Rethinking Query Expansion for BERT Reranking

By Ramith Padaki, Zhuyun Dai, Jamie Callan. 2020

49 citations recorded by the source corpus.

Recent studies have shown promising results of using BERT for Information Retrieval with its advantages in understanding the text content of documents and queries. Compared to short, keywords queries, higher accuracy of BERT were observed on long, natural language queries, demonstrating BERT’s ability in extracting rich information from complex queries. These results show the potential of using query expansion to generate better queries for BERT-based rankers. In this work, we explore BERT’s sensitivity to the addition of structure and concepts. We find that traditional word-based query expansion is not entirely applicable, and provide insight into methods that produce better experimental results.

Subfields: Computer Networks and Communications, Management Science and Operations Research

Citations in the corpus

• Document Ranking with a Pretrained Sequence-to-Sequence Model (2020, neutral): “trained reranking models [12].”