DS at S em E val-2023 Task 10: Explaining Online Sexism using Transformer based Approach (2023)

Abstract: In this paper, I describe the approach used in the SemEval 2023 - Task 10 Explainable Detection of Online Sexism (EDOS) competition (Kirk et al., 2023). I use different transformermodels, including BERT and RoBERTa which were fine-tuned on the EDOS dataset to classify text into different categories of sexism. I participated in three subtasks: subtask A is to classify given text as either sexist or not, while subtask B is to identify the specific category of sexism, such as (1) threats, (2) derogation, (3) animosity, (4) prejudiced discussions. Finally, subtask C involves predicting a finegrained vector representation of sexism, which included information about the severity, target and type of sexism present in the text. The use of transformer models allows the system to learn from the input data and make predictions on unseen text. By fine-tuning the models on the EDOS dataset, the system can improve its performance on the specific task of detecting online sexism. I got the following macro F1 scores: subtask A:77.16, subtask B: 46.11, and subtask C: 30.2.

## introduction sexism refers to any form of discrimination or prejudice against an individual based on their gender and can manifest in various ways, including harassment, stereotyping, and unequal treatment. it can be directed towards women, as well as individuals who identify as non-binary or gender nonconforming. the intersection of sexism with other forms of discrimination, such as racism or homophobia, can result in even greater harm to individuals who experience multiple forms of marginalization. these actions not only harm individual women but also create a toxic online culture that perpetuates social injustices and asymmetries. online sexism also contributes to the underrepresentation of women and other marginalized groups in certain online spaces and industries, limiting their opportunities to participate fully in the digital world. although automated tools are now extensively employed to detect and evaluate sexist content at a large scale, most of these tools merely provide generic and high-level classifications without any additional clarification. this is a common limitation of many automated tools that are used to detect and evaluate sexist content online. while these tools can be useful in identifying patterns of sexist language or behavior, they often lack the contextual understanding necessary to provide more nuanced classifications or explanations. this can be a problem because online sexism can take many different forms, and what may be considered sexist in one context may not be in another. therefore, it is essential to have tools that can accurately identify and contextualize instances of online sexism, rather than relying solely on high-level classifications. to enhance interpretability, trust, and comprehension of the decisions made by automated tools, it is important to flag the sexist content along with an explanation of why it is considered sexist. this approach empowers both the moderators and users of the online space. providing explanations for why certain content is flagged as sexist is important for enhancing the interpretability, trust, and comprehension of the decisions made by automated tools. this approach can help moderators and users better understand why certain content is being flagged, and how they can work to improve the online space to prevent future instances of online sexism. in addition to providing explanations for flagged content, it is also important to ensure that the explanations are clear and accessible to a wide range of users. this may involve providing additional context or examples to help users better understand why certain language or behavior is considered sexist, as well as using plain language and avoiding technical jargon to ensure that the explanations are easy to understand. by empowering both moder-ators and users to better understand and address instances of online sexism, we can work towards creating safer and more inclusive online spaces that are welcoming to everyone, regardless of their gender or other identity attributes. in this paper, i describe the approach used in three subtasks of the semeval 2023 task 10: explainable detection of online sexism (edos) @xcite . the following are the details of three hierarchical subtasks. ## related work i divide the related work into two components: hate speech detection and online sexism detection. ## experiments our approach (roberta) utilizes the pre-trained weights of the roberta model and fine-tunes it on the specific task of detecting sexism in text. now i describe the fine-tuning process and loss functions used in subtask a for the proposed model. to fine-tune the model, a fully connected layer was added on top of the pooled output, and a sigmoid activation function was used to output the probability of each class as either 1 or 0. the proposed the experiments were conducted on the training and dev sets provided by the task organizers for each respective subtask. the training set for subtask a consists of 14000 samples, and subtasks b and c consist of 3398 and 3398 samples, respectively. the test set for subtask a consists of 4000 samples, and subtasks b and c consist of 970 samples, respectively. additionally, a dev set for subtask a consists of 2800 samples, and subtasks b and c consist of 970 samples, respectively. for all the subtasks i used the train set for training, the dev set for validation, and the test set for testing. the models were fine-tuned using the adam optimizer for 25 epochs with a batch size of 32 and a learning rate of 5e-6, with a default epsilon value of 1e-6. i used roberta large in our experiments. the evaluation of the models was performed using the macro f1 score for all the subtasks, including a, b, and c.