通过数据增强与焦点损失提升食物危害分类准确率
Ustnlp16 at SemEval-2025 Task 9: Improving Model Performance through Imbalance Handling and Focal Loss
- 用EDA和过采样处理数据不平衡问题
- 结合焦点损失使模型对难分类样本更敏感
- 适用于短文本、类别重叠的食品安全检测场景
分类任务常因数据分布不均带来挑战，尤其在食物危害检测中，存在严重类别不平衡、文本短且无结构、语义类别重叠等问题。本文介绍我们在SemEval-2025任务9：食物危害检测中的系统方案，通过数据增强技术提升分类性能。采用BERT和RoBERTa等基于Transformer的模型作为主干分类器，探索随机过采样、Easy Data Augmentation（EDA）及焦点损失等多种数据平衡策略。实验表明，EDA能有效缓解类别不平衡，显著提升准确率与F1分数。进一步地，将焦点损失与过采样、EDA结合，增强了模型对难分类样本的鲁棒性。这些成果有助于构建更高效的基于NLP的食物危害检测分类模型。
原文摘要 · Abstract (English)
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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