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    <abstract>This paper presents our systems for two tasks at #SMM4H-HeaRD 2026. For Task 1 (multilingual Adverse Drug Event detection), we fine-tune BERT-based multilingual models (InfoXLM and XLM-RoBERTa) and Qwen3.5-9B with ensemble methods, achieving 0.8584 macro F1 on the development set and 0.5304 F1 on unseen Farsi. For Task 7 (span detection of ClinicalImpacts and SocialImpacts in opioid narratives), DeBERTa-Large with simplified labeling achieves the best test performance (0.583 relaxed F1, 0.500 strict F1). Our analysis shows that LLMs excel on known languages in Task 1, while transformer-based models with simplified labeling generalize better for NER tasks.</abstract>
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%0 Conference Proceedings
%T DNT at #SMM4H–HeaRD 2026: Leveraging BERT-based Encoders and LLMs for Medical Information Extraction
%A Tien, Doan Nhat
%A Văn, Thìn Đặng
%Y Lopez-Garcia, Guillermo
%Y Gonzalez-Hernandez, Graciela
%S Proceedings of the 11th Social Media Mining for Health Research and Applications (SMM4H-HeaRD 2026) Workshop and Shared Tasks
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, United States
%@ 979-8-89176-432-3
%F tien-van-2026-dnt
%X This paper presents our systems for two tasks at #SMM4H-HeaRD 2026. For Task 1 (multilingual Adverse Drug Event detection), we fine-tune BERT-based multilingual models (InfoXLM and XLM-RoBERTa) and Qwen3.5-9B with ensemble methods, achieving 0.8584 macro F1 on the development set and 0.5304 F1 on unseen Farsi. For Task 7 (span detection of ClinicalImpacts and SocialImpacts in opioid narratives), DeBERTa-Large with simplified labeling achieves the best test performance (0.583 relaxed F1, 0.500 strict F1). Our analysis shows that LLMs excel on known languages in Task 1, while transformer-based models with simplified labeling generalize better for NER tasks.
%U https://aclanthology.org/2026.smm4h-1.7/
%P 36-40

Markdown (Informal)

[DNT at #SMM4H–HeaRD 2026: Leveraging BERT-based Encoders and LLMs for Medical Information Extraction](https://aclanthology.org/2026.smm4h-1.7/) (Tien & Văn, SMM4H 2026)

• DNT at #SMM4H–HeaRD 2026: Leveraging BERT-based Encoders and LLMs for Medical Information Extraction (Tien & Văn, SMM4H 2026)

ACL

• Doan Nhat Tien and Thìn Đặng Văn. 2026. DNT at #SMM4H–HeaRD 2026: Leveraging BERT-based Encoders and LLMs for Medical Information Extraction. In Proceedings of the 11th Social Media Mining for Health Research and Applications (SMM4H-HeaRD 2026) Workshop and Shared Tasks, pages 36–40, San Diego, United States. Association for Computational Linguistics.