A Transformer-based approach to Irony and Sarcasm detection

TL;DR

This paper introduces a transformer-based neural network combined with RCNN to effectively identify irony, sarcasm, and metaphor in social media texts, achieving state-of-the-art results across multiple benchmark datasets.

Contribution

It presents a novel hybrid deep learning model that enhances transformer architectures with RCNN for figurative language detection in short texts.

Findings

Achieves state-of-the-art performance on four benchmark datasets.

Outperforms existing methods by a large margin.

Minimal data preprocessing required.

Abstract

Figurative Language (FL) seems ubiquitous in all social-media discussion forums and chats, posing extra challenges to sentiment analysis endeavors. Identification of FL schemas in short texts remains largely an unresolved issue in the broader field of Natural Language Processing (NLP), mainly due to their contradictory and metaphorical meaning content. The main FL expression forms are sarcasm, irony and metaphor. In the present paper we employ advanced Deep Learning (DL) methodologies to tackle the problem of identifying the aforementioned FL forms. Significantly extending our previous work [71], we propose a neural network methodology that builds on a recently proposed pre-trained transformer-based network architecture which, is further enhanced with the employment and devise of a recurrent convolutional neural network (RCNN). With this set-up, data preprocessing is kept in minimum. The performance of the devised hybrid neural architecture is tested on four benchmark datasets, and contrasted with other relevant state of the art methodologies and systems. Results demonstrate that the proposed methodology achieves state of the art performance under all benchmark datasets, outperforming, even by a large margin, all other methodologies and published studies.