GQE-PRF: Generative Query Expansion with Pseudo-Relevance Feedback
- URL: http://arxiv.org/abs/2108.06010v1
- Date: Fri, 13 Aug 2021 01:09:02 GMT
- Title: GQE-PRF: Generative Query Expansion with Pseudo-Relevance Feedback
- Authors: Minghui Huang, Dong Wang, Shuang Liu, Meizhen Ding
- Abstract summary: We propose a novel approach which effectively integrates text generation models into PRF-based query expansion.
Our approach generates augmented query terms via neural text generation models conditioned on both the initial query and pseudo-relevance feedback.
We evaluate the performance of our approach on information retrieval tasks using two benchmark datasets.
- Score: 8.142861977776256
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Query expansion with pseudo-relevance feedback (PRF) is a powerful approach
to enhance the effectiveness in information retrieval. Recently, with the rapid
advance of deep learning techniques, neural text generation has achieved
promising success in many natural language tasks. To leverage the strength of
text generation for information retrieval, in this article, we propose a novel
approach which effectively integrates text generation models into PRF-based
query expansion. In particular, our approach generates augmented query terms
via neural text generation models conditioned on both the initial query and
pseudo-relevance feedback. Moreover, in order to train the generative model, we
adopt the conditional generative adversarial nets (CGANs) and propose the
PRF-CGAN method in which both the generator and the discriminator are
conditioned on the pseudo-relevance feedback. We evaluate the performance of
our approach on information retrieval tasks using two benchmark datasets. The
experimental results show that our approach achieves comparable performance or
outperforms traditional query expansion methods on both the retrieval and
reranking tasks.
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