28 citations · 65 across the 9 of their papers we have counts for
10 papers
How Much Freedom Does An Effectiveness Metric Really Have?
Alistair Moffat, Joel Mackenzie
It is tempting to assume that because effectiveness metrics have free choice to assign scores to search engine result pages (SERPs) there must thus be a similar degree of freedom a…
Exploring the Representation Power of SPLADE Models
Joel Mackenzie, Shengyao Zhuang, Guido Zuccon
The SPLADE (SParse Lexical AnD Expansion) model is a highly effective approach to learned sparse retrieval, where documents are represented by term impact scores derived from large…
Efficient Immediate-Access Dynamic Indexing
Alistair Moffat, Joel Mackenzie
In a dynamic retrieval system, documents must be ingested as they arrive, and be immediately findable by queries. Our purpose in this paper is to describe an index structure and pr…
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…