  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.
