论文标题
通过多任务优化改善基于BERT的查询检索
Improving BERT-based Query-by-Document Retrieval with Multi-Task Optimization
论文作者
论文摘要
查询文件（QBD）检索是一项信息检索任务，其中种子文档充当查询，目标是检索相关文档 - 在专业搜索任务中特别常见。在这项工作中，我们提高了BERT重新级别的检索有效性，并提出了其微调步骤的扩展，以更好地利用查询的背景。为此，除了对BERT重新率调查时的排名目标外，我们还使用其他文档级表示学习目标。我们对两个QBD检索基准测试的实验表明，提出的多任务优化可显着提高排名效率，而无需更改BERT重新级别或使用其他培训样本。在将来的工作中，应进一步研究我们对其他检索任务的普遍性。
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.
