Pseudo-relevance feedback (PRF) can enhance average retrieval effectiveness over a sufficiently large number of queries. However, PRF often introduces a drift into the original information need, thus hurting the retrieval effectiveness of several queries. While a selective application of PRF can potentially alleviate this issue, previous approaches have largely relied on unsupervised or feature-based learning to determine whether a query should be expanded. In contrast, we revisit the problem of selective PRF from a deep learning perspective, presenting a model that is entirely data-driven and trained in an end-to-end manner. The proposed model leverages a transformer-based bi-encoder architecture. Additionally, to further improve retrieval effectiveness with this selective PRF approach, we make use of the model's confidence estimates to combine the information from the original and expanded queries. In our experiments, we apply this selective feedback on a number of different combinations of ranking and feedback models, and show that our proposed approach consistently improves retrieval effectiveness for both sparse and dense ranking models, with the feedback models being either sparse, dense or generative.

翻译：伪相关反馈(PRF)能在处理大规模查询时提升平均检索效能。然而，PRF常会引入原始信息需求漂移，反而损害部分查询的检索效果。虽然选择性应用PRF可缓解此问题，但现有方法大多依赖无监督或特征学习机制判定是否需要扩展查询。与此不同，我们从深度学习视角重新审视选择性PRF问题，提出了一种完全数据驱动且以端到端方式训练的模型。该模型采用基于Transformer的双编码器架构。此外，为借助选择性PRF方法进一步提升检索效能，我们利用模型置信度估计融合原始查询与扩展查询的信息。实验表明，将该选择性反馈应用于多种排序与反馈模型组合时，所提方法在稀疏与稠密排序模型（反馈模型涵盖稀疏、稠密或生成式）上均能持续提升检索效能。