Representation Sparsification with Hybrid Thresholding for Fast SPLADE-based Document Retrieval

Yifan Qiao, Yingrui Yang, Shanxiu He, Tao Yang

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.