Co-BERT: A Context-Aware BERT Retrieval Model Incorporating Local and Query-specific Context
This work addresses a gap in ad-hoc retrieval for search and ranking applications, but it is incremental as it builds on existing BERT-based methods with specific enhancements.
The paper tackled the problem of BERT-based text ranking models ignoring cross-document interactions and query-specific characteristics by proposing Co-BERT, an end-to-end transformer model that uses pseudo relevance feedback and joint modeling, resulting in improved performance over strong baselines on standard test collections.
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.