LISACT eam at S em E val-2022 Task 6: A Transformer based Approach for Intended Sarcasm Detection in E nglish Tweets (2022)

Abstract: In this paper, we present our system and findings for SemEval-2022 Task 6 - iSarcasmEval: Intended Sarcasm Detection in English. The main objective of this task was to identify sarcastic tweets. This task was challenging mainly due to (1) the small training dataset that contains only 3468 tweets and (2) the imbalanced class distribution (25% sarcastic and 75% non-sarcastic). Our submitted model (ranked eighth on Sub-Task A and fifth on Sub-Task C) consists of a Transformer-based approach (BERTweet model).

## introduction the cambridge dictionary 1 defines sarcasm as "the use of remarks that clearly mean the opposite of what they say, made in order to hurt someone's feelings or to criticize something in a humorous way." due to the web openness, sarcastic content becomes very frequent in social media and ecommerce platforms, which may cause misunderstandings. furthermore, identifying such content is a very challenging task even for humans @xcite . also, it could impact some natural language processing tasks such as sentiment analysis @xcite @xcite @xcite @xcite . we introduce the following example: "the movie was enjoyable to the point that i clapped because it is finished." for an opinion mining system, this sentence could be considered positive. however, the author expresses a negative judgment against the movie since the expression "i clapped because it is finished" means that it was boring. * contributed equally 1 https://dictionary.cambridge.org/fr/ dictionnaire/anglais/sarcasm for this reason, semeval 2022 set up task 6: isarcasmeval -intended sarcasm detection in english and arabic to detect sarcastic and nonsarcastic tweets @xcite . our submitted system consisted of a pre-trained transformer model for english tweets named bertweet @xcite , secured 8th and 5th positions respectively on sub-task a and sub-task c leaderboard. the rest of the paper is structured in the following manner: section 2 provides the data structure and the main objective of each sub-task. section 3 describes our system. section 4 details the experiments. and finally, section 5 concludes this paper. ## task description the organizers of this task introduced two tweet datasets for both english and arabic languages that contain: this task consists mainly of three sub-tasks for the english dataset and two sub-tasks for the arabic dataset where sub-task a aims at determining whether a tweet is sarcastic or non-sarcastic, sub-task b, which is available for english only, is a binary multi-label classification task that intends to determine which ironic speech category a sarcastic tweet belongs to if any, and finally, sub-task c that takes two inputs: a sarcastic tweet and its non-sarcastic rephrase, and focuses on identifying the sarcastic one between them. ## system description in this section, we describe our proposed system that tackles sub-task a and sub-task c english. ## experimental results we experimented our model on the semeval 2022 task 6: isarcasmeval -intended sarcasm detection in english sub-task a and sub-task c datasets. all our experiments have been conducted in google colab environment foot_2 , the following libraries: transformers -hugging face foot_3 @xcite , scikit-learn foot_4 @xcite , and keras foot_5 were used to train and to asses the performance of our model. ## conclusion in this paper, we described our approach for tackling sub-task a and sub-task c of semeval 2022 task 6: isarcasmeval -intended sarcasm detection in english. our submitted system consisted of a pre-trained transformer model for english tweets named bertweet, secured 8th and 5th positions respectively on sub-task a and sub-task c leaderboard. since the top-ranked system for the english sub-task a scored about 0.6052 f1-score for the sarcastic class, future studies and works will focus on improving the performance of sarcasm detection tasks by adopting other approaches such as data augmentation and oversampling.