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Extractive Question-Answering

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TitleStatusHype
longhorns at DADC 2022: How many linguists does it take to fool a Question Answering model? A systematic approach to adversarial attacks.—0
longhorns at DADC 2022: How many linguists does it take to fool a Question Answering model? A systematic approach to adversarial attacks—0
Stars at Qur’an QA 2022: Building Automatic Extractive Question Answering Systems for the Holy Qur’an with Transformer Models and Releasing a New Dataset—0
Natural Questions in Icelandic—0
KECP: Knowledge Enhanced Contrastive Prompting for Few-shot Extractive Question AnsweringCode0
Science Checker: Extractive-Boolean Question Answering For Scientific Fact CheckingCode0
XQA-DST: Multi-Domain and Multi-Lingual Dialogue State TrackingCode0
Breaking Character: Are Subwords Good Enough for MRLs After All?—0
Improving Zero-Shot Event Extraction via Sentence Simplification—0
Simulating Bandit Learning from User Feedback for Extractive Question AnsweringCode1
Breaking Character: Are Subwords Good Enough for MRLs After All?—0
Mosaic Augmentation for Text: Cropping and Collaging as Cross-Domain Techniques—0
Cooperative Self-training of Machine Reading Comprehension—0
Development of an Extractive Clinical Question Answering Dataset with Multi-Answer and Multi-Focus Questions—0
Models in the Loop: Aiding Crowdworkers with Generative Annotation Assistants—0
Block-Skim: Efficient Question Answering for TransformerCode0
Semantic Search as Extractive Paraphrase Span DetectionCode0
On the Multilingual Capabilities of Very Large-Scale English Language Models—0
Delving Deep into Extractive Question Answering Data—0
Can Explanations Be Useful for Calibrating Black Box Models?—0
Simulating Bandit Learning from User Feedback for Extractive Question Answering—0
AutoEQA: Auto-Encoding Questions for Extractive Question Answering—0
Semantic Search as Extractive Paraphrase Span Detection—0
Cooperative Semi-Supervised Transfer Learning of Machine Reading Comprehension—0
Can Explanations Be Useful for Calibrating Black Box Models?Code1
Attention-guided Generative Models for Extractive Question Answering—0
A Few More Examples May Be Worth Billions of ParametersCode1
A Comparative Study of Transformer-Based Language Models on Extractive Question Answering—0
ReasonBERT: Pre-trained to Reason with Distant SupervisionCode1
On the Multilingual Capabilities of Very Large-Scale English Language ModelsCode0
How Optimal is Greedy Decoding for Extractive Question Answering?Code1
Multi-modal Retrieval of Tables and Texts Using Tri-encoder Models—0
A Robustly Optimized BERT Pre-training Approach with Post-training—0
MarIA: Spanish Language ModelsCode1
ReasonBert: Pre-trained to Reason with Distant Supervision—0
Efficient Retrieval Optimized Multi-task Learning—0
Sequence tagging for biomedical extractive question answeringCode1
Cooperative Self-training of Machine Reading ComprehensionCode1
Towards Confident Machine Reading Comprehension—0
EfficientQA : a RoBERTa Based Phrase-Indexed Question-Answering System—0
Improving QA Generalization by Concurrent Modeling of Multiple BiasesCode0
LUKE: Deep Contextualized Entity Representations with Entity-aware Self-attentionCode1
Rethinking the Objectives of Extractive Question AnsweringCode0
MKQA: A Linguistically Diverse Benchmark for Multilingual Open Domain Question AnsweringCode1
Probabilistic Assumptions Matter: Improved Models for Distantly-Supervised Document-Level Question AnsweringCode0
Look at the First Sentence: Position Bias in Question AnsweringCode1
MLQA: Evaluating Cross-lingual Extractive Question AnsweringCode1
Multilingual Question Answering from Formatted Text applied to Conversational Agents—0
Gendered Pronoun Resolution using BERT and an extractive question answering formulationCode0
End-to-End Learning for Answering Structured Queries Directly over Text—0
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