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 7451–7500 of 10817 papers

TitleStatusHype
Video Question Answering on Screencast Tutorials—0
Multi-Agent Embodied Question Answering in Interactive Environments—0
TRRNet: Tiered Relation Reasoning for Compositional Visual Question Answering—0
Interpretable Visual Reasoning via Probabilistic Formulation under Natural Supervision—0
Trojaning Language Models for Fun and ProfitCode1
Domain-Specific Language Model Pretraining for Biomedical Natural Language ProcessingCode0
NeuralQA: A Usable Library for Question Answering (Contextual Query Expansion + BERT) on Large DatasetsCode1
MKQA: A Linguistically Diverse Benchmark for Multilingual Open Domain Question AnsweringCode1
Big Bird: Transformers for Longer SequencesCode1
REXUP: I REason, I EXtract, I UPdate with Structured Compositional Reasoning for Visual Question AnsweringCode0
A Survey on Complex Question Answering over Knowledge Base: Recent Advances and Challenges—0
A Survey on Graph Neural Networks for Knowledge Graph Completion—0
Better Early than Late: Fusing Topics with Word Embeddings for Neural Question Paraphrase Identification—0
Distributed Associative Memory Network with Memory Refreshing LossCode0
Frustratingly Hard Evidence Retrieval for QA Over Books—0
Multimodal Dialogue State Tracking By QA Approach with Data Augmentation—0
Semantic Equivalent Adversarial Data Augmentation for Visual Question AnsweringCode1
AWS CORD-19 Search: A Neural Search Engine for COVID-19 Literature—0
Visual Relation Grounding in VideosCode1
Learning to Discretely Compose Reasoning Module Networks for Video CaptioningCode1
Knowledge-Based Video Question Answering with Unsupervised Scene DescriptionsCode1
Using Holographically Compressed Embeddings in Question Answering—0
Template-Based Question Answering over Linked Geospatial Data—0
Reducing Language Biases in Visual Question Answering with Visually-Grounded Question Encoder—0
Applying recent advances in Visual Question Answering to Record LinkageCode0
Image Captioning with Compositional Neural Module Networks—0
What Can We Learn From Almost a Decade of Food TweetsCode0
Less is More: Rejecting Unreliable Reviews for Product Question AnsweringCode0
Advances of Transformer-Based Models for News Headline GenerationCode1
IQ-VQA: Intelligent Visual Question AnsweringCode0
KQA Pro: A Dataset with Explicit Compositional Programs for Complex Question Answering over Knowledge BaseCode1
What Gives the Answer Away? Question Answering Bias Analysis on Video QA Datasets—0
Auto-captions on GIF: A Large-scale Video-sentence Dataset for Vision-language Pre-training—0
Modality Shifting Attention Network for Multi-modal Video Question Answering—0
Text Data Augmentation: Towards better detection of spear-phishing emails—0
Eliminating Catastrophic Interference with Biased Competition—0
El Departamento de Nosotros: How Machine Translated Corpora Affects Language Models in MRC TasksCode0
A Competence-aware Curriculum for Visual Concepts Learning via Question Answering—0
Visual Question Answering as a Multi-Task Problem—0
Facts as Experts: Adaptable and Interpretable Neural Memory over Symbolic Knowledge—0
Scene Graph Reasoning for Visual Question Answering—0
Leveraging Passage Retrieval with Generative Models for Open Domain Question AnsweringCode1
The Impact of Explanations on AI Competency Prediction in VQA—0
Detecting Ongoing Events Using Contextual Word and Sentence Embeddings—0
IIE-NLP-NUT at SemEval-2020 Task 4: Guiding PLM with Prompt Template Reconstruction Strategy for ComVE—0
Project PIAF: Building a Native French Question-Answering DatasetCode1
COVID-QA: A Question Answering Dataset for COVID-19Code1
How Self-Attention Improves Rare Class Performance in a Question-Answering Dialogue Agent—0
Asking Effective and Diverse Questions: A Machine Reading Comprehension based Framework for Joint Entity-Relation ExtractionCode1
Relevance-guided Supervision for OpenQA with ColBERTCode2
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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