This work addresses the need for linguistic resources that enable language models to understand and adapt to subjective and abstract concepts in the domain of moral values within texts. In light of the growing interest in the study of moral values and its limited exploration in Spanish-speaking contexts, this work addresses this gap by developing a novel Spanish-language corpus. Furthermore, the corpus’s development process ensures that the annotations capture a wide range of perspectives, resulting in a resource that reflects the diversity of moral interpretations in real-world contexts. Specifically, there are two main contributions. 1 The creation of the first large-scale Spanish corpus annotated according to Moral Foundations Theory. 2 We introduce an experimental framework that investigates how annotators’ religious orientations could shape moral annotation patterns and propagate to model behavior. To do so, we employ a prompt-based alignment method that improves moral detection regardless of religious alignment for which the model was trained. In this scenario, we explore whether language models can align moral interpretations across divergent belief orientations.

This paper describes the GSI-UPM system for SemEval-2019 Task 5, which tackles multilingual detection of hate speech on Twitter. The main contribution of the paper is the use of a method based on word embeddings and semantic similarity combined with traditional paradigms, such as n-grams, TF-IDF and POS. This combination of several features is fine-tuned through ablation tests, demonstrating the usefulness of different features. While our approach outperforms baseline classifiers on different sub-tasks, the best of our submitted runs reached the 5th position on the Spanish sub-task A.