Improving BERT-based Query-by-Document Retrieval with Multi-Task Optimization

TL;DR

This paper enhances BERT-based query-by-document retrieval by introducing a multi-task fine-tuning approach that leverages additional document-level representations, leading to improved ranking effectiveness without extra data.

Contribution

It proposes a novel multi-task optimization method for fine-tuning BERT re-rankers, improving retrieval performance in QBD tasks.

Findings

Significant improvement in retrieval effectiveness on benchmarks.

Effective without additional training data.

No changes needed to the original BERT re-ranker.

Abstract

Query-by-document (QBD) retrieval is an Information Retrieval task in which a seed document acts as the query and the goal is to retrieve related documents -- it is particular common in professional search tasks. In this work we improve the retrieval effectiveness of the BERT re-ranker, proposing an extension to its fine-tuning step to better exploit the context of queries. To this end, we use an additional document-level representation learning objective besides the ranking objective when fine-tuning the BERT re-ranker. Our experiments on two QBD retrieval benchmarks show that the proposed multi-task optimization significantly improves the ranking effectiveness without changing the BERT re-ranker or using additional training samples. In future work, the generalizability of our approach to other retrieval tasks should be further investigated.