Home > Events > CLIP Colloquium: Yue Wang (UNC Chapel Hill)
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CLIP Colloquium: Yue Wang (UNC Chapel Hill)

Time: 
Wednesday, September 22, 2021 - 2:00 PM to 3:00 PM
Location: 
Virtual: https://umd.zoom.us/j/98806584197?pwd=SXBWOHE1cU9adFFKUmN2UVlwUEJXdz09

 

(Note that the time differs from regular colloquium time)

Towards Explainable Retrieval Models for Professional Search Tasks

Abstract: In professional search tasks such as precision medicine literature search, a query often involves multiple aspects. To assess the relevance of a document, a searcher would have to painstakingly validate each aspect in the query and follow a task-specific logic to make a relevance decision. In such scenarios, we say the searcher makes a structured relevance judgment, as opposed to the conventional univariate (binary or graded) relevance judgment. Ideally, a search engine can support the searcher’s workflow and follow the same steps to predict document relevance. This approach may not only yield highly effective retrieval models, but also open up opportunities for the model to explain its decisions in the same ‘lingo’ as the searcher.

In this talk, I will discuss our recent work on explainable retrieval models that emulate how medical experts make structured relevance judgments. Using data from the TREC Precision Medicine literature search track (2017-2019), we found that a simple, explainable, and label-efficient model can consistently perform as well as complex, black-box, and data-hungry learning-to-rank models. These results suggest that leveraging the structure in professional search queries is a promising direction towards building explainable search tools to support professional search tasks.

Bio: Yue Wang is an assistant professor in the School of Information and Library Science in the University of North Carolina at Chapel Hill. His research interests include text mining, machine learning, and information retrieval. His recent work focuses on designing and evaluating interactive and interpretable machine learning algorithms that can help scientists gain knowledge from large unstructured text. He publishes in a broad range of venues in computer and information sciences, including SIGIR, WSDM, CIKM, ACL, WWW, KDD, CHI, AMIA, and JAMIA. He serves as a regular program committee member for WSDM, SIGIR, and WWW. He received the Best Paper Award in WSDM 2016 and Outstanding Program Committee Member Award in WSDM 2016, 2019, and 2020.
 
Yue's personal website: https://ils.unc.edu/~wangyue/