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 6701–6750 of 10817 papers

TitleStatusHype
Medical Visual Question Answering: A Survey—0
UFO: A UniFied TransfOrmer for Vision-Language Representation Learning—0
Dynamic-TinyBERT: Boost TinyBERT's Inference Efficiency by Dynamic Sequence Length—0
Achieving Human Parity on Visual Question Answering—0
Flagging Comprehensibility Issues in Hindi Text with Question Answering—0
Fill-in-the-Blank: A Challenging Video Understanding Evaluation Framework—0
Open Domain Question Answering over Virtual Documents: A Unified Approach for Data and Text—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
Training Data is More Valuable than You Think: A Simple and Effective Method by Retrieving from Training Data—0
Uncertainty-based Visual Question Answering: Estimating Semantic Inconsistency between Image and Knowledge Base—0
ARCNN: A Semantic Enhanced Relation Detection Model for Knowledge Base Question Answering—0
CCQA: A New Web-Scale Question Answering Dataset for Model Pre-Training—0
ArchivalQA: A Large-scale Benchmark Dataset for Open Domain Question Answering over Archival News Collections—0
Retrieval-guided Counterfactual Generation for QA—0
Event Detection via Derangement Question Answering—0
Can Explanations Be Useful for Calibrating Black Box Models?—0
Unsupervised multiple-choice question generation for out-of-domain Q\&A fine-tuning—0
Calibration of Machine Reading Systems at Scale—0
QA4PRF: A Question Answering based Framework for Pseudo Relevance Feedback—0
QA Domain Adaptation using Data Augmentation and Contrastive Adaptation—0
Enabling Multimodal Generation on CLIP via Vision-Language Knowledge Distillation—0
Modeling Multi-hop Question Answering as Single Sequence Prediction—0
Breaking Down Questions for Outside-Knowledge Visual Question Answering—0
E-KAR: A Benchmark for Rationalizing Natural Language Analogical Reasoning—0
ViQuAE, a Dataset for Knowledge-based Visual Question Answering about Named EntitiesCode0
MarCQAp: Effective Context Modeling for Conversational Question Answering—0
Salient Phrase Aware Dense Retrieval: Can a Dense Retriever Imitate a Sparse One?—0
Unsupervised Open-Domain Question Answering with Higher Answerability—0
DrugEHRQA: A Question Answering Dataset on Structured and Unstructured Electronic Health Records For Medicine Related Queries—0
Do We Need to Differentiate Negative Candidates Before Training a Neural Ranker?—0
XLTime: A Cross-Lingual Knowledge Transfer Framework for Zero-Shot Low-Resource Language Temporal Expression Extraction—0
On the Multilingual Capabilities of Very Large-Scale English Language Models—0
Reasoning over Hybrid Chain for Table-and-Text Open Domain Question Answering—0
Lifting the Curse of Multilinguality by Pre-training Modular Transformers—0
Question Answering Infused Pre-training of General-Purpose Contextualized Representations—0
ANNA: Enhanced Language Representation for Question Answering—0
Document AI: Benchmarks, Models and Applications—0
Question-Led Semantic Structure Enhanced Attentions for VQA—0
Retrieval Data Augmentation Informed by Downstream Question Answering Performance—0
TABi: Type-Aware Bi-encoders for End-to-End Entity Retrieval—0
Parameter-Efficient Abstractive Question Answering over Tables and over Text—0
A Neural Approach to KGQA via SPARQL Silhouette Generation—0
Language Model-Guided Knowledge Subgraphs for Question Answering—0
Language bias in Visual Question Answering: A Survey and Taxonomy—0
Delving Deep into Extractive Question Answering Data—0
Probing Difficulty and Discrimination of Natural Language Questions With Item Response Theory—0
Towards Faithful Response Generation for Chinese Table Question Answering—0
Synthetic Question Value Estimation for Domain Adaptation of Question Answering—0
KALA: Knowledge-Augmented Language Model Adaptation—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