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 8501–8550 of 10817 papers

TitleStatusHype
Explicit Diversity Conditions for Effective Question Answer Generation with Large Language Models—0
Explicit Knowledge-based Reasoning for Visual Question Answering—0
Explicit Reasoning over End-to-End Neural Architectures for Visual Question Answering—0
Exploiting Attention to Reveal Shortcomings in Memory Models—0
Exploiting Bilingual Translation for Question Retrieval in Community-Based Question Answering—0
Exploiting Discourse Relations between Sentences for Text Clustering—0
Exploiting Hybrid Semantics of Relation Paths for Multi-hop Question Answering Over Knowledge Graphs—0
Exploiting Language Models as a Source of Knowledge for Cognitive Agents—0
Exploiting Language Models for Visual Recognition—0
GS-KGC: A Generative Subgraph-based Framework for Knowledge Graph Completion with Large Language Models—0
Exploiting Morphological Regularities in Distributional Word Representations—0
Exploiting Multiple Sources for Open-Domain Hypernym Discovery—0
Exploiting Numerical-Contextual Knowledge to Improve Numerical Reasoning in Question Answering—0
Exploiting Parallel News Streams for Unsupervised Event Extraction—0
Exploiting Portuguese Lexical Knowledge Bases for Answering Open Domain Cloze Questions Automatically—0
Exploiting Qualitative Information from Automatic Word Alignment for Cross-lingual NLP Tasks—0
Exploiting Rich Syntax for Better Knowledge Base Question Answering—0
Exploiting Sentence Embedding for Medical Question Answering—0
Exploiting Structured Knowledge in Text via Graph-Guided Representation Learning—0
Exploiting Structure in Representation of Named Entities using Active Learning—0
Exploiting the Human Computational Effort Dedicated to Message Reply Formatting for Training Discursive Email Segmenters—0
Exploiting Timegraphs in Temporal Relation Classification—0
Exploiting User Search Sessions for the Semantic Categorization of Question-like Informational Search Queries—0
Exploiting WordNet Synset and Hypernym Representations for Answer Selection—0
Explore before Moving: A Feasible Path Estimation and Memory Recalling Framework for Embodied Navigation—0
Explore Person Specific Evidence in Web Person Name Disambiguation—0
Explore the Hallucination on Low-level Perception for MLLMs—0
Explore then Determine: A GNN-LLM Synergy Framework for Reasoning over Knowledge Graph—0
Explore until Confident: Efficient Exploration for Embodied Question Answering—0
Exploring Advanced Techniques for Visual Question Answering: A Comprehensive Comparison—0
Exploring and Evaluating Personalized Models for Code Generation—0
Exploring BERT Parameter Efficiency on the Stanford Question Answering Dataset v2.0—0
Exploring Diverse Expressions for Paraphrase Generation—0
Exploring Diverse Methods in Visual Question Answering—0
Exploring Embedding Priors in Prompt-Tuning for Improved Interpretability and Control—0
Exploring Fine-grained Entity Type Constraints for Distantly Supervised Relation Extraction—0
Exploring Fluent Query Reformulations with Text-to-Text Transformers and Reinforcement Learning—0
Exploring Graph-structured Passage Representation for Multi-hop Reading Comprehension with Graph Neural Networks—0
Exploring Human-like Attention Supervision in Visual Question Answering—0
Exploring Hybrid Question Answering via Program-based Prompting—0
Exploring In-Context Learning Capabilities of Foundation Models for Generating Knowledge Graphs from Text—0
Exploring Jailbreak Attacks on LLMs through Intent Concealment and Diversion—0
Exploring Knowledge Boundaries in Large Language Models for Retrieval Judgment—0
Explicit Utilization of General Knowledge in Machine Reading Comprehension—0
Exploring Markov Logic Networks for Question Answering—0
Exploring Neural Net Augmentation to BERT for Question Answering on SQUAD 2.0—0
Exploring Question Decomposition for Zero-Shot VQA—0
Exploring Question Understanding and Adaptation in Neural-Network-Based Question Answering—0
Exploring Sparse Spatial Relation in Graph Inference for Text-Based VQA—0
Exploring Spatial Language Grounding Through Referring Expressions—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