  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.
