8 citations · 11 across the 3 of their papers we have counts for
5 papers
Human Preferences as Dueling Bandits
Xinyi Yan, Chengxi Luo, Charles L. A. Clarke +3
The dramatic improvements in core information retrieval tasks engendered by neural rankers create a need for novel evaluation methods. If every ranker returns highly relevant items…
Predicting Efficiency/Effectiveness Trade-offs for Dense vs. Sparse Retrieval Strategy Selection
Negar Arabzadeh, Xinyi Yan, Charles L. A. Clarke
Over the last few years, contextualized pre-trained transformer models such as BERT have provided substantial improvements on information retrieval tasks. Recent approaches based o…
Assessing top- preferences
Charles L. A. Clarke, Alexandra Vtyurina, Mark D. Smucker
Assessors make preference judgments faster and more consistently than graded judgments. Preference judgments can also recognize distinctions between items that appear equivalent un…
The Effects of Latency Penalties in Evaluating Push Notification Systems
Luchen Tan, Jimmy Lin, Adam Roegiest +1
We examine the effects of different latency penalties in the evaluation of push notification systems, as operationalized in the TREC 2015 Microblog track evaluation. The purpose of…
Assessing Efficiency-Effectiveness Tradeoffs in Multi-Stage Retrieval Systems Without Using Relevance Judgments
Charles L. A. Clarke, J. Shane Culpepper, Alistair Moffat
Large-scale retrieval systems are often implemented as a cascading sequence of phases -- a first filtering step, in which a large set of candidate documents are extracted using a s…