Ustnlp16 at SemEval-2025 Task 9: Improving Model Performance through Imbalance Handling and Focal Loss

• URL: http://arxiv.org/abs/2505.00021v1
• Date: Thu, 24 Apr 2025 16:35:44 GMT
• Title: Ustnlp16 at SemEval-2025 Task 9: Improving Model Performance through Imbalance Handling and Focal Loss
• Authors: Zhuoang Cai, Zhenghao Li, Yang Liu, Liyuan Guo, Yangqiu Song,
• Abstract summary: classification tasks often suffer from severe class imbalances, short and unstructured text, and overlapping semantic categories.<n>We present our system for SemEval- 2025 Task 9: Food Hazard Detection, which ad- dresses these issues by applying data augmenta- tion techniques to improve classification perfor- mance.
• Score: 38.70308073598037
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Classification tasks often suffer from imbal- anced data distribution, which presents chal- lenges in food hazard detection due to severe class imbalances, short and unstructured text, and overlapping semantic categories. In this paper, we present our system for SemEval- 2025 Task 9: Food Hazard Detection, which ad- dresses these issues by applying data augmenta- tion techniques to improve classification perfor- mance. We utilize transformer-based models, BERT and RoBERTa, as backbone classifiers and explore various data balancing strategies, including random oversampling, Easy Data Augmentation (EDA), and focal loss. Our ex- periments show that EDA effectively mitigates class imbalance, leading to significant improve- ments in accuracy and F1 scores. Furthermore, combining focal loss with oversampling and EDA further enhances model robustness, par- ticularly for hard-to-classify examples. These findings contribute to the development of more effective NLP-based classification models for food hazard detection.

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