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 adhoc 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-theart neural IR models.
Another recent work has achieved state-of-the-art performance by creating a neural pseudo relevance feedback framework (NPRF) that can be used with existing neural IR models as building blocks REF .
NPRF: A Neural Pseudo Relevance Feedback Framework for Ad-hoc Information Retrieval
ArXiv
ArXiv
Computer Science