An end-to-end pseudo relevance feedback framework for neural document retrieval

Highlights：

• Three instantiations of the NPRF framework are introduced based on state-of-the-art neural IR models, namely the unigram DRMM and KNRM models, and the n-gram PACRR model.

• Thorough experiments support the framework’s intuition and showcase its ability in enhancing the retrieval performance of neural retrieval. In addition, analysis indicates reduced training and validation losses by our NPRF framework, leading to significantly improved effectiveness of the learned ranking functions.

摘要

•The proposal of a novel neural pseudo relevance feedback (NPRF) framework that enables the integration of PRF for neural retrieval. To the best of our knowledge, this is the first neural retrieval model that integrates PRF information by end-to-end learning.•Three instantiations of the NPRF framework are introduced based on state-of-the-art neural IR models, namely the unigram DRMM and KNRM models, and the n-gram PACRR model.•Thorough experiments support the framework’s intuition and showcase its ability in enhancing the retrieval performance of neural retrieval. In addition, analysis indicates reduced training and validation losses by our NPRF framework, leading to significantly improved effectiveness of the learned ranking functions.

论文评审过程：Received 28 May 2019, Revised 7 November 2019, Accepted 13 December 2019, Available online 20 December 2019, Version of Record 20 December 2019.