DUTIR at S em E val-2023 Task 10: Semi-supervised Learning for Sexism Detection in E nglish (2023)

Abstract: Sexism is an injustice afflicting women and has become a common form of oppression in social media. In recent years, the automatic detection of sexist instances has been utilized to combat this oppression. The Subtask A of SemEval-2023 Task 10, Explainable Detection of Online Sexism, aims to detect whether an English-language post is sexist. In this paper, we describe our system for the competition. The structure of the classification model is based on RoBERTa, and we further pre-train it on the domain corpus. For fine-tuning, we adopt Unsupervised Data Augmentation (UDA), a semi-supervised learning approach, to improve the robustness of the system. Specifically, we employ Easy Data Augmentation (EDA) method as the noising operation for consistency training. We train multiple models based on different hyperparameter settings and adopt the majority voting method to predict the labels of test entries. Our proposed system achieves a Macro-F1 score of 0.8352 and a ranking of 41/84 on the leaderboard of Subtask A.

## introduction with the evolution of the internet, people are free to express their opinions on social media. this may lead to the mass dissemination of hateful or abusive messages @xcite , such as sexist expressions that people use intentionally or unintentionally. sexism is a complex phenomenon, broadly defined as "prejudice, stereotyping, or discrimination, typically against women, on the basis of sex." 1 sexism takes many forms, including blatant, covert, and subtle sexism @xcite , causing sexism detection to be a challenge for the filtering mechanism of platforms. the @xcite ) is an explainable detection task of online sexism, and we participate in subtask a, the binary classification task, to detect whether an english-language 1 oxford english dictionary post is sexist or not. the competition organizers released datasets collected from gab and reddit, containing 20,000 labeled entries and 2 million unlabeled entries. for the sexism detection task, we build a system based on the pre-trained language model roberta. firstly, we further pre-train roberta on the unlabeled corpus to obtain our domain-specific encoder. furthermore, we reuse the abundant unlabelled data by unsupervised data augmentation (uda) @xcite approach for consistency training, which is a common practice in semi-supervised learning. as for the noising operation, we apply the easy data augmentation (eda) @xcite ) method to transform the unlabeled examples into their noised versions. the consistency training can propagate label information from labeled to unlabeled examples to enhance the generalization of the model. to further improve the robustness of our system, we train models based on different hyperparameter settings and perform majority voting on the predicted labels to obtain the final prediction results. the structure of this paper is as follows: we first take a brief overview of related work in section 2, and then describe our proposed system in section 3. we introduce the details of our experiments in section 4, including the experimental settings, results, and discussions. finally, a brief conclusion is presented in section 5. ## related work as more and more people suffer or witness sexism on social media, automatically detecting sexist posts can help combat this phenomenon @xcite . many studies have been proposed to identify gender-based violence in texts. for misogyny, @xcite build a corpus of misogynous tweets labeled from different perspectives and conduct exploratory investigations. aiming at multi-label fine-grained sexism classification, training encoder classifier labeled text � unlabeled text � augmentation unlabeled text � kl divergence loss label � * final loss cross-entropy loss m j m 1 not sexist sexist not sexist sexist sexist sexist prediction text sexist m 0 m 2 m 3 m 4 m 5 pre-training roberta-base token sequence masking masked sequence masked tokens prediction abburi et al. (2020) introduce a semi-supervised multi-task neural approach. inspired by speech acts theory and discourse analysis studies, chiril et al. (2020) propose a new characterization of sexist content. besides, some works (badjatiya et al., 2017; davidson et al., 2017; espinosa anke et al., 2019) regard sexism as a category of hate and detect it using hate speech classification methods. ## system description given a sentence @xmath0, • • • , x n } consisting of n tokens, the sentence is embedded as a vector by an encoder. we use a sentence-level embedding z to represent the entire sentence and feed it into a linear binary classifier to get the predicted label y. the goal of our system can be formulated as training a model p θ (y|x), where the network parameters are denoted as θ, to predict the ground-truth label y * for a given x. as shown in figure 1 , the general implementation of our system can be described as 3 phases: pretraining, training, and prediction. in pre-training phase, we pre-train the language model on a relevant corpus so that the language model can learn more domain knowledge. then we adopt the pre-trained language model as our domain-specific encoder. in training phase, we attach a linear classifier to the encoder and fine-tune the model through semi-supervised training. in prediction phase, we use the majority voting method to predict the final classification labels. ## conclusion this paper describes our approach in semeval-2023 task 10 subtask a to detect sexism in english-language social media posts. our system employs self-supervised and semi-supervised learning to pre-train and fine-tune the pre-trained language model roberta-base respectively. we demonstrate the effectiveness of our system. there are still considerable areas for improvement in our system, and how to better utilize the unlabeled data is an essential issue for us to explore in the future.