Incorporating Query Term Independence Assumption for Efficient Retrieval and Ranking using Deep Neural Networks

TL;DR

This paper introduces a method to incorporate query term independence into neural IR models, enabling precomputation and significantly reducing retrieval costs without substantially sacrificing accuracy.

Contribution

It adapts three neural IR models to assume query term independence, allowing efficient precomputation and making deep neural retrieval feasible for large-scale collections.

Findings

No significant performance loss for Duet and CKNRM models.

Small degradation observed in BERT performance.

Enables practical use of deep neural models for large-scale retrieval.

Abstract

Classical information retrieval (IR) methods, such as query likelihood and BM25, score documents independently w.r.t. each query term, and then accumulate the scores. Assuming query term independence allows precomputing term-document scores using these models---which can be combined with specialized data structures, such as inverted index, for efficient retrieval. Deep neural IR models, in contrast, compare the whole query to the document and are, therefore, typically employed only for late stage re-ranking. We incorporate query term independence assumption into three state-of-the-art neural IR models: BERT, Duet, and CKNRM---and evaluate their performance on a passage ranking task. Surprisingly, we observe no significant loss in result quality for Duet and CKNRM---and a small degradation in the case of BERT. However, by operating on each query term independently, these otherwise computationally intensive models become amenable to offline precomputation---dramatically reducing the cost of query evaluations employing state-of-the-art neural ranking models. This strategy makes it practical to use deep models for retrieval from large collections---and not restrict their usage to late stage re-ranking.