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 8676–8700 of 10817 papers

TitleStatusHype
Out of the Box: Reasoning with Graph Convolution Nets for Factual Visual Question Answering—0
An Interactive Web-Interface for Visualizing the Inner Workings of the Question Answering LSTM—0
Explaining non-linear Classifier Decisions within Kernel-based Deep Architectures—0
Expletives in Universal Dependency TreebanksCode0
CogCompTime: A Tool for Understanding Time in Natural Language—0
Exploiting Attention to Reveal Shortcomings in Memory Models—0
Preferred Answer Selection in Stack Overflow: Better Text Representations ... and Metadata, Metadata, Metadata—0
OpenKE: An Open Toolkit for Knowledge EmbeddingCode0
Proceedings of the 6th BioASQ Workshop A challenge on large-scale biomedical semantic indexing and question answering—0
Automatic Opinion Question GenerationCode0
Ontology-Based Retrieval \& Neural Approaches for BioASQ Ideal Answer Generation—0
Extraction Meets Abstraction: Ideal Answer Generation for Biomedical Questions—0
Retrieve and Re-rank: A Simple and Effective IR Approach to Simple Question Answering over Knowledge Graphs—0
On the Generation of Medical Question-Answer Pairs—0
Interactive Instance-based Evaluation of Knowledge Base Question AnsweringCode0
An Adaption of BIOASQ Question Answering dataset for Machine Reading systems by Manual Annotations of Answer Spans.—0
Results of the sixth edition of the BioASQ Challenge—0
Macquarie University at BioASQ 6b: Deep learning and deep reinforcement learning for query-based summarisation—0
AttentionMeSH: Simple, Effective and Interpretable Automatic MeSH Indexer—0
Improving Machine Reading Comprehension with General Reading StrategiesCode0
On the Effectiveness of Minimal Context Selection for Robust Question Answering—0
Compositional Attention Networks for Interpretability in Natural Language Question Answering—0
ReviewQA: a relational aspect-based opinion reading dataset—0
Do Explanations make VQA Models more Predictable to a Human?—0
TallyQA: Answering Complex Counting QuestionsCode0
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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