Efficient Neural Ranking using Forward Indexes

Understand

Neural document ranking approaches, specifically transformer models, have achieved impressive gains in ranking performance.

• However, query processing using such over-parameterized models is both resource and time intensive.
• In this paper, we propose the Fast-Forward index -- a simple vector forward index that facilitates ranking documents using interpolation of lexical and semantic scores -- as a replacement for contextual re-rankers and dense indexes based on nearest neighbor search.
• Fast-Forward indexes rely on efficient sparse models for retrieval and merely look up pre-computed dense transformer-based vector representations of documents and passages in constant time for fast CPU-based semantic similarity computation during query processing.