Representation Sparsification with Hybrid Thresholding for Fast SPLADE-based Document Retrieval
Information Retrieval
2023-06-21 v1
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.
Cite
@article{arxiv.2306.11293,
title = {Representation Sparsification with Hybrid Thresholding for Fast SPLADE-based Document Retrieval},
author = {Yifan Qiao and Yingrui Yang and Shanxiu He and Tao Yang},
journal= {arXiv preprint arXiv:2306.11293},
year = {2023}
}
Comments
This paper is published in SIGIR'23