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Passage Retrieval

Passage retrieval is a specialized type of IR application that retrieves relevant passages (or pieces of text) rather than an entire ranked set of documents.

Papers

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Showing 226–250 of 268 papers

TitleStatusHype
Assessing the Benefits of Model Ensembles in Neural Re-Ranking for Passage Retrieval—0
A Comparison of Question Rewriting Methods for Conversational Passage RetrievalCode0
Weakly Supervised Label SmoothingCode1
Structural Text Segmentation of Legal DocumentsCode1
A Deep Metric Learning Method for Biomedical Passage Retrieval—0
A Little Bit Is Worse Than None: Ranking with Limited Training DataCode0
Making Information Seeking Easier: An Improved Pipeline for Conversational Search—0
Neural Passage Retrieval with Improved Negative Contrast—0
CoRT: Complementary Rankings from TransformersCode1
Learning To Retrieve: How to Train a Dense Retrieval Model Effectively and EfficientlyCode1
RocketQA: An Optimized Training Approach to Dense Passage Retrieval for Open-Domain Question Answering—0
Open-Domain Question Answering Goes Conversational via Question RewritingCode1
Embedding-based Zero-shot Retrieval through Query GenerationCode1
Generation-Augmented Retrieval for Open-domain Question AnsweringCode1
Brown University at TREC Deep Learning 2019—0
Leveraging Passage Retrieval with Generative Models for Open Domain Question AnsweringCode1
Approximate Nearest Neighbor Negative Contrastive Learning for Dense Text RetrievalCode1
Query Resolution for Conversational Search with Limited SupervisionCode1
Multi-Stage Conversational Passage Retrieval: An Approach to Fusing Term Importance Estimation and Neural Query Rewriting—0
Do Neural Ranking Models Intensify Gender Bias?Code0
Question Rewriting for Conversational Question Answering—0
Zero-shot Neural Passage Retrieval via Domain-targeted Synthetic Question Generation—0
Expansion via Prediction of Importance with ContextualizationCode0
Dense Passage Retrieval for Open-Domain Question AnsweringCode1
Overview of the TREC 2019 deep learning trackCode1
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