BERT-QE: Contextualized Query Expansion for Document Re-ranking
By Zhi Zheng, Kai Hui, Ben He, Xianpei Han, Le Sun, Andrew Yates. 2020
121 citations recorded by the source corpus.
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.
Subfields: Artificial Intelligence, Information Retrieval & Search
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Nick Craswell
- Bhaskar Mitra
- Jimmy J. Lin
- Emine Yilmaz
- Daniel Fernando Campos
- Sebastian Hofstätter
- Allan Hanbury
- Hamed Zamani
In Information Retrieval & Search
- Jimmy J. Lin
- Nick Craswell
- Emine Yilmaz
- Bhaskar Mitra
- Daniel Fernando Campos
- Sebastian Hofstätter
- Rodrigo Nogueira
- Allan Hanbury
Citations in the corpus
- MS MARCO: Benchmarking Ranking Models in the Large-Data Regime (2021, methodology): “Similar strategies of finetuning on MS MARCO and evaluating on Robust04, GOV2 [10], and ClueWeb [11] have been employed in other recent studies [30, 37, 40, 91, 92], sometimes in combination with weak”
- Large Language Models for Information Retrieval: A Survey (2023, unknown): “Mainly-used methods include relevance feedback [87–92], word-embedding based methods [93, 94] etc.”