Sustainable semantic similarity assessment

Abstract

The automatic semantic similarity assessment field has attracted much attention due to its impact on multiple areas of study. In addition, it is also relevant that recent advances in neural computation have taken the solutions to a higher stage. However, some inherent problems persist. For example, large amounts of data are still needed to train solutions, the interpretability of the trained models is not the most suitable one, and the energy consumption required to create the models seems out of control. Therefore, we propose a novel method to achieve significant results for a sustainable semantic similarity assessment, where accuracy, interpretability, and energy efficiency are equally important. We rely on a method based on multi-objective symbolic regression to generate a Pareto front of compromise solutions. After analyzing the output generated and comparing other relevant works published, our approach’s results seem to be promising.