In the vast realm of Natural Language Processing, one of the areas that still presents a bottleneck is Figurative Language Understanding. However, this field is of fundamental interest both theoretically and practically; in all new applications of Artificial Intelligence, there is an increasing demand for a correct understanding of human language, which is naturally rich in rhetorical figures. To comprehend this figurative language, it is necessary for the model used to grasp all the different nuances and reasons, going beyond the mere literal meaning. Until now, attempts to solve this problem have primarily involved training specific models on large databases of rhetorical figures.