SUTNLP at S em E val-2023 Task 10: RLAT -Transformer for explainable online sexism detection (2023)

Abstract: There is no simple definition of sexism, butit can be described as prejudice, stereotyping,or discrimination, especially against women,based on their gender. In online interactions,sexism is common. One out of ten Americanadults says that they have been harassed be-cause of their gender and have been the targetof sexism, so sexism is a growing issue. TheExplainable Detection of Online Sexism sharedtask in SemEval-2023 aims at building sexismdetection systems for the English language. Inorder to address the problem, we use largelanguage models such as RoBERTa and De-BERTa. In addition, we present Random LayerAdversarial Training (RLAT) for transformers,and show its significant impact on solving allsubtasks. Moreover, we use virtual adversar-ial training and contrastive learning to improveperformance on subtask A. Upon completionof subtask A, B, and C test sets, we obtainedmacro-F1 of 84.45, 67.78, and 52.52, respec-tively outperforming proposed baselines on allsubtasks. Our code is publicly available onGithub.

## introduction sexism refers to any negative, abusive, or discriminatory behavior, attitude, or language that targets individuals based on their gender, particularly women, and involves prejudice, stereotyping, or discrimination @xcite . in essence, sexism encompasses any negative, abusive, or discriminatory behavior or attitude that targets women based on their gender or a combination of their gender with other identity characteristics such as race, religion, or gender identity, for instance, black women, muslim women, or trans women @xcite . the internet is a breeding ground for sexism, with approximately one in ten american adults reporting that they have been harassed due to their gender, and have experienced sexist behavior @xcite . in order to promote a fair and inclusive online environment, it is vital to acknowledge and combat sexism on online platforms because of their significant scale, extensive reach, and powerful influence. 2 sexism poses a growing problem in the online environment, and its consequences can be severe, such as making online spaces inaccessible and perpetuating social injustices. to address this issue, various automated tools have been deployed to detect and assess sexist content. nonetheless, most of these tools only provide generic classifications without further explanation. identifying sexist content and explaining why it is offensive will increase an automated tool's interpretability, trustworthiness, and understanding of its decisions. this, in turn, will empower both users and moderators to make informed decisions and create a fair and inclusive online environment @xcite . the aim of the semeval-2023 explainable detection of online sexism shared task is to develop and implement sexism detection systems for the english language @xcite . in order to deal with the problem, we use large language models such as roberta @xcite and deberta @xcite . we introduce a novel method called random layer adversarial training (rlat) based on famous adversarial training and demonstrate that it has a positive impact on boosting performance across all subtasks. to the best knowledge of the authors, this is the first research using random layers of a network for adversarial training. as part of subtask a, we also use virtual adver-sarial training and contrastive learning to improve the model's performance. in subtask a, our team ranked 18th out of 84 teams, in subtask b, 9th out of 69 teams, and in subtask c, we ranked 6th out of 63 teams. in subtasks a and b, we outperform baseline methods by a substantial margin, and in subtask c, we outperform baseline by a large margin using the rlat. several experiments were conducted to examine the effect of the rlat on the current task. ## system overview overall we developed our models based on large pre-trained language models and random layer adversarial training. subtask a is built on random layer adversarial training, virtual adversarial training and contrastive learning. subtasks b and c are solved similarly and are dependent only on random layer adversarial training. so we describe our system in two different sections. ## conclusion this paper presents sutnlp's submission to semeval-2023 task 10 "explainable detection of online sexism" competition. to solve this problem, we use transformers and further propose random layer adversarial training (rlat) to boost the performance of the base models by large margins. we conducted experiments to show the superiority of rlat compared to conventional adversarial training used in nlp for the current task. for all subtasks, the performance of the model is evaluated by macro-f1. using macro-f1 criteria, appending the rlat to base transformers shows gains of 1.08 % in macro-f1 and 7.12 % macro-f1 for subtasks b and c respectively. in the future, we intend to analyse the rlat method to determine how it performs on other nlp tasks and further improve it.