Irony Detection in a Multilingual Context - Archive ouverte HAL Accéder directement au contenu
Communication Dans Un Congrès Année : 2020

Irony Detection in a Multilingual Context

Résumé

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.

Dates et versions

hal-02889008 , version 1 (03-07-2020)

Identifiants

Citer

Bilal Ghanem, Jihen Karoui, Farah Benamara, Paolo Rosso, Véronique Moriceau. Irony Detection in a Multilingual Context. 42nd European Conference on Information Retrieval (ECIR 2020), Apr 2020, online, Portugal. pp.141-149, ⟨10.1007/978-3-030-45442-5_18⟩. ⟨hal-02889008⟩
31 Consultations
0 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More