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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 8401–8425 of 10817 papers

TitleStatusHype
Generating clickbait spoilers with an ensemble of large language models—0
Ask Your Neurons: A Neural-based Approach to Answering Questions about Images—0
Generating Answering Patterns from Factoid Arabic Questions—0
Generative Relation Linking for Question Answering over Knowledge Bases—0
Combining Deep Neural Reranking and Unsupervised Extraction for Multi-Query Focused Summarization—0
Revisiting the Superficial Alignment Hypothesis—0
SAGAN: A Machine Translation Approach for Cross-Lingual Textual Entailment—0
SAGAN: An approach to Semantic Textual Similarity based on Textual Entailment—0
Revisiting the Primacy of English in Zero-shot Cross-lingual Transfer—0
SAIL: Search-Augmented Instruction Learning—0
Generating Answer Candidates for Quizzes and Answer-Aware Question Generators—0
Salience-Aware Event Chain Modeling for Narrative Understanding—0
SceMQA: A Scientific College Entrance Level Multimodal Question Answering Benchmark—0
SceneGATE: Scene-Graph based co-Attention networks for TExt visual question answering—0
Scene Graph Reasoning for Visual Question Answering—0
Revisiting the Open-Domain Question Answering Pipeline—0
Revisiting the Evaluation of Theory of Mind through Question Answering—0
Genie: Achieving Human Parity in Content-Grounded Datasets Generation—0
Generate, Transform, Answer: Question Specific Tool Synthesis for Tabular Data—0
Sample then Identify: A General Framework for Risk Control and Assessment in Multimodal Large Language Models—0
Samsung Poland NLP Team at SemEval-2016 Task 1: Necessity for diversity; combining recursive autoencoders, WordNet and ensemble methods to measure semantic similarity.—0
Samsung Research Poland (SRPOL) at SemEval-2022 Task 9: Hybrid Question Answering Using Semantic Roles—0
Revisiting the Evaluation for Cross Document Event Coreference—0
Samvaadhana: A Telugu Dialogue System in Hospital Domain—0
Generate then Select: Open-ended Visual Question Answering Guided by World Knowledge—0
Show:102550
← PrevPage 337 of 433Next →
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