CIIR Talk Series: Re-Thinking Re-Ranking

Abstract: Re-ranking systems take a "cascading" approach, wherein an initial candidate pool of documents are ranked and filtered to produce a final result list. This approach exhibits a fundamental relevance misalignment problem: the most relevant documents may be filtered out by a prior stage as insufficiently relevant, ultimately reducing recall and limiting the potential effectiveness. In this talk, I challenge the cascading paradigm by proposing methods that efficiently pull in additional potentially-relevant documents during the re-ranking process, using the long-standing Cluster Hypothesis. I demonstrate that these methods can improve the efficiency and effectiveness of both bi-encoder and cross-encoder retrieval models at various operational points. Cascading is dead, long live re-ranking!

Bio: Sean MacAvaney is a Lecturer in Machine Learning at the University of Glasgow and a member of the Terrier Team. His research primarily focuses on effective and efficient neural retrieval. He completed his PhD at Georgetown University in 2021, where he was a member of the IR Lab and an ARCS Endowed Scholar. He was a co-recipient of the SIGIR 2023 Best Paper Award and the ECIR 2023 Best Short Paper Award.

To attend this talk via Zoom, click here. To obtain the passcode for this series, please see the event advertisement on the seminars email list or reach out to zamani [at] cs.umass.edu (Hamed Zamani). For any questions about this event with the Center for Intelligent Information Retrieval, please contact jean [at] cs.umass.edu (subject: CIIR%20Talk%20Series) (Jean Joyce).