This paper proposes the first multilingual (French, English and Arabic) and multicultural (Indo-European languages vs. less culturally close languages) irony detection system. We employ both feature-based models and neural architectures using monolingual word representation. We compare the performance of these systems with state-of-the-art systems to identify their capabilities. We show that these monolingual models trained separately on different languages using multilingual word representation or text-based features can open the door to irony detection in languages that lack of annotated data for irony.
@article{arxiv.2002.02427,
title = {Irony Detection in a Multilingual Context},
author = {Bilal Ghanem and Jihen Karoui and Farah Benamara and Paolo Rosso and Véronique Moriceau},
journal= {arXiv preprint arXiv:2002.02427},
year = {2020}
}