  BERT-based text ranking models have dramatically advanced the
state-of-the-art in ad-hoc retrieval, wherein most models tend to consider
individual query-document pairs independently. In the mean time, the importance
and usefulness to consider the cross-documents interactions and the
query-specific characteristics in a ranking model have been repeatedly
confirmed, mostly in the context of learning to rank. The BERT-based ranking
model, however, has not been able to fully incorporate these two types of
ranking context, thereby ignoring the inter-document relationships from the
ranking and the differences among queries. To mitigate this gap, in this work,
an end-to-end transformer-based ranking model, named Co-BERT, has been proposed
to exploit several BERT architectures to calibrate the query-document
representations using pseudo relevance feedback before modeling the relevance
of a group of documents jointly. Extensive experiments on two standard test
collections confirm the effectiveness of the proposed model in improving the
performance of text re-ranking over strong fine-tuned BERT-Base baselines. We
plan to make our implementation open source to enable further comparisons.
