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 6051–6100 of 10817 papers

TitleStatusHype
LiVLR: A Lightweight Visual-Linguistic Reasoning Framework for Video Question Answering—0
A Grounded Well-being Conversational Agent with Multiple Interaction Modes: Preliminary Results—0
Answer Generation for Questions With Multiple Information Sources in E-Commerce—0
A novel knowledge graph development for industry design: A case study on indirect coal liquefaction process—0
Tapping BERT for Preposition Sense Disambiguation—0
An Optimal Algorithm for Finding Champions in Tournament GraphsCode1
TunBERT: Pretrained Contextualized Text Representation for Tunisian Dialect—0
SwinBERT: End-to-End Transformers with Sparse Attention for Video CaptioningCode1
Scene Graph Generation with Geometric Context—0
VIOLET : End-to-End Video-Language Transformers with Masked Visual-token ModelingCode1
UniTAB: Unifying Text and Box Outputs for Grounded Vision-Language ModelingCode1
Many Heads but One Brain: Fusion Brain -- a Competition and a Single Multimodal Multitask ArchitectureCode1
Zero-Shot Open-Book Question AnsweringCode0
Teaching Humans When To Defer to a Classifier via ExemplarsCode1
A Confidence-Based Interface for Neuro-Symbolic Visual Question Answering—0
Textbook to triples: Creating knowledge graph in the form of triples from AI TextBook—0
Graph-augmented Learning to Rank for Querying Large-scale Knowledge Graph—0
Medical Visual Question Answering: A Survey—0
Building a Question Answering System for the Manufacturing Domain—0
UFO: A UniFied TransfOrmer for Vision-Language Representation Learning—0
Dynamic-TinyBERT: Boost TinyBERT's Inference Efficiency by Dynamic Sequence Length—0
DeBERTaV3: Improving DeBERTa using ELECTRA-Style Pre-Training with Gradient-Disentangled Embedding SharingCode2
Achieving Human Parity on Visual Question Answering—0
QA Domain Adaptation using Data Augmentation and Contrastive Adaptation—0
Flagging Comprehensibility Issues in Hindi Text with Question Answering—0
Fill-in-the-Blank: A Challenging Video Understanding Evaluation Framework—0
Fantastic Questions and Where to Find Them: FairytaleQA--An Authentic Dataset for Narrative Comprehension—0
Fair comparison of knowledge graphs for question answering—0
ArchivalQA: A Large-scale Benchmark Dataset for Open Domain Question Answering over Archival News Collections—0
Calibration of Machine Reading Systems at Scale—0
Retrieval Data Augmentation Informed by Downstream Question Answering Performance—0
Uncertainty-based Visual Question Answering: Estimating Semantic Inconsistency between Image and Knowledge Base—0
Enabling Multimodal Generation on CLIP via Vision-Language Knowledge Distillation—0
E-KAR: A Benchmark for Rationalizing Natural Language Analogical Reasoning—0
Probing Difficulty and Discrimination of Natural Language Questions With Item Response Theory—0
Using Interactive Feedback to Improve the Accuracy and Explainability of Question Answering Systems Post-Deployment—0
DrugEHRQA: A Question Answering Dataset on Structured and Unstructured Electronic Health Records For Medicine Related Queries—0
Delving Deep into Extractive Question Answering Data—0
Towards Faithful Response Generation for Chinese Table Question Answering—0
It is AI’s Turn to Ask Human a Question: Question and Answer Pair Generation for Children Storybooks in FairytaleQA Dataset—0
Cross-Task Generalization via Natural Language Crowdsourcing Instructions—0
Towards Transparent Interactive Semantic Parsing via Step-by-Step Correction—0
On the Multilingual Capabilities of Very Large-Scale English Language Models—0
CQARE: Contrastive Question-Answering for Few-shot Relation Extraction with Prompt Tuning—0
Co-VQA : Answering by Interactive Sub Question Sequence—0
Context-Paraphrase Enhanced Commonsense Question Answering—0
Unsupervised Open-Domain Question Answering with Higher Answerability—0
Modeling Multi-hop Question Answering as Single Sequence Prediction—0
Retrieval-guided Counterfactual Generation for QA—0
Get Your Model Puzzled: Introducing Crossword-Solving as a New NLP Benchmark—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