  This paper proposes a dual skipping guidance scheme with hybrid scoring to
accelerate document retrieval that uses learned sparse representations while
still delivering a good relevance. This scheme uses both lexical BM25 and
learned neural term weights to bound and compose the rank score of a candidate
document separately for skipping and final ranking, and maintains two top-k
thresholds during inverted index traversal. This paper evaluates time
efficiency and ranking relevance of the proposed scheme in searching MS MARCO
TREC datasets.
