Rational Retrieval Acts: Leveraging Pragmatic Reasoning to Improve Sparse Retrieval

6 pages - 2 figures - conference: accepted at SIGIR 2025 ; International audience ; 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 tokendocument 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.