Coco at S em E val-2023 Task 10: Explainable Detection of Online Sexism (2023)

Abstract: Sexism has become a growing concern on social media platforms as it impacts the health of the internet and can have negative impacts on society. This paper describes the coco system that participated in SemEval-2023 Task 10, Explainable Detection of Online Sexism (EDOS), which aims at sexism detection in various settings of natural language understanding. We develop a novel neural framework for sexism detection and misogyny that can combine text representations obtained using pre-trained language model models such as Bidirectional Encoder Representations from Transformers and using BiLSTM architecture to obtain the local and global semantic information. Further, considering that the EDOS dataset is relatively small and extremely unbalanced, we conducted data augmentation and introduced two datasets in the field of sexism detection. Moreover, we introduced Focal Loss which is a loss function in order to improve the performance of processing imbalanced data classification. Our system achieved an F1 score of 78.95\% on Task A - binary sexism.

## introduction social media platforms offer unparalleled communication and information-sharing abilities while providing an anonymous space for people of varying genders, ethnicities, races, and cultures to interact online. @xcite @xcite . as a result, there has been a rise in incidents, hostile behavior, and instances of harassment on social media platforms, especially sexism @xcite . sexism is connected to beliefs about the essential nature of women and men, as well as the roles they should assume in society. this hierarchical way of thinking can be deliberate and hostile, or it can be unintentional and take the form of unconscious bias. the semeval task 10 supports the development of english-language models for sexism detection that fine-grained classifications for sexist content from gab and reddit, which consists of three subtasks @xcite . as illustrated in fig. 1: (1) task a -binary sexism detection: it is a binary classification task which models are required to predict whether a text contains gender bias or not; our method employed bert @xcite ) as mono-lingual pre-trained language models, and various models including cnn, lstm and bil-stm for further feature extraction to obtain context semantic features. our contributions are summarized below. ## background in this section, we review the work on the classification of sexism or misogyny. we also pay attention to some work related to the identification of hate speech, because some of these works are applicable to sexism detection to some extent, and these methods treat sexism as a kind of hate @xcite . over the last few years, due to the exponential growth of user-generated content and the diverse range of behaviors towards women on social media, manually inspecting and moderating sexist content has become unfeasible @xcite . therefore, there has been a significant surge in academic research aimed at automatically detecting sexist behavior in both monolingual and multilingual scenarios in recent years @xcite . to tackle this issue, the first attempt was made by hewitt et al. @xcite , who studied the manual detection of sexist tweets. @xcite conducted the first survey of automatic sexism detection in social media. in an effort to address the issue of unintended bias in machine learning models for sexism detection, a new method proposed by nozza et al. @xcite try to measure and mitigate this bias. agrawal et al. @xcite investigated the detection of cyberbullying on social media platforms using deep learning methods. unlike previous studies on hate detection, they attempt to identifying sexism and misogyny, which encompass hate speech targeted towards women but is not limited to hate @xcite . recently, academic work research on the detection of sexism or misogyny has been conducted extensively, particularly in multilingual and crossdomain scenarios @xcite . the majority of studies focusing on the detection of sexism or misogyny against women utilize supervised approaches. in their research, basile et al. @xcite used the support vector machine to perform sexism detection, with features based on word and character n-grams. buscaldi et al. @xcite employed random decision forests which is an ensemble learning method by constructing a multitude of decision trees at training and utilized locally weighted character n-grams as features for the detection of sexism or misogyny. deep learning approaches based on pre-trained models(ptm) have become more popular, such as deberta @xcite , roberta @xcite , xlm-roberta @xcite , xlnet @xcite , and spanbert @xcite , which have made state-of-the-art achievements in different languages @xcite . parikh et al. @xcite built a bert-based neural architecture to perform sexism detection and showed the models perform better than classic supervised approaches. there are many opensource multilingual datasets in the field of detection of sexism or hate speech, such as exist-2022 (rodríguez-sánchez et al., 2022), trac-2020 @xcite , hateval @xcite and ami@ibereval @xcite . our model introduced the additional datasets, exist-2022 and trac-2020, which help with the sexism detection task. our approach focuses on pre-trained models (ptm) architecture, combines using pre-trained language model bert and bilstm network, which is inspired by rodrıguez (rodríguez-sánchez et al., 2020) and parikh @xcite . ## system overview an overview of the architecture of our proposed model is depicted in figure 2 . the model we propose comprises the following components: bert is used to extracting the embedding sequence of the word sequence. at the same time, the embedding sequence output by bert is further feature extracted through bilstm to obtain context semantic information, and then sent to the output layer. the output layer is composed of a fully connected layer and a softmax layer, in which the dimension of the feature vector is adjusted in the fully connected layer and the softmax classifier is used to classify to achieve sexism detection. ## conclusion in this paper, we introduce the system developed and evaluated for semeval 2023 task 10 and employ a novel neural framework that combines text representations obtained using pre-trained language model bert, and using bilstm architecture to obtain context semantic information for better sexism detection. in addition, in order to alleviate the problem of data imbalance, we introduced additional datasets and tried different loss functions, which improved the performance of the model. exploring approaches for detecting sexism in multilingual settings shows promise as a potential direction for future research.