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 7601–7625 of 10817 papers

TitleStatusHype
On the Importance of Adaptive Data Collection for Extremely Imbalanced Pairwise Tasks—0
Hierarchical Poset Decoding for Compositional Generalization in Language—0
A Graph Representation of Semi-structured Data for Web Question Answering—0
F1 is Not Enough! Models and Evaluation Towards User-Centered Explainable Question AnsweringCode0
Does my multimodal model learn cross-modal interactions? It's harder to tell than you might think!—0
A Wrong Answer or a Wrong Question? An Intricate Relationship between Question Reformulation and Answer Selection in Conversational Question AnsweringCode0
Neural, Symbolic and Neural-Symbolic Reasoning on Knowledge Graphs—0
End-to-End Synthetic Data Generation for Domain Adaptation of Question Answering SystemsCode0
BioMegatron: Larger Biomedical Domain Language Model—0
Towards Accurate and Reliable Energy Measurement of NLP ModelsCode0
On the Importance of Adaptive Data Collection for Extremely Imbalanced Pairwise TasksCode0
Localizing Open-Ontology QA Semantic Parsers in a Day Using Machine TranslationCode0
Beyond Language: Learning Commonsense from Images for ReasoningCode0
Interpretable Neural Computation for Real-World Compositional Visual Question Answering—0
Artificial Intelligence (AI) in Action: Addressing the COVID-19 Pandemic with Natural Language Processing (NLP)—0
Relation Classification as Two-way Span-Prediction—0
Characterizing Datasets for Social Visual Question Answering, and the New TinySocial Dataset—0
On the importance of pre-training data volume for compact language models—0
DiPair: Fast and Accurate Distillation for Trillion-Scale Text Matching and Pair Modeling—0
Learning a Cost-Effective Annotation Policy for Question AnsweringCode0
Unsupervised Evaluation for Question Answering with Transformers—0
Improving QA Generalization by Concurrent Modeling of Multiple BiasesCode0
Finding the Evidence: Localization-aware Answer Prediction for Text Visual Question Answering—0
Pathological Visual Question Answering—0
Multi-Fact Correction in Abstractive Text Summarization—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