# GQE-PRF: Generative Query Expansion with Pseudo-Relevance Feedback

arXiv: https://arxiv.org/abs/2108.06010 · HTML: https://mlsanity.com/paper/2108.06010/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 24 references that are in the map.
- First posted on arXiv 13 Aug 2021.

## Builds on (most cited first, 9 of 9)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [Conditional Generative Adversarial Nets](https://mlsanity.com/paper/1411.1784/) (2014)
- [Generation-Augmented Retrieval for Open-domain Question Answering](https://mlsanity.com/paper/2009.08553/) (2020)
- [CEDR: Contextualized Embeddings for Document Ranking](https://mlsanity.com/paper/1904.07094/) (2019)
- [Query expansion techniques for information retrieval: A survey](https://mlsanity.com/paper/1708.00247/) (2017)
- [ANTIQUE: A Non-Factoid Question Answering Benchmark](https://mlsanity.com/paper/1905.08957/) (2019)
- [WIKIR: A Python toolkit for building a large-scale Wikipedia-based English Information Retrieval Dataset](https://mlsanity.com/paper/1912.01901/) (2019)
- [Query expansion with artificially generated texts](https://mlsanity.com/paper/2012.08787/) (2020)

## Built on by (most cited first, 1 of 1)

- [Modified Query Expansion Through Generative Adversarial Networks for Information Extraction in E-Commerce](https://mlsanity.com/paper/2301.00036/) (2022)

## Abstract (arXiv)

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.