论文标题
BERT-QE：文档重新排列的上下文化查询扩展
BERT-QE: Contextualized Query Expansion for Document Re-ranking
论文作者
论文摘要
查询扩展旨在减轻查询和文档中使用的语言之间的不匹配。但是，查询扩展方法在扩展查询时会引入非相关信息。为了弥合这一差距，这是受到将上下文化模型（例如BERT）应用于文档检索任务的最新进展的启发，本文提出了一种新型查询扩展模型，该模型利用BERT模型的强度来选择相关文档块进行扩展。在评估标准TREC Robust04和Gov2测试集合中，提出的BERT-QE模型显着胜过Bert-large模型。
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.
