UIRISC at S em E val-2023 Task 10: Explainable Detection of Online Sexism by Ensembling Fine-tuning Language Models (2023)

Abstract: Under the umbrella of anonymous social networks, many women have suffered from abuse, discrimination, and other sexist expressions online. However, exsiting methods based on keyword filtering and matching performed poorly on online sexism detection, which lacked the capability to identify implicit stereotypes and discrimination. Therefore, this paper proposes a System of Ensembling Fine-tuning Models (SEFM) at SemEval-2023 Task 10: Explainable Detection of Online Sexism. We firstly use four task-adaptive pre-trained language models to flag all texts. Secondly, we alleviate the data imbalance from two perspectives: over-sampling the labelled data and adjusting the loss function. Thirdly, we add indicators and feedback modules to enhance the overall performance. Our system attained macro F1 scores of 0.8538, 0.6619, and 0.4641 for Subtask A, B, and C, respectively. Our system exhibited strong performance across multiple tasks, with particularly noteworthy performance in Subtask B. Comparison experiments and ablation studies demonstrate the effectiveness of our system.

## introduction sexism refers to prejudice, stereotyping, or discrimination based on one's gender or sex, typically against women @xcite . sexist expressions cause gender stereotypes and discrimination, such as "whxxe" or "husbands. kill your piece of sxxt commie wives 1 ". especially with the widespread and fast propagation of social media, the negative impact of gender discrimination has been further exacerbated. online sexist texts can not only affect the user experience and community environment but also lead to offline violence, persecution even crimes, which may cause much harm to real society. it is essential to eliminate sexist expressions and build a harmonious community. for this reason, many previous studies have focused on capturing offensive posts and comments @xcite . these methods usually filtered texts by keywords matching e.g. lexicon-based models. however, the increasing number of active users made it ineffective of lexicon based methods. moreover, many expressions do not contain indicative words e.g "bixxh", but they still convey strong sexism and prejudice as well, such as "i always cancel as soon as the driver accepts my ride is a female. then immediately rebook 1 ". they both in turn affect the performance of sexism detection. in this paper, we propose a computational system named system of ensembling fine-tuning models (sefm) for semeval-2023 task 10: explainable detection of online sexism (edos) @xcite . sefm consists of three modules: data preprocessing, sexsim detection, and ensembling. in the data preprocessing module, we extend the original dataset by easy data augmentation (eda). the sexism detection model includes three improvements to enhance the model effect: sexism indicator for subtask a (sia), feedback for subtask b (fb), and fine-graind indicator for subtask c (fic). the codes will be open sourced foot_0 . the rest of the paper is organized as follows: section 2 gives a brief literature survey. section 3 introduces our system. section 4 describes the experimental setups, while section 5 demonstrates the results and makes the analysis. finally, we reach the conclusions in section 6. ## background semeval-2023 holds task 10: explainable detection of online sexism, which contains three sub-tasks to flag what is sexist content and explain why it is sexist, which aims to approach explainable sexism detection via the granularity of classification labels 3 . ## system overview the framework of our system is shown in figure 2 , and the detailed description for each part is presented as follows. ## results and analysis we submitted the scores predicted by the ensemble method introduced above. the official ranking is presented in ## conclusions this paper presents our system implementation submitted to the semeval-2023 task 10: explanable detection of online sexism. we propose an ensemble framework named system of ensembling fine-tuning models (sefm) that enhances system performance by pre-processing data, training taskadaptive plms, and adding indicator and fb modules. in the future, we plan to utilize the dataset further and improve our system by introducing the prompt module and fusion label vector to enhance the performance of online sexism detection.