Learned sparse representations form an attractive class of contextual embeddings for text
retrieval. That is so because they are effective models of relevance and are interpretable by …

Learned sparse retrieval systems aim to combine the effectiveness of contextualized
language models with the scalability of conventional data structures such as inverted …

This paper revisits dynamic pruning through rank score thresholding in cluster-based sparse
retrieval to skip the index partially at cluster and document levels during inference. It …

Learned Sparse Retrieval (LSR) techniques use neural machinery to represent queries and
documents as learned bags of words. In contrast with other neural retrieval techniques, such …

Top-k threshold estimation is the problem of estimating the score of the k-th highest ranking
result of a search query. A good estimate can be used to speed up many common top-k …

Efficiently searching for relevant documents on a large dataset typically employs an initial
retrieval stage to extract the most relevant candidates. This process often utilizes a sparse …