Towards the Evaluation of Information Retrieval Systems on Evolving Datasets with Pivot Systems

Gabriela Nicole González Sáez, Philippe Mulhem, Lorraine Goeuriot

Abstract: Evaluation of information retrieval systems follows the Cranfield paradigm, where the evaluation of several IR systems relies on a common evaluation environment (test collection and evaluation settings). The Cranfield paradigm requires the evaluation environment (EE) to be strictly identical to compare system’s performances. For those cases where such paradigm cannot be used, e.g. when we do not have access to the code of the systems, we consider an evaluation framework that allows for slight changes in the EEs, as the evolution of the document corpus or topics. To do so, we propose to compare systems evaluated on different environments using a reference system, called pivot. In this paper, we present and validate a method to select a pivot, which is used to construct a correct ranking of systems evaluated in different environments. We test our framework on the TREC-COVID test collection, which is composed of five rounds of growing topics, documents and relevance judgments. The results of our experiments show that the pivot strategy can propose a correct ranking of systems evaluated in an evolving test collection.