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Dense retrieval systems conduct first-stage retrieval using embedded representations and simple similarity metrics to match a query to documents. Its effectiveness depends on encoded embeddings to capture the semantics of queries and documents, a challenging task due to the shortness and ambiguity of search queries. This paper proposes ANCE-PRF, a new query encoder that uses pseudo relevance feedback (PRF) to improve query representations for dense retrieval. ANCE-PRF uses a BERT encoder that consumes the query and the top retrieved documents from a dense retrieval model, ANCE, and it learns to produce better query embeddings directly from relevance labels. It also keeps the document index unchanged to reduce overhead. ANCE-PRF significantly outperforms ANCE and other recent dense retrieval systems on several datasets. Analysis shows that the PRF encoder effectively captures the relevant and complementary information from PRF documents, while ignoring the noise with its learned attention mechanism.
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Rethinking Query Expansion for BERT Reranking
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.
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- Award ID(s):
- 1815528
- PAR ID:
- 10170075
- Date Published:
- Journal Name:
- Advances in Information Retrieval - 42nd European Conference on IR Research, ECIR 2020
- Page Range / eLocation ID:
- 297-304
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1007/978-3-030-45442-5_37
