49 citations · 120 across the 26 of their papers we have counts for
15 papers · 1 filter
SSE: A Metric for Evaluating Search System Explainability
Catherine Chen, Carsten Eickhoff
Explainable Information Retrieval (XIR) is a growing research area focused on enhancing transparency and trustworthiness of the complex decision-making processes taking place in mo…
Evaluating Search System Explainability with Psychometrics and Crowdsourcing
Catherine Chen, Carsten Eickhoff
As information retrieval (IR) systems, such as search engines and conversational agents, become ubiquitous in various domains, the need for transparent and explainable systems grow…
PoolRank: Max/Min Pooling-based Ranking Loss for Listwise Learning & Ranking Balance
Zhizhong Chen, Carsten Eickhoff
Numerous neural retrieval models have been proposed in recent years. These models learn to compute a ranking score between the given query and document. The majority of existing mo…
ExpertRank: A Multi-level Coarse-grained Expert-based Listwise Ranking Loss
Zhizhong Chen, Carsten Eickhoff
The goal of information retrieval is to recommend a list of document candidates that are most relevant to a given query. Listwise learning trains neural retrieval models by compari…
The Cross-Lingual Arabic Information REtrieval (CLAIRE) System
Zhizhong Chen, Carsten Eickhoff
Despite advances in neural machine translation, cross-lingual retrieval tasks in which queries and documents live in different natural language spaces remain challenging. Although…
Brown University at TREC Deep Learning 2019
George Zerveas, Ruochen Zhang, Leila Kim +1
This paper describes Brown University's submission to the TREC 2019 Deep Learning track. We followed a 2-phase method for producing a ranking of passages for a given input query: I…