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arXiv · 2010.00200

RRF102: Meeting the TREC-COVID Challenge with a 100+ Runs Ensemble

Michael Bendersky·Honglei Zhuang·Ji Ma·Shuguang Han·Keith Hall·Ryan McDonald
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Abstract

In this paper, we report the results of our participation in the TREC-COVID challenge. To meet the challenge of building a search engine for rapidly evolving biomedical collection, we propose a simple yet effective weighted hierarchical rank fusion approach, that ensembles together 102 runs from (a) lexical and semantic retrieval systems, (b) pre-trained and fine-tuned BERT rankers, and (c) relevance feedback runs. Our ablation studies demonstrate the contributions of each of these systems to the overall ensemble. The submitted ensemble runs achieved state-of-the-art performance in rounds 4 and 5 of the TREC-COVID challenge.

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Michael Bendersky, Honglei Zhuang, Ji Ma, Shuguang Han, Keith Hall, Ryan McDonald. 2020-10-01. RRF102: Meeting the TREC-COVID Challenge with a 100+ Runs Ensemble. https://arxiv.org/abs/2010.00200

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