CS-UM6P at SemEval-2022 Task 6: Transformer-based Models for Intended Sarcasm Detection in English and Arabic

TL;DR

This paper presents transformer-based models for detecting intended sarcasm in English and Arabic, achieving top performance in Arabic sarcasm detection tasks at SemEval-2022.

Contribution

The authors developed and evaluated deep learning models leveraging pre-trained transformers for sarcasm detection in two languages, with state-of-the-art results in Arabic.

Findings

Best performance on Arabic sub-task A

Second place in Arabic sub-task B

Ranked 7th and 11th in sub-task C for Arabic and English

Abstract

Sarcasm is a form of figurative language where the intended meaning of a sentence differs from its literal meaning. This poses a serious challenge to several Natural Language Processing (NLP) applications such as Sentiment Analysis, Opinion Mining, and Author Profiling. In this paper, we present our participating system to the intended sarcasm detection task in English and Arabic languages. Our system\footnote{The source code of our system is available at \url{https://github.com/AbdelkaderMH/iSarcasmEval}} consists of three deep learning-based models leveraging two existing pre-trained language models for Arabic and English. We have participated in all sub-tasks. Our official submissions achieve the best performance on sub-task A for Arabic language and rank second in sub-task B. For sub-task C, our system is ranked 7th and 11th on Arabic and English datasets, respectively.