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 6251–6275 of 10817 papers

TitleStatusHype
Improving Pre-trained Language Model Fine-tuning with Noise Stability Regularization—0
Less Is More: Linear Layers on CLIP Features as Powerful VizWiz Model—0
Few-shot Question Generation for Personalized Feedback in Intelligent Tutoring Systems—0
Explanation as Question Answering based on a Task Model of the Agent's Design—0
Enhancing Dual-Encoders with Question and Answer Cross-Embeddings for Answer Retrieval—0
cViL: Cross-Lingual Training of Vision-Language Models using Knowledge DistillationCode0
Investigating the use of Paraphrase Generation for Question Reformulation in the FRANK QA system—0
From Pixels to Objects: Cubic Visual Attention for Visual Question Answering—0
QAGCN: Answering Multi-Relation Questions via Single-Step Implicit Reasoning over Knowledge GraphsCode0
Structured Two-stream Attention Network for Video Question Answering—0
VL-BEiT: Generative Vision-Language Pretraining—0
FQuAD2.0: French Question Answering and Learning When You Don’t Know—0
FinMath: Injecting a Tree-structured Solver for Question Answering over Financial Reports—0
Fine-tuning vs From Scratch: Do Vision & Language Models Have Similar Capabilities on Out-of-Distribution Visual Question Answering?—0
Argument Similarity Assessment in German for Intelligent Tutoring: Crowdsourced Dataset and First Experiments—0
SAMPLE-HD: Simultaneous Action and Motion Planning Learning Environment—0
Multilingual Image Corpus – Towards a Multimodal and Multilingual Dataset—0
Natural Questions in Icelandic—0
eRock at Qur’an QA 2022: Contemporary Deep Neural Networks for Qur’an based Reading Comprehension Question Answers—0
Building a Dataset for Automatically Learning to Detect Questions Requiring Clarification—0
Elvis vs. M. Jackson: Who has More Albums? Classification and Identification of Elements in Comparative Questions—0
Russian Jeopardy! Data Set for Question-Answering SystemsCode0
RadQA: A Question Answering Dataset to Improve Comprehension of Radiology Reports—0
DTW at Qur’an QA 2022: Utilising Transfer Learning with Transformers for Question Answering in a Low-resource DomainCode0
Un jeu de données pour répondre à des questions visuelles à propos d’entités nommées en utilisant des bases de connaissances (ViQuAE, a Dataset for Knowledge-based Visual Question Answering about Named Entities)—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