Abstract

Web search engines often perform an initial candidate generation phase using a fast and simple ranking function, followed by subsequent reranking with more expensive rankers. Such simple ranking functions usually compute the score of a document as the sum of term-wise impact scores, and they include traditional baselines such as BM25 and Query Likelihood, as well as some recently proposed learned sparse models based on document expansion and learned impact scores. In this paper, we explore extremely fast and highly effective early termination techniques for such simple ranking functions. Our extensive experiments with a number of different ranking functions show that our methods achieve very fast response times on MSMarco V1 and V2 data while maintaining retrieval quality close to that of a safe and much slower baseline.