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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 151–175 of 268 papers

TitleStatusHype
CoT-MoTE: Exploring ConTextual Masked Auto-Encoder Pre-training with Mixture-of-Textual-Experts for Passage Retrieval—0
Generalized Weak Supervision for Neural Information Retrieval—0
Noise-Robust Dense Retrieval via Contrastive Alignment Post Training—0
Evidentiality-aware Retrieval for Overcoming Abstractiveness in Open-Domain Question Answering—0
CoT-MAE v2: Contextual Masked Auto-Encoder with Multi-view Modeling for Passage Retrieval—0
Can a Frozen Pretrained Language Model be used for Zero-shot Neural Retrieval on Entity-centric Questions?—0
Query Performance Prediction for Neural IR: Are We There Yet?Code0
How to Train Your DRAGON: Diverse Augmentation Towards Generalizable Dense Retrieval—0
An Experimental Study on Pretraining Transformers from Scratch for IR—0
HYRR: Hybrid Infused Reranking for Passage Retrieval—0
CAPSTONE: Curriculum Sampling for Dense Retrieval with Document Expansion—0
PolQA: Polish Question Answering Dataset—0
MASTER: Multi-task Pre-trained Bottlenecked Masked Autoencoders are Better Dense Retrievers—0
Open-Domain Conversational Question Answering with Historical AnswersCode0
Bridging the Training-Inference Gap for Dense Phrase Retrieval—0
Entity-Focused Dense Passage Retrieval for Outside-Knowledge Visual Question Answering—0
Revisiting the Roles of "Text" in Text Games—0
Towards Robust Neural Retrieval with Source Domain Synthetic Pre-Finetuning—0
On the Impact of Speech Recognition Errors in Passage Retrieval for Spoken Question AnsweringCode0
Zero-shot Event Causality Identification with Question Answering—0
DPTDR: Deep Prompt Tuning for Dense Passage RetrievalCode0
Evaluating Dense Passage Retrieval using Transformers—0
SimLM: Pre-training with Representation Bottleneck for Dense Passage Retrieval—0
MIA 2022 Shared Task Submission: Leveraging Entity Representations, Dense-Sparse Hybrids, and Fusion-in-Decoder for Cross-Lingual Question Answering—0
Towards Robust Ranker for Text Retrieval—0
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