SSS at SemEval-2023 Task 10: Explainable Detection of Online Sexism using Majority Voted Fine-Tuned Transformers

Proceedings of the The 17th International Workshop on Semantic Evaluation (SemEval-2023), pages 1231–1236 July 13-14, 2023 ©2023 Association for Computational Linguistics SSS at SemEval-2023 Task 10: Explainable Detection of Online Sexism using Majority Voted Fine-Tuned Transformers Sriya Rallabandi Sanchit Singhal Manipal Institute of TechnologyPratinav Seth Manipal Academy of Higher Education, Manipal, India {sriyarallabandi,sanchitsinghal,seth.pratinav}@gmail.com Abstract This paper describes our submission to Task 10 at SemEval 2023-Explainable Detection of On- line Sexism (EDOS) Kirk et al. (2023), divided into three subtasks. The recent rise in social me- dia platforms has seen an increase in dispropor- tionate levels of sexism experienced by women on social media platforms. This has made de- tecting and explaining online sexist content more important than ever to make social me- dia safer and more accessible for women. Our approach consists of experimenting and finetun- ing BERT-based models and using a Majority Voting ensemble model that outperforms in- dividual baseline model scores. Our system achieves a macro F1 score of 0.8392 for Task A, 0.6092 for Task B and 0.4319 for Task C and the code is available on GitHub.1 1 Introduction Social media has become a free and powerful tool for communication, allowing people to share their ideas and connect with others worldwide. For women, social media has provided a platform for bringing visibility, empowering, and educating peo- ple on women’s issues. However, with an increase in the number of users, there is a significant in- crease in sexism on these platforms has led to on- line harassment and disinformation, making social media less accessible and unwelcoming, continu- ing to uphold unequal power dynamics and unfair treatment within society. Social media’s anonymity and ease of access have made it easier for individu- als to spread sexist content online. This has led to a growing interest and needs to detect and identify online content as sexist and explain why that con- tent is sexist. Natural Language Processing (NLP) techniques have shown promise in enabling the au- tomated detection of sexist language and providing insights into its nature of it. This paper describes our contributions to SemEval 1https://github.com/sriya26/SemEval 2023 Task 10:Explainable Detection of Online Sex- ism Kirk et al. (2023), which proposed a new dataset of sexist content from Gab and Reddit to detect and explain online sexist content through three hierarchical classification subtasks. Task 10 of SemEval 2023 includes three hierarchi- cal subtasks: Task A, which is a binary classifica- tion task to classify whether a post is sexist or not sexist, Task B, a multi-class classification task for sexist posts which further classifies them into four categories and Task C, another multi-class classi- fication task to classify the sexist posts into one of eleven fine grained vectors. We conducted de- tailed experiments on BERT-based models, such as RoBERTa Liu et al. (2019) and DeBERTaV3 V3 He et al. (2021), using different training meth- ods and loss functions to reduce class imbalance along with hyperparameter tuning. We then en- sembled these individual models using a majority voting ensemble and …

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