  Deep pre-trained language models (e,g. BERT) are effective at large-scale
text retrieval task. Existing text retrieval systems with state-of-the-art
performance usually adopt a retrieve-then-reranking architecture due to the
high computational cost of pre-trained language models and the large corpus
size. Under such a multi-stage architecture, previous studies mainly focused on
optimizing single stage of the framework thus improving the overall retrieval
performance. However, how to directly couple multi-stage features for
optimization has not been well studied. In this paper, we design Hybrid List
Aware Transformer Reranking (HLATR) as a subsequent reranking module to
incorporate both retrieval and reranking stage features. HLATR is lightweight
and can be easily parallelized with existing text retrieval systems so that the
reranking process can be performed in a single yet efficient processing.
Empirical experiments on two large-scale text retrieval datasets show that
HLATR can efficiently improve the ranking performance of existing multi-stage
text retrieval methods.
