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 7726–7750 of 10817 papers

TitleStatusHype
IslamicPCQA: A Dataset for Persian Multi-hop Complex Question Answering in Islamic Text Resources—0
Is It Dish Washer Safe? Automatically Answering ``Yes/No'' Questions Using Customer Reviews—0
Cut to the Chase: A Context Zoom-in Network for Reading Comprehension—0
Automated Graph Generation at Sentence Level for Reading Comprehension Based on Conceptual Graphs—0
Is Graph Structure Necessary for Multi-hop Question Answering?—0
Is GPT-3 all you need for Visual Question Answering in Cultural Heritage?—0
QCRI: Answer Selection for Community Question Answering - Experiments for Arabic and English—0
IS-GGT: Iterative Scene Graph Generation With Generative Transformers—0
ISCAS\_NLP at SemEval-2016 Task 1: Sentence Similarity Based on Support Vector Regression using Multiple Features—0
Curriculum Script Distillation for Multilingual Visual Question Answering—0
Is a Video worth n n Images? A Highly Efficient Approach to Transformer-based Video Question Answering—0
Is Aligning Embedding Spaces a Challenging Task? A Study on Heterogeneous Embedding Alignment Methods—0
ISAAQ - Mastering Textbook Questions with Pre-trained Transformers and Bottom-Up and Top-Down Attention—0
QirK: Question Answering via Intermediate Representation on Knowledge Graphs—0
QIRL: Boosting Visual Question Answering via Optimized Question-Image Relation Learning—0
Curriculum Learning for Compositional Visual Reasoning—0
Curriculum Learning Effectively Improves Low Data VQA—0
Automated Factual Benchmarking for In-Car Conversational Systems using Large Language Models—0
QnAMaker: Data to Bot in 2 Minutes—0
ISAAQ -- Mastering Textbook Questions with Pre-trained Transformers and Bottom-Up and Top-Down Attention—0
IrokoBench: A New Benchmark for African Languages in the Age of Large Language Models—0
QQATeam at Qur’an QA 2022: Fine-Tunning Arabic QA Models for Qur’an QA Task—0
QRMeM: Unleash the Length Limitation through Question then Reflection Memory Mechanism—0
Q-SFT: Q-Learning for Language Models via Supervised Fine-Tuning—0
Curriculum Learning Based on Reward Sparseness for Deep Reinforcement Learning of Task Completion Dialogue Management—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