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        <title>MINDS at SemEval-2025 Task 9: Multi-Task Transformers for Food Hazard Coarse-Fine Classification</title>
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    <abstract>Food safety is a critical concern: hazardous incident reports need to be classified to be able to take appropriate measures in a timely manner. The SemEval-2025 Task 9 on Food Hazard Detection aims to classify food-related incident reports by identifying both the type of hazard and the product involved, at both coarse and fine levels of granularity. In this paper, we present our solution that approaches the problem by leveraging two independent encoder-only transformer models, each fine-tuned separately to classify hazards and food products, at the two levels of granularity of interest. Experimental results show that our approach effectively addresses the classification task, achieving high-quality performance on both subtasks. We additionally include a discussion on potential improvements for future iterations, and a brief description of failed attempts. We make the code available at https://github.com/fgiobergia/SemEval2025-Task9.</abstract>
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%0 Conference Proceedings
%T MINDS at SemEval-2025 Task 9: Multi-Task Transformers for Food Hazard Coarse-Fine Classification
%A Giobergia, Flavio
%Y Rosenthal, Sara
%Y Rosá, Aiala
%Y Ghosh, Debanjan
%Y Zampieri, Marcos
%S Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-273-2
%F giobergia-2025-minds
%X Food safety is a critical concern: hazardous incident reports need to be classified to be able to take appropriate measures in a timely manner. The SemEval-2025 Task 9 on Food Hazard Detection aims to classify food-related incident reports by identifying both the type of hazard and the product involved, at both coarse and fine levels of granularity. In this paper, we present our solution that approaches the problem by leveraging two independent encoder-only transformer models, each fine-tuned separately to classify hazards and food products, at the two levels of granularity of interest. Experimental results show that our approach effectively addresses the classification task, achieving high-quality performance on both subtasks. We additionally include a discussion on potential improvements for future iterations, and a brief description of failed attempts. We make the code available at https://github.com/fgiobergia/SemEval2025-Task9.
%U https://aclanthology.org/2025.semeval-1.287/
%P 2213-2218

Markdown (Informal)

[MINDS at SemEval-2025 Task 9: Multi-Task Transformers for Food Hazard Coarse-Fine Classification](https://aclanthology.org/2025.semeval-1.287/) (Giobergia, SemEval 2025)

• MINDS at SemEval-2025 Task 9: Multi-Task Transformers for Food Hazard Coarse-Fine Classification (Giobergia, SemEval 2025)

ACL

• Flavio Giobergia. 2025. MINDS at SemEval-2025 Task 9: Multi-Task Transformers for Food Hazard Coarse-Fine Classification. In Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025), pages 2213–2218, Vienna, Austria. Association for Computational Linguistics.