BrightCookies at SemEval-2025 Task 9: Exploring Data Augmentation for Food Hazard Classification
This work addresses a domain-specific challenge in food safety by improving classification of minority classes, but it is incremental as the augmentation techniques did not consistently enhance overall performance.
The paper tackled the problem of poor performance on minority classes in food hazard classification by exploring text augmentation techniques, finding that contextual word insertion improved accuracy for minority hazard classes by 6% with BERT models.
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.