@inproceedings{mackenzie-etal-2022-accelerating,
title = "Accelerating Learned Sparse Indexes Via Term Impact Decomposition",
author = "Mackenzie, Joel and
Mallia, Antonio and
Moffat, Alistair and
Petri, Matthias",
editor = "Goldberg, Yoav and
Kozareva, Zornitsa and
Zhang, Yue",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2022",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.findings-emnlp.205/",
doi = "10.18653/v1/2022.findings-emnlp.205",
pages = "2830--2842",
abstract = "Novel inverted index-based learned sparse ranking models provide more effective, but less efficient, retrieval performance compared to traditional ranking models like BM25. In this paper, we introduce a technique we call postings clipping to improve the query efficiency of learned representations. Our technique amplifies the benefit of dynamic pruning query processing techniques by accounting for changes in term importance distributions of learned ranking models. The new clipping mechanism accelerates top-k retrieval by up to 9.6X without any loss in effectiveness."
}
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%0 Conference Proceedings
%T Accelerating Learned Sparse Indexes Via Term Impact Decomposition
%A Mackenzie, Joel
%A Mallia, Antonio
%A Moffat, Alistair
%A Petri, Matthias
%Y Goldberg, Yoav
%Y Kozareva, Zornitsa
%Y Zhang, Yue
%S Findings of the Association for Computational Linguistics: EMNLP 2022
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates
%F mackenzie-etal-2022-accelerating
%X Novel inverted index-based learned sparse ranking models provide more effective, but less efficient, retrieval performance compared to traditional ranking models like BM25. In this paper, we introduce a technique we call postings clipping to improve the query efficiency of learned representations. Our technique amplifies the benefit of dynamic pruning query processing techniques by accounting for changes in term importance distributions of learned ranking models. The new clipping mechanism accelerates top-k retrieval by up to 9.6X without any loss in effectiveness.
%R 10.18653/v1/2022.findings-emnlp.205
%U https://aclanthology.org/2022.findings-emnlp.205/
%U https://doi.org/10.18653/v1/2022.findings-emnlp.205
%P 2830-2842
Markdown (Informal)
[Accelerating Learned Sparse Indexes Via Term Impact Decomposition](https://aclanthology.org/2022.findings-emnlp.205/) (Mackenzie et al., Findings 2022)
ACL