Tackling Irony Detection using Ensemble Classifiers

Christoph Turban, Udo Kruschwitz


Abstract
Automatic approaches to irony detection have been of interest to the NLP community for a long time, yet, state-of-the-art approaches still fall way short of what one would consider a desirable performance. In part this is due to the inherent difficulty of the problem. However, in recent years ensembles of transformer-based approaches have emerged as a promising direction to push the state of the art forward in a wide range of NLP applications. A different, more recent, development is the automatic augmentation of training data. In this paper we will explore both these directions for the task of irony detection in social media. Using the common SemEval 2018 Task 3 benchmark collection we demonstrate that transformer models are well suited in ensemble classifiers for the task at hand. In the multi-class classification task we observe statistically significant improvements over strong baselines. For binary classification we achieve performance that is on par with state-of-the-art alternatives. The examined data augmentation strategies showed an effect, but are not decisive for good results.
Anthology ID:
2022.lrec-1.754
Volume:
Proceedings of the Thirteenth Language Resources and Evaluation Conference
Month:
June
Year:
2022
Address:
Marseille, France
Editors:
Nicoletta Calzolari, Frédéric Béchet, Philippe Blache, Khalid Choukri, Christopher Cieri, Thierry Declerck, Sara Goggi, Hitoshi Isahara, Bente Maegaard, Joseph Mariani, Hélène Mazo, Jan Odijk, Stelios Piperidis
Venue:
LREC
SIG:
Publisher:
European Language Resources Association
Note:
Pages:
6976–6984
Language:
URL:
https://aclanthology.org/2022.lrec-1.754
DOI:
Bibkey:
Cite (ACL):
Christoph Turban and Udo Kruschwitz. 2022. Tackling Irony Detection using Ensemble Classifiers. In Proceedings of the Thirteenth Language Resources and Evaluation Conference, pages 6976–6984, Marseille, France. European Language Resources Association.
Cite (Informal):
Tackling Irony Detection using Ensemble Classifiers (Turban & Kruschwitz, LREC 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.lrec-1.754.pdf
Code
 christophturban/lrec-irony-detection-ensemble-classifier