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 8751–8800 of 10817 papers

TitleStatusHype
Learning to Inference Adaptively for Multimodal Large Language Models—0
Learning to Initialize: Can Meta Learning Improve Cross-task Generalization in Prompt Tuning?—0
Learning to Interactively Learn and Assist—0
Learning to Jointly Predict Ellipsis and Comparison Structures—0
Learning to Make Inferences in a Semantic Parsing Task—0
Learning to Map Natural Language Statements into Knowledge Base Representations for Knowledge Base Construction—0
Learning to Model Multilingual Unrestricted Coreference in OntoNotes—0
Learning to Navigate Wikipedia by Taking Random Walks—0
Learning to Order Natural Language Texts—0
Learning to Order Sub-questions for Complex Question Answering—0
Learning to Organize Knowledge and Answer Questions with N-Gram Machines—0
LEARNING TO ORGANIZE KNOWLEDGE WITH N-GRAM MACHINES—0
Learning to Paraphrase for Question Answering—0
Learning to Plan and Generate Text with Citations—0
LearningToQuestion at SemEval 2017 Task 3: Ranking Similar Questions by Learning to Rank Using Rich Features—0
Learning to Rank Answer Candidates for Automatic Resolution of Crossword Puzzles—0
Learning to Reason Iteratively and Parallelly for Complex Visual Reasoning Scenarios—0
Learning to Reason Over Time: Timeline Self-Reflection for Improved Temporal Reasoning in Language Models—0
Neural Reasoning, Fast and Slow, for Video Question Answering—0
Learning to Recognize the Unseen Visual Predicates—0
Learning to Recover Reasoning Chains for Multi-Hop Question Answering via Cooperative Games—0
Learning to Rehearse in Long Sequence Memorization—0
Learning to Selectively Transfer: Reinforced Transfer Learning for Deep Text Matching—0
Learning to Select Question-Relevant Relations for Visual Question Answering—0
Learning to Select the Relevant History Turns in Conversational Question Answering—0
Learning to Solve Geometry Problems from Natural Language Demonstrations in Textbooks—0
Learning to Specialize with Knowledge Distillation for Visual Question Answering—0
Learning to Summarise Related Sentences—0
Learning to Summarize and Answer Questions about a Virtual Robot's Past Actions—0
Learning to Temporally Order Medical Events in Clinical Text—0
Learning to Translate for Multilingual Question Answering—0
Learning to Transpile AMR into SPARQL—0
Learning to Transpile AMR into SPARQL—0
Learning Trajectory-Word Alignments for Video-Language Tasks—0
Learning Transferable Representation for Bilingual Relation Extraction via Convolutional Neural Networks—0
Learning Verb Inference Rules from Linguistically-Motivated Evidence—0
Learning Visual Knowledge Memory Networks for Visual Question Answering—0
Learning What Makes a Difference from Counterfactual Examples and Gradient Supervision—0
Learning What to Remember: Long-term Episodic Memory Networks for Learning from Streaming Data—0
Learning When to Retrieve, What to Rewrite, and How to Respond in Conversational QA—0
Learning with Limited Data for Multilingual Reading Comprehension—0
Learning Word Embeddings for Low-Resource Languages by PU Learning—0
Learning Word Representations by Jointly Modeling Syntagmatic and Paradigmatic Relations—0
Learn to Refuse: Making Large Language Models More Controllable and Reliable through Knowledge Scope Limitation and Refusal Mechanism—0
Learn to Unlearn: Meta-Learning-Based Knowledge Graph Embedding Unlearning—0
Least-to-Most Prompting Enables Complex Reasoning in Large Language Models—0
Legal Question Answering using Ranking SVM and Deep Convolutional Neural Network—0
LegalRAG: A Hybrid RAG System for Multilingual Legal Information Retrieval—0
Lego: Learning to Disentangle and Invert Personalized Concepts Beyond Object Appearance in Text-to-Image Diffusion Models—0
LEGO-Puzzles: How Good Are MLLMs at Multi-Step Spatial Reasoning?—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