Multilingual Irony Detection with Dependency Syntax and Neural Models

• Main Authors: Cignarella, Alessandra Teresa, Basile, Valerio, Sanguinetti, Manuela, Bosco, Cristina, Rosso, Paolo, Benamara, Farah
• Format: Journal Article
• Language: English
• Published: 11-11-2020
• Subjects: Computer Science - Computation and Language
• DOI: 10.48550/arxiv.2011.05706

This paper presents an in-depth investigation of the effectiveness of
dependency-based syntactic features on the irony detection task in a
multilingual perspective (English, Spanish, French and Italian). It focuses on
the contribution from syntactic knowledge, exploiting linguistic resources
where syntax is annotated according to the Universal Dependencies scheme. Three
distinct experimental settings are provided. In the first, a variety of
syntactic dependency-based features combined with classical machine learning
classifiers are explored. In the second scenario, two well-known types of word
embeddings are trained on parsed data and tested against gold standard
datasets. In the third setting, dependency-based syntactic features are
combined into the Multilingual BERT architecture. The results suggest that
fine-grained dependency-based syntactic information is informative for the
detection of irony.