Efficient Interaction-based Neural Ranking with Locality Sensitive Hashing

Abstract

Interaction-based neural ranking has been shown to be effective for document search using distributed word representations. However the time or space required is very expensive for online query processing with neural ranking. This paper investigates fast approximation of three interaction-based neural ranking algorithms using Locality Sensitive Hashing (LSH). It accelerates query-document interaction computation by using a runtime cache with precomputed term vectors, and speeds up kernel calculation by taking advantages of limited integer similarity values. This paper presents the design choices with cost analysis, and an evaluation that assesses efficiency benefits and relevance tradeoffs for the tested datasets.