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Neural Passage Quality Estimation for Static Pruning - Efficiency for Search

Association for Computing Machinery (ACM) via YouTube

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Explore a 14-minute conference talk from SIGIR 2024 focused on Neural Passage Quality Estimation for Static Pruning. Delve into the research presented by authors Xuejun Chang, Debabrata Mishra, Craig Macdonald, and Sean MacAvaney as they discuss innovative approaches to improve search efficiency. Learn about the latest advancements in static pruning techniques and how neural networks are being utilized to estimate passage quality, potentially revolutionizing information retrieval systems.

Syllabus

SIGIR 2024 M1.3 [fp] Neural Passage Quality Estimation for Static Pruning

Taught by

Association for Computing Machinery (ACM)

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