# BERT-QE: Contextualized Query Expansion for Document Re-ranking

arXiv: https://arxiv.org/abs/2009.07258 · HTML: https://mlsanity.com/paper/2009.07258/

## Key facts

- Cited by 40 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Sep 2020.

## Built on by (most cited first, 30 of 40)

- [Large Language Models for Information Retrieval: A Survey](https://mlsanity.com/paper/2308.07107/) (2023)
- [MS MARCO: Benchmarking Ranking Models in the Large-Data Regime](https://mlsanity.com/paper/2105.04021/) (2021)
- [When do Generative Query and Document Expansions Fail? A Comprehensive Study Across Methods, Retrievers, and Datasets](https://mlsanity.com/paper/2309.08541/) (2023)
- [What's in a Name? Answer Equivalence For Open-Domain Question Answering](https://mlsanity.com/paper/2109.05289/) (2021)
- [Utilizing BERT for Information Retrieval: Survey, Applications, Resources, and Challenges](https://mlsanity.com/paper/2403.00784/) (2024)
- [Taxonomy-guided Semantic Indexing for Academic Paper Search](https://mlsanity.com/paper/2410.19218/) (2024)
- [EmbedDistill: A Geometric Knowledge Distillation for Information Retrieval](https://mlsanity.com/paper/2301.12005/) (2023)
- [Improving Retrieval in Theme-specific Applications using a Corpus Topical Taxonomy](https://mlsanity.com/paper/2403.04160/) (2024)
- [Scientific Paper Retrieval with LLM-Guided Semantic-Based Ranking](https://mlsanity.com/paper/2505.21815/) (2025)
- [Improving Scientific Document Retrieval with Concept Coverage-based Query Set Generation](https://mlsanity.com/paper/2502.11181/) (2025)
- [DREQ: Document Re-Ranking Using Entity-based Query Understanding](https://mlsanity.com/paper/2401.05939/) (2024)
- [Database-Augmented Query Representation for Information Retrieval](https://mlsanity.com/paper/2406.16013/) (2024)
- [Bridging Information Asymmetry in Text-video Retrieval: A Data-centric Approach](https://mlsanity.com/paper/2408.07249/) (2024)
- [CODER: An efficient framework for improving retrieval through COntextual Document Embedding Reranking](https://mlsanity.com/paper/2112.08766/) (2021)
- [Incorporating Relevance Feedback for Information-Seeking Retrieval using Few-Shot Document Re-Ranking](https://mlsanity.com/paper/2210.10695/) (2022)
- [Relevance Feedback with Brain Signals](https://mlsanity.com/paper/2312.05669/) (2023)
- [Pseudo Relevance Feedback is Enough to Close the Gap Between Small and Large Dense Retrieval Models](https://mlsanity.com/paper/2503.14887/) (2025)
- [Graph Enhanced BERT for Query Understanding](https://mlsanity.com/paper/2204.06522/) (2022)
- [QAEncoder: Towards Aligned Representation Learning in Question Answering Systems](https://mlsanity.com/paper/2409.20434/) (2024)
- [PAIRS: Parametric-Verified Adaptive Information Retrieval and Selection for Efficient RAG](https://mlsanity.com/paper/2508.04057/) (2025)
- [Decide Then Retrieve: A Training-Free Framework with Uncertainty-Guided Triggering and Dual-Path Retrieval](https://mlsanity.com/paper/2601.03908/) (2026)
- [ReFIT: Relevance Feedback from a Reranker during Inference](https://mlsanity.com/paper/2305.11744/) (2023)
- [LoL: A Comparative Regularization Loss over Query Reformulation Losses for Pseudo-Relevance Feedback](https://mlsanity.com/paper/2204.11545/) (2022)
- [Retrieval Augmentation for T5 Re-ranker using External Sources](https://mlsanity.com/paper/2210.05145/) (2022)
- [Cross-Model Comparative Loss for Enhancing Neuronal Utility in Language Understanding](https://mlsanity.com/paper/2301.03765/) (2023)
- [Exploiting LLMs' Reasoning Capability to Infer Implicit Concepts in Legal Information Retrieval](https://mlsanity.com/paper/2410.12154/) (2024)
- [PaperRegister: Boosting Flexible-grained Paper Search via Hierarchical Register Indexing](https://mlsanity.com/paper/2508.11116/) (2025)
- [Retrieval-Feedback-Driven Distillation and Preference Alignment for Efficient LLM-based Query Expansion](https://mlsanity.com/paper/2603.13776/) (2026)
- [A Comparison of Approaches for Imbalanced Classification Problems in the Context of Retrieving Relevant Documents for an Analysis](https://mlsanity.com/paper/2205.01600/) (2022)
- [Topic-Grained Text Representation-based Model for Document Retrieval](https://mlsanity.com/paper/2207.04656/) (2022)

## Abstract (arXiv)

Query expansion aims to mitigate the mismatch between the language used in a query and in a document. However, query expansion methods can suffer from introducing non-relevant information when expanding the query. To bridge this gap, inspired by recent advances in applying contextualized models like BERT to the document retrieval task, this paper proposes a novel query expansion model that leverages the strength of the BERT model to select relevant document chunks for expansion. In evaluation on the standard TREC Robust04 and GOV2 test collections, the proposed BERT-QE model significantly outperforms BERT-Large models.