Multilingual Irony Detection with Dependency Syntax and Neural Models

TL;DR

This study explores how dependency-based syntactic features can improve multilingual irony detection across English, Spanish, French, and Italian using various models and resources.

Contribution

It introduces a comprehensive analysis of dependency syntax features in irony detection, combining classical ML, word embeddings, and multilingual BERT in a multilingual setting.

Findings

Dependency syntactic features enhance irony detection accuracy.

Fine-grained syntax information is particularly informative.

Multilingual models benefit from syntactic features.

Abstract

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.