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 8201–8250 of 10817 papers

TitleStatusHype
CFO: A Framework for Building Production NLP Systems—0
Learning Representations and Agents for Information Retrieval—0
Abductive Commonsense ReasoningCode0
Multi-class Hierarchical Question Classification for Multiple Choice Science ExamsCode0
Integrating Multimodal Information in Large Pretrained TransformersCode0
A Multi-Type Multi-Span Network for Reading Comprehension that Requires Discrete ReasoningCode0
Reasoning-Driven Question-Answering for Natural Language Understanding—0
Reactive Multi-Stage Feature Fusion for Multimodal Dialogue Modeling—0
VideoNavQA: Bridging the Gap between Visual and Embodied Question AnsweringCode1
Towards Optimisation of Collaborative Question Answering over Knowledge Graphs—0
SG-Net: Syntax-Guided Machine Reading ComprehensionCode0
Fusion of Detected Objects in Text for Visual Question Answering—0
StructBERT: Incorporating Language Structures into Pre-training for Deep Language Understanding—0
Variational Fusion for Multimodal Sentiment Analysis—0
Generative Question Refinement with Deep Reinforcement Learning in Retrieval-based QA SystemCode0
Why Does a Visual Question Have Different Answers?—0
AmazonQA: A Review-Based Question Answering TaskCode0
Multimodal Unified Attention Networks for Vision-and-Language Interactions—0
Multi-modality Latent Interaction Network for Visual Question Answering—0
A Generate-Validate Approach to Answering Questions about Qualitative Relationships—0
TEQUILA: Temporal Question Answering over Knowledge Bases—0
Question-Agnostic Attention for Visual Question Answering—0
CRIC: A VQA Dataset for Compositional Reasoning on Vision and Commonsense—0
Mitigating Noisy Inputs for Question Answering—0
ViLBERT: Pretraining Task-Agnostic Visiolinguistic Representations for Vision-and-Language TasksCode1
Answering Questions about Data Visualizations using Efficient Bimodal FusionCode0
Exploring Neural Net Augmentation to BERT for Question Answering on SQUAD 2.0—0
Extending Neural Question Answering with Linguistic Input Features—0
Simple and Effective Text Matching with Richer Alignment FeaturesCode0
ARS\_NITK at MEDIQA 2019:Analysing Various Methods for Natural Language Inference, Recognising Question Entailment and Medical Question Answering System—0
ChiMed: A Chinese Medical Corpus for Question AnsweringCode0
Overview of the MEDIQA 2019 Shared Task on Textual Inference, Question Entailment and Question AnsweringCode1
A Paraphrase Generation System for EHR Question Answering—0
ANU-CSIRO at MEDIQA 2019: Question Answering Using Deep Contextual Knowledge—0
DUT-NLP at MEDIQA 2019: An Adversarial Multi-Task Network to Jointly Model Recognizing Question Entailment and Question Answering—0
DUT-BIM at MEDIQA 2019: Utilizing Transformer Network and Medical Domain-Specific Contextualized Representations for Question Answering—0
LasigeBioTM at MEDIQA 2019: Biomedical Question Answering using Bidirectional Transformers and Named Entity Recognition—0
The Meaning of ``Most'' for Visual Question Answering Models—0
Curio SmartChat : A system for Natural Language Question Answering for Self-Paced K-12 Learning—0
IIT-KGP at MEDIQA 2019: Recognizing Question Entailment using Sci-BERT stacked with a Gradient Boosting Classifier—0
UU\_TAILS at MEDIQA 2019: Learning Textual Entailment in the Medical Domain—0
Predicting the Difficulty of Multiple Choice Questions in a High-stakes Medical Exam—0
Amharic Question Answering for Biography, Definition, and Description Questions—0
Learning Question-Guided Video Representation for Multi-Turn Video Question Answering—0
An Empirical Study of Batch Normalization and Group Normalization in Conditional Computation—0
LEAF-QA: Locate, Encode & Attend for Figure Question Answering—0
Reinforced Dynamic Reasoning for Conversational Question GenerationCode0
ERNIE 2.0: A Continual Pre-training Framework for Language UnderstandingCode3
A mathematical model for universal semanticsCode0
Text-to-SQL Generation for Question Answering on Electronic Medical RecordsCode0
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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