针对食品召回报告中的危险类别分类，通过文本增强提升少数类表现。
BrightCookies at SemEval-2025 Task 9: Exploring Data Augmentation for Food Hazard Classification
- 采用同义词替换、随机词交换、上下文词插入三种文本增强方法。
- 上下文词插入使少数危险类别的准确率提升6%，且在BERT模型上显著优于基线。
- 适用于需要提升少数类别分类性能的食品安全事件分析场景。
本文介绍为SemEval-2025任务9：食品危害检测挑战赛开发的系统。该共享任务旨在评估从食品召回事件报告中以两级粒度分类危害与产品的可解释分类系统。本文提出使用文本增强技术来改善少数类别表现，并在多种Transformer和机器学习模型上比较其效果。我们探索了三种词级数据增强方法：同义词替换、随机词交换和上下文词插入。结果显示，Transformer模型整体表现更优。三种增强方法均未一致提升整体分类性能。但在细粒度类别中，使用BERT模型对比基线与每种增强模型时，发现统计上显著的改进（P < 0.05）。相比基线，上下文词插入增强使少数危险类别的预测准确率提升6%。这表明针对少数类别进行定向增强可有效提升Transformer模型性能。
原文摘要 · Abstract (English)
This paper presents our system developed for the SemEval-2025 Task 9: The Food Hazard Detection Challenge. The shared task's objective is to evaluate explainable classification systems for classifying hazards and products in two levels of granularity from food recall incident reports. In this work, we propose text augmentation techniques as a way to improve poor performance on minority classes and compare their effect for each category on various transformer and machine learning models. We explore three word-level data augmentation techniques, namely synonym replacement, random word swapping, and contextual word insertion. The results show that transformer models tend to have a better overall performance. None of the three augmentation techniques consistently improved overall performance for classifying hazards and products. We observed a statistically significant improvement (P < 0.05) in the fine-grained categories when using the BERT model to compare the baseline with each augmented model. Compared to the baseline, the contextual words insertion augmentation improved the accuracy of predictions for the minority hazard classes by 6%. This suggests that targeted augmentation of minority classes can improve the performance of transformer models.
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