Zuifeng at S em E val-2025 Task 9: Multitask Learning with Fine-Tuned R o BERT a for Food Hazard Detection (2025)

Abstract: This paper describes our system used in theSemEval-2025 Task 9 The Food Hazard Detec-tion Challenge. Through data processing thatremoves elements and shared multi-task archi-tecture improve the performance of detection.Without complex architectural modificationsthe proposed method achieves competitive per-formance with 0.7835 Marco F1-score on sub-task 1 and 0.4712 Marco F1-score on sub-task2. Comparative experiments reveal that jointprediction outperforms separate task trainingby 1.3% F1-score, showing the effectiveness ofmulti-task learning of this challenge

## introduction food safety hazards pose persistent threats to public health and economic security, driving societal demand for real-time detection of emerging risks. while early incident reports proliferate on social media platforms, automated systems are urgently needed to accurately parse vast unstructured data. the development of efficient automated detection algorithms thus emerges as a critical research focus, directly addressing the pressing imperative for risk mitigation. the semeval-2025 task 9 @xcite ) is a food hazard detection task which extract food issues from web sources like social media, and we participate in sub-task 1 and sub-task 2.sub-task 1 focus on text classification for food hazard prediction,predicting the type of hazard and product.subtask 2 focus on food hazard and product "vector" detection, predicting the exact hazard and product. though machine learning approaches have demonstrated promise in food safety monitoring, two challenges persist in real-world deployment scenarios. first, social media texts exhibit inherent †corresponding author. linguistic complexity through abbreviated syntax, domain-specific jargon (e.g., "salmonella spp."), and implicit hazard references that resist traditional keyword matching. second, the long-tail distribution of both hazards and products creates class imbalance -our analysis reveals that the top 3 hazard categories account for nearly 70% of occurrences in training set. our work addresses these challenges by systematically integrating transformer architectures with multi-task learning paradigms. unlike baseline methods that process hazard and product detection sequentially, we perform joint optimization of these closely related tasks using shared semantic representations. this approach enables mutual reinforcement between hazard context understanding and product-specific recognition. the choice of roberta @xcite as the base architecture stems from its proven capability in robust tokenlevel representation learning, which is particularly crucial for detecting implicit hazard mentions in short texts. in our training process, we treat the prediction tasks for hazard and product labels equally. ## experiments in the experiments, we selected roberta as the base model, and we also conducted some preliminary experiments using bert @xcite . we employed the official script to calculate the macro f1 score for evaluating our experimental results, and we will strive to present the results of our direct submissions to the official platform, even if these results may be incomplete. detailed hyperparameter settings are provided in all experiments were conducted on an nvidia geforce gtx 3090 gpu. ## conclusion in this paper, we present our solution for semeval-2025 task 9:the food hazard detection challenge. the task objective focuses on simultaneously predicting hazard types and corresponding food products from social media texts. to address this challenge, we implemented a systematic pipeline beginning with data preprocessing steps that removed semantically irrelevant elements like timestamps and document ids. subsequently, we developed a multi-task learning framework based on the roberta architecture, which enables joint prediction for both hazard and product classification through parameter sharing. our final system achieved competitive performance with macro-f1 scores of 0.7835 on sub-task 1 and 0.4712 on subtask 2 in the official evaluation.