Irony Detection in a Multilingual Context
This work addresses the challenge of irony detection for multilingual and multicultural contexts, but it appears incremental as it builds on existing methods without introducing a new paradigm.
The paper tackled the problem of irony detection across multiple languages and cultures by proposing the first multilingual system for French, English, and Arabic, using feature-based and neural models with monolingual word representations; it showed that these models can enable irony detection in languages lacking annotated data, though no concrete performance numbers were provided.
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.