Pseudo-relevance feedback (PRF) is commonly used to boost the performance of
traditional information retrieval (IR) models by using top-ranked documents to
identify and weight new query terms, thereby reducing the effect of
query-document vocabulary mismatches. While neural retrieval models have
recently demonstrated strong results for ad-hoc retrieval, combining them with
PRF is not straightforward due to incompatibilities between existing PRF
approaches and neural architectures. To bridge this gap, we propose an
end-to-end neural PRF framework that can be used with existing neural IR models
by embedding different neural models as building blocks. Extensive experiments
on two standard test collections confirm the effectiveness of the proposed NPRF
framework in improving the performance of two state-of-the-art neural IR
models.

本文提出了一个端到端的神经伪相关反馈（PRF）框架，通过将不同的神经模型嵌入到建筑块中，可以与现有的神经信息检索模型一起使用来改善它们的性能。在两个标准测试集上的广泛实验证实了提出的 NPRF 框架在改善两个最先进的神经 IR 模型的性能方面的有效性。