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 5876–5900 of 10817 papers

TitleStatusHype
FORTAP: Using Formulas for Numerical-Reasoning-Aware Table Pretraining—0
FedNLP: Benchmarking Federated Learning Methods for Natural Language Processing Tasks—0
FAQ Search using Transformers—0
ERNIE-Layout: Layout-Knowledge Enhanced Multi-modal Pre-training for Document UnderstandingCode0
Dynamic Relevance Graph Network for Knowledge-Aware Question Answering—0
Retrieval Enhanced Data Augmentation for Question Answering on Privacy Policies—0
Interpretable Proof Generation via Iterative Backward Reasoning—0
On Measuring Social Biases in Prompt-Based Learning—0
XLTime: A Cross-Lingual Knowledge Transfer Framework for Temporal Expression Extraction—0
Tapping BERT for Preposition Sense Disambiguation—0
Modeling Exemplification in Long-form Question Answering via Retrieval—0
How do QA models combine knowledge from LM and 100 passages?—0
Consecutive Question Generation with Multitask Joint Reranking and Dynamic Rationale Search—0
All You May Need for VQA are Image Captions—0
Framework for Weakly Supervised Causal Knowledge Extraction from Text—0
CL-ReKD: Cross-lingual Knowledge Distillation for Multilingual Retrieval Question Answering—0
Fine-tuning Strategies for Domain Specific Question Answering under Low Annotation Budget Constraints—0
Causal Distillation for Language Models—0
Mosaic Augmentation for Text: Cropping and Collaging as Cross-Domain Techniques—0
Evidentiality-guided Generation for Knowledge-Intensive NLP Tasks—0
MANGO: Enhancing the Robustness of VQA Models via Adversarial Noise Generation—0
StoryQA: Story Grounded Question Answering Dataset—0
MixQG: Neural Question Generation with Mixed Answer Types—0
Re2G: Retrieve, Rerank, Generate—0
Probing the Role of Positional Information in Vision-Language Models—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