17 citations · 18 across the 3 of their papers we have counts for
6 papers · 1 filter
Faster Learned Sparse Retrieval with Guided Traversal
Antonio Mallia, Joel Mackenzie, Torsten Suel +1
Neural information retrieval architectures based on transformers such as BERT are able to significantly improve system effectiveness over traditional sparse models such as BM25. Th…
A Sensitivity Analysis of the MSMARCO Passage Collection
Joel Mackenzie, Matthias Petri, Alistair Moffat
The recent MSMARCO passage retrieval collection has allowed researchers to develop highly tuned retrieval systems. One aspect of this data set that makes it distinctive compared to…
Wacky Weights in Learned Sparse Representations and the Revenge of Score-at-a-Time Query Evaluation
Joel Mackenzie, Andrew Trotman, Jimmy Lin
Recent advances in retrieval models based on learned sparse representations generated by transformers have led us to, once again, consider score-at-a-time query evaluation techniqu…
Anytime Ranking on Document-Ordered Indexes
Joel Mackenzie, Matthias Petri, Alistair Moffat
Inverted indexes continue to be a mainstay of text search engines, allowing efficient querying of large document collections. While there are a number of possible organizations, do…
Supporting Interoperability Between Open-Source Search Engines with the Common Index File Format
Jimmy Lin, Joel Mackenzie, Chris Kamphuis +5
There exists a natural tension between encouraging a diverse ecosystem of open-source search engines and supporting fair, replicable comparisons across those systems. To balance th…
Boosting Search Performance Using Query Variations
Rodger Benham, Joel Mackenzie, Alistair Moffat +1
Rank fusion is a powerful technique that allows multiple sources of information to be combined into a single result set. However, to date fusion has not been regarded as being cost…