TY - GEN
T1 - Faster Learned Sparse Retrieval with Guided Traversal
AU - Mallia, Antonio
AU - MacKenzie, Joel
AU - Suel, Torsten
AU - Tonellotto, Nicola
N1 - Publisher Copyright:
© 2022 ACM.
PY - 2022/7/6
Y1 - 2022/7/6
N2 - Neural information retrieval architectures based on transformers such as BERT are able to significantly improve system effectiveness over traditional sparse models such as BM25. Though highly effective, these neural approaches are very expensive to run, making them difficult to deploy under strict latency constraints. To address this limitation, recent studies have proposed new families of learned sparse models that try to match the effectiveness of learned dense models, while leveraging the traditional inverted index data structure for efficiency. Current learned sparse models learn the weights of terms in documents and, sometimes, queries; however, they exploit different vocabulary structures, document expansion techniques, and query expansion strategies, which can make them slower than traditional sparse models such as BM25. In this work, we propose a novel indexing and query processing technique that exploits a traditional sparse model's "guidance"to efficiently traverse the index, allowing the more effective learned model to execute fewer scoring operations. Our experiments show that our guided processing heuristic is able to boost the efficiency of the underlying learned sparse model by a factor of four without any measurable loss of effectiveness.
AB - Neural information retrieval architectures based on transformers such as BERT are able to significantly improve system effectiveness over traditional sparse models such as BM25. Though highly effective, these neural approaches are very expensive to run, making them difficult to deploy under strict latency constraints. To address this limitation, recent studies have proposed new families of learned sparse models that try to match the effectiveness of learned dense models, while leveraging the traditional inverted index data structure for efficiency. Current learned sparse models learn the weights of terms in documents and, sometimes, queries; however, they exploit different vocabulary structures, document expansion techniques, and query expansion strategies, which can make them slower than traditional sparse models such as BM25. In this work, we propose a novel indexing and query processing technique that exploits a traditional sparse model's "guidance"to efficiently traverse the index, allowing the more effective learned model to execute fewer scoring operations. Our experiments show that our guided processing heuristic is able to boost the efficiency of the underlying learned sparse model by a factor of four without any measurable loss of effectiveness.
KW - inverted index
KW - learned sparse retrieval
KW - query processing
UR - http://www.scopus.com/inward/record.url?scp=85135041007&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85135041007&partnerID=8YFLogxK
U2 - 10.1145/3477495.3531774
DO - 10.1145/3477495.3531774
M3 - Conference contribution
AN - SCOPUS:85135041007
T3 - SIGIR 2022 - Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval
SP - 1901
EP - 1905
BT - SIGIR 2022 - Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval
PB - Association for Computing Machinery, Inc
T2 - 45th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2022
Y2 - 11 July 2022 through 15 July 2022
ER -