Retrieval for Extremely Long Queries and Documents with RPRS: A Highly Efficient and Effective Transformer-based Re-Ranker
ACM Transactions on Information Systems, vol. 42, pp. 1–32
Abstract
Retrieval with extremely long queries and documents is a well-known and challenging task in information retrieval and is commonly known as Query-by-Document (QBD) retrieval. Specifically designed Transformer models that can handle long input sequences have not shown high effectiveness in QBD tasks in previous work. We propose a Re-Ranker based on the novel Proportional Relevance Score (RPRS) to compute the relevance score between a query and the top- k candidate documents. Our extensive evaluation shows RPRS obtains significantly better results than the state-of-the-art models on five different datasets. Furthermore, RPRS is highly efficient, since all documents can be pre-processed, embedded, and indexed before query time that gives our re-ranker the advantage of having a complexity of O(N) , where N is the total number of sentences in the query and candidate documents. Furthermore, our method solves the problem of the low-resource training in QBD retrieval tasks as it does not need large amounts of training data and has only three parameters with a limited range that can be optimized with a grid search even if a small amount of labeled data is available. Our detailed analysis shows that RPRS benefits from covering the full length of candidate documents and queries.
Authors 5
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Arian Askari Aachen
Affiliation as printed
Leiden University, Leiden, The Netherlands
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Suzan Verberne Aachen
Affiliation as printed
Leiden University, Leiden, The Netherlands
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Amin Abolghasemi Aachen
Affiliation as printed
Leiden University, Leiden, The Netherlands
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Wessel Kraaij Aachen
Affiliation as printed
Leiden University, Leiden, The Netherlands
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Affiliation as printed
University of Milano-Bicocca, Milano, Italy
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