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Showing 2650 of 178 papers

TitleStatusHype
Would You Ask it that Way? Measuring and Improving Question Naturalness for Knowledge Graph Question AnsweringCode1
Table Retrieval May Not Necessitate Table-specific Model DesignCode1
How Do We Answer Complex Questions: Discourse Structure of Long-form AnswersCode1
Asyncval: A Toolkit for Asynchronously Validating Dense Retriever Checkpoints during TrainingCode1
Text and Code Embeddings by Contrastive Pre-TrainingCode1
Continual Learning with Knowledge Transfer for Sentiment ClassificationCode1
TempoQR: Temporal Question Reasoning over Knowledge GraphsCode1
Open Domain Question Answering with A Unified Knowledge InterfaceCode1
Efficient Passage Retrieval with Hashing for Open-domain Question AnsweringCode1
Corpus-Level Evaluation for Event QA: The IndiaPoliceEvents Corpus Covering the 2002 Gujarat ViolenceCode1
Back-Training excels Self-Training at Unsupervised Domain Adaptation of Question Generation and Passage RetrievalCode1
End-to-End Training of Neural Retrievers for Open-Domain Question AnsweringCode1
Rider: Reader-Guided Passage Reranking for Open-Domain Question AnsweringCode1
RealFormer: Transformer Likes Residual AttentionCode1
RECONSIDER: Re-Ranking using Span-Focused Cross-Attention for Open Domain Question AnsweringCode1
AutoQA: From Databases To QA Semantic Parsers With Only Synthetic Training DataCode1
Generation-Augmented Retrieval for Open-domain Question AnsweringCode1
QED: A Framework and Dataset for Explanations in Question AnsweringCode1
MKQA: A Linguistically Diverse Benchmark for Multilingual Open Domain Question AnsweringCode1
Leveraging Passage Retrieval with Generative Models for Open Domain Question AnsweringCode1
C3VQG: Category Consistent Cyclic Visual Question GenerationCode1
Document Modeling with Graph Attention Networks for Multi-grained Machine Reading ComprehensionCode1
Event Extraction by Answering (Almost) Natural QuestionsCode1
Knowledge Guided Text Retrieval and Reading for Open Domain Question AnsweringCode1
Retrieval-Augmented Generation as Noisy In-Context Learning: A Unified Theory and Risk Bounds0
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