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 5751–5800 of 10817 papers

TitleStatusHype
ChartQA: A Benchmark for Question Answering about Charts with Visual and Logical ReasoningCode2
Sequence-to-Sequence Knowledge Graph Completion and Question AnsweringCode1
Are You Robert or RoBERTa? Deceiving Online Authorship Attribution Models Using Neural Text Generators—0
Simulating Bandit Learning from User Feedback for Extractive Question AnsweringCode1
DP-KB: Data Programming with Knowledge Bases Improves Transformer Fine Tuning for Answer Sentence Selection—0
elBERto: Self-supervised Commonsense Learning for Question Answering—0
Knowledge Graph Embedding Methods for Entity Alignment: An Experimental ReviewCode1
MuKEA: Multimodal Knowledge Extraction and Accumulation for Knowledge-based Visual Question AnsweringCode1
Ask to Understand: Question Generation for Multi-hop Question Answering—0
A New Quantum CNN Model for Image Classification—0
C-MORE: Pretraining to Answer Open-Domain Questions by Consulting Millions of ReferencesCode0
Synthetic Question Value Estimation for Domain Adaptation of Question AnsweringCode1
E-KAR: A Benchmark for Rationalizing Natural Language Analogical Reasoning—0
Training Data is More Valuable than You Think: A Simple and Effective Method by Retrieving from Training DataCode1
Can you even tell left from right? Presenting a new challenge for VQA—0
Revitalize Region Feature for Democratizing Video-Language Pre-training of RetrievalCode1
Generalized but not Robust? Comparing the Effects of Data Modification Methods on Out-of-Domain Generalization and Adversarial Robustness—0
Reasoning over Public and Private Data in Retrieval-Based SystemsCode1
Choose Your QA Model Wisely: A Systematic Study of Generative and Extractive Readers for Question Answering—0
ScienceWorld: Is your Agent Smarter than a 5th Grader?Code2
Hyperlink-induced Pre-training for Passage Retrieval in Open-domain Question AnsweringCode1
Towards Semantic Search for Community Question Answering for Mortgage Officers—0
All in One: Exploring Unified Video-Language Pre-trainingCode2
CLIP Models are Few-shot Learners: Empirical Studies on VQA and Visual Entailment—0
Efficient Language Modeling with Sparse all-MLP—0
Uncertainty-Aware Text-to-Program for Question Answering on Structured Electronic Health RecordsCode0
Towards Visual-Prompt Temporal Answering Grounding in Medical Instructional Video—0
Enabling Multimodal Generation on CLIP via Vision-Language Knowledge Distillation—0
Cross-lingual Inference with A Chinese Entailment GraphCode0
QDrop: Randomly Dropping Quantization for Extremely Low-bit Post-Training QuantizationCode2
Internet-augmented language models through few-shot prompting for open-domain question answering—0
DUAL: Discrete Spoken Unit Adaptive Learning for Textless Spoken Question AnsweringCode1
LEMON: LanguagE ModeL for Negative Sampling of Knowledge Graph Embeddings—0
Barlow constrained optimization for Visual Question AnsweringCode0
IT5: Text-to-text Pretraining for Italian Language Understanding and GenerationCode1
Dynamic Key-value Memory Enhanced Multi-step Graph Reasoning for Knowledge-based Visual Question AnsweringCode0
Modeling Coreference Relations in Visual Dialog—0
Divide and Conquer: Text Semantic Matching with Disentangled Keywords and IntentsCode1
Training language models to follow instructions with human feedbackCode6
QaNER: Prompting Question Answering Models for Few-shot Named Entity RecognitionCode1
Recent, rapid advancement in visual question answering architecture: a review—0
Video Question Answering: Datasets, Algorithms and ChallengesCode1
Semantic Sentence Composition Reasoning for Multi-Hop Question Answering—0
Read before Generate! Faithful Long Form Question Answering with Machine Reading—0
Improving Time Sensitivity for Question Answering over Temporal Knowledge Graphs—0
Improving Lexical Embeddings for Robust Question Answering—0
'Tis but Thy Name: Semantic Question Answering Evaluation with 11M Names for 1M Entities—0
Subgraph Retrieval Enhanced Model for Multi-hop Knowledge Base Question AnsweringCode1
A Generative Model for Relation Extraction and Classification—0
Joint Answering and Explanation for Visual Commonsense ReasoningCode0
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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