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 6101–6150 of 10817 papers

TitleStatusHype
Human Attention in Visual Question Answering: Do Humans and Deep Networks Look at the Same Regions?—0
MC\^2: Multi-perspective Convolutional Cube for Conversational Machine Reading Comprehension—0
Human Attention in Visual Question Answering: Do Humans and Deep Networks Look at the Same Regions?—0
A Survey on Why-Type Question Answering Systems—0
Human-Adversarial Visual Question Answering—0
Human Adversarial QA: Did the Model Understand the Paragraph?—0
Huge Automatically Extracted Training-Sets for Multilingual Word SenseDisambiguation—0
MCQA: Multimodal Co-attention Based Network for Question Answering—0
MCR-Net: A Multi-Step Co-Interactive Relation Network for Unanswerable Questions on Machine Reading Comprehension—0
MCSFF: Multi-modal Consistency and Specificity Fusion Framework for Entity Alignment—0
MCTS-KBQA: Monte Carlo Tree Search for Knowledge Base Question Answering—0
E3D-GPT: Enhanced 3D Visual Foundation for Medical Vision-Language Model—0
Contrastive Data and Learning for Natural Language Processing—0
A survey on VQA_Datasets and Approaches—0
Mitigating Knowledge Conflicts in Language Model-Driven Question Answering—0
HRVQA: A Visual Question Answering Benchmark for High-Resolution Aerial Images—0
EACO: Enhancing Alignment in Multimodal LLMs via Critical Observation—0
Meaningful Answer Generation of E-Commerce Question-Answering—0
HRoT: Hybrid prompt strategy and Retrieval of Thought for Table-Text Hybrid Question Answering—0
Measuring an Artificial Intelligence System's Performance on a Verbal IQ Test For Young Children—0
Contrastive Cross-Modal Knowledge Sharing Pre-training for Vision-Language Representation Learning and Retrieval—0
Biomedical Question Answering: A Survey of Approaches and Challenges—0
Mitigating Clickbait: An Approach to Spoiler Generation Using Multitask Learning—0
Measuring CLEVRness: Black-box Testing of Visual Reasoning Models—0
Measuring CLEVRness: Blackbox testing of Visual Reasoning Models—0
Measuring Compositional Consistency for Video Question Answering—0
HRCA+: Advanced Multiple-choice Machine Reading Comprehension Method—0
HPI Question Answering System in BioASQ 2016—0
Measuring Domain Portability and ErrorPropagation in Biomedical QA—0
Biomedical Question Answering via Weighted Neural Network Passage Retrieval—0
A Survey on Table Question Answering: Recent Advances—0
How You Ask Matters: The Effect of Paraphrastic Questions to BERT Performance on a Clinical SQuAD Dataset—0
Addressing Semantic Drift in Generative Question Answering with Auxiliary Extraction—0
Measuring Popularity of Machine-Generated Sentences Using Term Count, Document Frequency, and Dependency Language Model—0
Mitigating Bias for Question Answering Models by Tracking Bias Influence—0
Measuring Retrieval Complexity in Question Answering Systems—0
Mitigating Hallucination in Visual-Language Models via Re-Balancing Contrastive Decoding—0
Measuring Sentences Similarity: A Survey—0
Mitigating Large Language Model Hallucination with Faithful Finetuning—0
Measuring the Limit of Semantic Divergence for English Tweets.—0
MEBench: Benchmarking Large Language Models for Cross-Document Multi-Entity Question Answering—0
Mitigating Lost-in-Retrieval Problems in Retrieval Augmented Multi-Hop Question Answering—0
Continuous Training and Fine-tuning for Domain-Specific Language Models in Medical Question Answering—0
How well do Computers Solve Math Word Problems? Large-Scale Dataset Construction and Evaluation—0
A Survey on Table-and-Text HybridQA: Concepts, Methods, Challenges and Future Directions—0
How Well can We Learn Interpretable Entity Types from Text?—0
How Well Can Vison-Language Models Understand Humans' Intention? An Open-ended Theory of Mind Question Evaluation Benchmark—0
How Vision-Language Tasks Benefit from Large Pre-trained Models: A Survey—0
Echo-Attention: Attend Once and Get N Attentions for Free—0
How Transferable are Reasoning Patterns in VQA?—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