answering DatasetCode1
Asking Effective and Diverse Questions: A Machine Reading Comprehension based Framework for Joint Entity-Relation ExtractionCode1
Latent Compositional Representations Improve Systematic Generalization in Grounded Question AnsweringCode1
CorefQA: Coreference Resolution as Query-based Span PredictionCode1
DocVQA: A Dataset for VQA on Document ImagesCode1
COVID-QA: A Question Answering Dataset for COVID-19Code1
Improving Multi-hop Question Answering over Knowledge Graphs using Knowledge Base EmbeddingsCode1
Transferability of Natural Language Inference to Biomedical Question AnsweringCode1
Ontology-guided Semantic Composition for Zero-Shot LearningCode1
Multi-Partition Embedding Interaction with Block Term Format for Knowledge Graph CompletionCode1
Answering Questions on COVID-19 in Real-TimeCode1
Graph Optimal Transport for Cross-Domain AlignmentCode1
ReCO: A Large Scale Chinese Reading Comprehension Dataset on OpinionCode1
A Qualitative Evaluation of Language Models on Automatic Question-Answering for COVID-19Code1
Sparse and Continuous Attention MechanismsCode1
Closed Loop Neural-Symbolic Learning via Integrating Neural Perception, Grammar Parsing, and Symbolic ReasoningCode1
Large-Scale Adversarial Training for Vision-and-Language Representation LearningCode1
ClarQ: A large-scale and diverse dataset for Clarification Question GenerationCode1
Roses Are Red, Violets Are Blue... but Should Vqa Expect Them To?Code1
Counterfactual VQA: A Cause-Effect Look at Language BiasCode1
Pre-training Polish Transformer-based Language Models at ScaleCode1
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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