Efficient Neural Ranking using Forward Indexes
This work addresses efficiency issues in neural ranking for information retrieval, offering a practical solution for resource-constrained environments, though it is incremental in optimizing existing methods.
The paper tackles the problem of high computational cost in neural document ranking by proposing Fast-Forward indexes, which combine lexical and semantic scores for efficient CPU-based query processing, showing improvements in performance and efficiency on TREC-DL datasets.
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. We propose index pruning and theoretically grounded early stopping techniques to improve the query processing throughput. We conduct extensive large-scale experiments on TREC-DL datasets and show improvements over hybrid indexes in performance and query processing efficiency using only CPUs. Fast-Forward indexes can provide superior ranking performance using interpolation due to the complementary benefits of lexical and semantic similarities.