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

Zhi Zheng, Kai Hui, Ben He, Xianpei Han, Le Sun, Andrew Yates


Abstract
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.
Anthology ID:
2020.findings-emnlp.424
Volume:
Findings of the Association for Computational Linguistics: EMNLP 2020
Month:
November
Year:
2020
Address:
Online
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
4718–4728
Language:
URL:
https://aclanthology.org/2020.findings-emnlp.424
DOI:
10.18653/v1/2020.findings-emnlp.424
Bibkey:
Cite (ACL):
Zhi Zheng, Kai Hui, Ben He, Xianpei Han, Le Sun, and Andrew Yates. 2020. BERT-QE: Contextualized Query Expansion for Document Re-ranking. In Findings of the Association for Computational Linguistics: EMNLP 2020, pages 4718–4728, Online. Association for Computational Linguistics.
Cite (Informal):
BERT-QE: Contextualized Query Expansion for Document Re-ranking (Zheng et al., Findings 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.findings-emnlp.424.pdf
Code
 zh-zheng/BERT-QE
Data
MS MARCO