HITSZQ at S em E val-2023 Task 10: Category-aware Sexism Detection Model with Self-training Strategy (2023)

Abstract: This paper describes our system used in the SemEval-2023 \textit{Task 10 Explainable Detection of Online Sexism (EDOS)}. Specifically, we participated in subtask B: a 4-class sexism classification task, and subtask C: a more fine-grained (11-class) sexism classification task, where it is necessary to predict the category of sexism. We treat these two subtasks as one multi-label hierarchical text classification problem, and propose an integrated sexism detection model for improving the performance of the sexism detection task. More concretely, we use the pre-trained BERT model to encode the text and class label and a hierarchy-relevant structure encoder is employed to model the relationship between classes of subtasks B and C. Additionally, a self-training strategy is designed to alleviate the imbalanced problem of distribution classes. Extensive experiments on subtasks B and C demonstrate the effectiveness of our proposed approach.

## introduction sexism is especially rampant online due to the anonymity of the internet, making the online community unfriendly towards women @xcite @xcite . detecting sexism on social media is meaningful in building a more harmonious internet world. sexism detection @xcite can be considered as a particular sentiment classification task, applied to predict the categories (the detailed categories are shown in figure 1 ) of prejudice or discrimination based on a person's gender in the whole sentence. for example, the sentence "as roosh said having discussions with woman is a waste of time anyway." is a sexist comment, which belongs to a kind of descriptive attack expressing derogation against women. semeval-2023 task 10 (kirk et al., 2023) (explainable detection of online sexism, edos), is an online sexism detection and classification task. ## conclusion in this paper, we propose a model to address subtasks b and c of semeval-2023 task 10 explainable detection of online sexism. our goal is to identify sexist statements and explain why they are sexist. the model treats the two subtasks as one multi-label hierarchical classification problem to capture class features from two perspectives: label texts and class hierarchy, utilizing a pre-trained bert model and a graph convolution neural network respectively. our model is proven effective by extensive comparison experiments. online sexism detection is getting more and more attention as people begin to realize its importance to the internet community. more efforts need to be put into this cause to make the internet world better for everybody.