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 4676–4700 of 10817 papers

TitleStatusHype
Automated Answer Validation using Text Similarity—0
An Audio-enriched BERT-based Framework for Spoken Multiple-choice Question Answering—0
CSS: Combining Self-training and Self-supervised Learning for Few-shot Dialogue State Tracking—0
CSReader at SemEval-2018 Task 11: Multiple Choice Question Answering as Textual Entailment—0
AutoKnow: Self-Driving Knowledge Collection for Products of Thousands of Types—0
A Domain and Language Independent Named Entity Classification Approach Based on Profiles and Local Information—0
CS-NLP team at SemEval-2020 Task 4: Evaluation of State-of-the-art NLP Deep Learning Architectures on Commonsense Reasoning Task—0
AUTOHOME-ORCA at SemEval-2019 Task 8: Application of BERT for Fact-Checking in Community Forums—0
CSE-SFP: Enabling Unsupervised Sentence Representation Learning via a Single Forward Pass—0
CSAT‑FTCN: A Fuzzy‑Oriented Model with Contextual Self‑attention Network for Multimodal Emotion Recognition—0
A Natural Language Instructor for pedestrian navigation based in generation by selection—0
Investigating Data Contamination in Modern Benchmarks for Large Language Models—0
Investigating Energy Efficiency and Performance Trade-offs in LLM Inference Across Tasks and DVFS Settings—0
IPFormer-VideoLLM: Enhancing Multi-modal Video Understanding for Multi-shot Scenes—0
CS563-QA: A Collection for Evaluating Question Answering Systems—0
An Attentive Sequence Model for Adverse Drug Event Extraction from Biomedical Text—0
Crowdsourcing Inference-Rule Evaluation—0
Crowdsourcing for the identification of event nominals: an experiment—0
AutoEQA: Auto-Encoding Questions for Extractive Question Answering—0
Crowdsourcing for (almost) Real-time Question Answering—0
CROWD-IN-THE-LOOP: A Hybrid Approach for Annotating Semantic Roles—0
AutoDrive-QA- Automated Generation of Multiple-Choice Questions for Autonomous Driving Datasets Using Large Vision-Language Models—0
An Attention Mechanism for Answer Selection Using a Combined Global and Local View—0
ADMUS: A Progressive Question Answering Framework Adaptable to Multiple Knowledge Sources—0
CrossVQA: Scalably Generating Benchmarks for Systematically Testing VQA Generalization—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