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 7701–7750 of 10817 papers

TitleStatusHype
Fine-Grained Relevance Annotations for Multi-Task Document Ranking and Question AnsweringCode0
Assisting Scene Graph Generation with Self-Supervision—0
Interpretable Multi-Step Reasoning with Knowledge Extraction on Complex Healthcare Question Answering—0
Effective Transfer Learning for Identifying Similar Questions: Matching User Questions to COVID-19 FAQs—0
Video Question Answering on Screencast Tutorials—0
Multi-Agent Embodied Question Answering in Interactive Environments—0
Interpretable Visual Reasoning via Probabilistic Formulation under Natural Supervision—0
TRRNet: Tiered Relation Reasoning for Compositional Visual Question Answering—0
Domain-Specific Language Model Pretraining for Biomedical Natural Language ProcessingCode0
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
AWS CORD-19 Search: A Neural Search Engine for COVID-19 Literature—0
Template-Based Question Answering over Linked Geospatial Data—0
Using Holographically Compressed Embeddings in Question Answering—0
Reducing Language Biases in Visual Question Answering with Visually-Grounded Question Encoder—0
Applying recent advances in Visual Question Answering to Record LinkageCode0
What Can We Learn From Almost a Decade of Food TweetsCode0
Image Captioning with Compositional Neural Module Networks—0
Less is More: Rejecting Unreliable Reviews for Product Question AnsweringCode0
IQ-VQA: Intelligent Visual Question AnsweringCode0
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
Visual Question Answering as a Multi-Task Problem—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
Facts as Experts: Adaptable and Interpretable Neural Memory over Symbolic Knowledge—0
Scene Graph Reasoning for Visual Question Answering—0
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
Aligned Dual Channel Graph Convolutional Network for Visual Question Answering—0
Open-Domain Question Answering—0
On the Importance of Diversity in Question Generation for QA—0
Evaluating and Enhancing the Robustness of Neural Network-based Dependency Parsing Models with Adversarial Examples—0
Multi-source Meta Transfer for Low Resource Multiple-Choice Question Answering—0
Multimodal Neural Graph Memory Networks for Visual Question Answering—0
SimsterQ: A Similarity based Clustering Approach to Opinion Question Answering—0
Answering Complex Questions by Combining Information from Curated and Extracted Knowledge Bases—0
DoQA - Accessing Domain-Specific FAQs via Conversational QA—0
Learning to Identify Follow-Up Questions in Conversational Question Answering—0
Developing a How-to Tip Machine Comprehension Dataset and its Evaluation in Machine Comprehension by BERT—0
COVID-19 Literature Knowledge Graph Construction and Drug Repurposing Report Generation—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