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

arXiv (Cornell University)

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.

Authors 3

1. Amin Abolghasemi Aachen

   Leiden University

   Affiliation as printed

   Leiden University , The Netherlands

2. Suzan Verberne Aachen

   Leiden University

   Affiliation as printed

   Leiden University , The Netherlands

3. Leif A. Azzopardi

   University of Strathclyde

   Affiliation as printed

   University of Strathclyde , UK