  Although considerable efforts have been devoted to transformer-based ranking
models for document search, the relevance-efficiency tradeoff remains a
critical problem for ad-hoc ranking. To overcome this challenge, this paper
presents BECR (BERT-based Composite Re-Ranking), a composite re-ranking scheme
that combines deep contextual token interactions and traditional lexical
term-matching features. In particular, BECR exploits a token encoding mechanism
to decompose the query representations into pre-computable uni-grams and
skip-n-grams. By applying token encoding on top of a dual-encoder architecture,
BECR separates the attentions between a query and a document while capturing
the contextual semantics of a query. In contrast to previous approaches, this
framework does not perform expensive BERT computations during online inference.
Thus, it is significantly faster, yet still able to achieve high
competitiveness in ad-hoc ranking relevance. Finally, an in-depth comparison
between BECR and other start-of-the-art neural ranking baselines is described
using the TREC datasets, thereby further demonstrating the enhanced relevance
and efficiency of BECR.
