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 6576–6600 of 10817 papers

TitleStatusHype
MetaQA: Combining Expert Agents for Multi-Skill Question Answering—0
MetaICL: Learning to Learn In Context—0
Dynamic Relevance Graph Network for Knowledge-Aware Question Answering—0
MANGO: Enhancing the Robustness of VQA Models via Adversarial Noise Generation—0
DUAL: Textless Spoken Question Answering with Speech Discrete Unit Adaptive Learning—0
Logical Story Representations via FrameNet + Semantic Parsing—0
Double Retrieval and Ranking for Accurate Question Answering—0
Divide and Conquer: Text Semantic Matching with Disentangled Keywords and Intents—0
Learning to Transpile AMR into SPARQL—0
Disaggregating Hops: Can We Guide a Multi-Hop Reasoning Language Model to Incrementally Learn at each Hop?—0
Bayesian Deep Learning for Interactive Community Question Answering—0
StoryQA: Story Grounded Question Answering Dataset—0
Probing the Role of Positional Information in Vision-Language Models—0
An Encoder Attribution Analysis for Dense Passage Retriever in Open-Domain Question Answering—0
Knowledge Base Index Compression via Dimensionality and Precision Reduction—0
JointLK: Joint Reasoning with Language Models and Knowledge Graphs for Commonsense Question Answering—0
Data Augmentation for Biomedical Factoid Question Answering—0
Interpretable Proof Generation via Iterative Backward Reasoning—0
Table Retrieval Does Not Necessitate Table-specific Model Design—0
In Situ Answer Sentence Selection at Web-scale—0
A Two-Stage Approach towards Generalization in Knowledge Base Question Answering—0
Incorporating Question Answering-Based Signals into Abstractive Summarization via Salient Span Selection—0
Improving Data Augmentation in Low-resource Question Answering with Active Learning in Multiple Stages—0
Improved and Efficient Conversational Slot Labeling through Question Answering—0
Cooperative Self-training of Machine Reading Comprehension—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