Rational Retrieval Acts: Leveraging Pragmatic Reasoning to Improve Sparse Retrieval

• URL: http://arxiv.org/abs/2505.03676v1
• Date: Tue, 06 May 2025 16:21:10 GMT
• Title: Rational Retrieval Acts: Leveraging Pragmatic Reasoning to Improve Sparse Retrieval
• Authors: Arthur Satouf, Gabriel Ben Zenou, Benjamin Piwowarski, Habiboulaye Amadou Boubacar, Pablo Piantanida,
• Abstract summary: Current sparse neural information retrieval methods do not take into account the document collection and the complex interplay between different term weights when representing a single document.<n>We show how the Rational Speech Acts (RSA), a linguistics framework used to minimize the number of features to be communicated when identifying an object in a set, can be adapted to the IR case.<n>Experiments show that incorporating RSA consistently improves multiple sparse retrieval models and state-of-the-art performance on out-of-domain datasets.
• Score: 29.652506774818267
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Current sparse neural information retrieval (IR) methods, and to a lesser extent more traditional models such as BM25, do not take into account the document collection and the complex interplay between different term weights when representing a single document. In this paper, we show how the Rational Speech Acts (RSA), a linguistics framework used to minimize the number of features to be communicated when identifying an object in a set, can be adapted to the IR case -- and in particular to the high number of potential features (here, tokens). RSA dynamically modulates token-document interactions by considering the influence of other documents in the dataset, better contrasting document representations. Experiments show that incorporating RSA consistently improves multiple sparse retrieval models and achieves state-of-the-art performance on out-of-domain datasets from the BEIR benchmark. https://github.com/arthur-75/Rational-Retrieval-Acts

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