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Representation Sparsification with Hybrid Thresholding for Fast SPLADE-based Document Retrieval
June 20, 2023 Β· Declared Dead Β· π Annual International ACM SIGIR Conference on Research and Development in Information Retrieval
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Authors
Yifan Qiao, Yingrui Yang, Shanxiu He, Tao Yang
arXiv ID
2306.11293
Category
cs.IR: Information Retrieval
Citations
9
Venue
Annual International ACM SIGIR Conference on Research and Development in Information Retrieval
Last Checked
4 months ago
Abstract
Learned sparse document representations using a transformer-based neural model has been found to be attractive in both relevance effectiveness and time efficiency. This paper describes a representation sparsification scheme based on hard and soft thresholding with an inverted index approximation for faster SPLADE-based document retrieval. It provides analytical and experimental results on the impact of this learnable hybrid thresholding scheme.
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