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.

“Chinese Frame Semantic Parsing (CFSP) is a semantic parsing task based on Chinese FrameNet(CFN). This paper presents a solution for CCL2023-Eval Task 3. We first attempt various pre-trained models for different sub-tasks. Then, we explore multiple approaches to solving eachtask from the perspectives of feature engineering, model structure, and other tricks. Finally,we provide prospects for the task and propose potential alternative solutions. We conductedextensive comparative experiments to validate the effectiveness of our system. Introduction”