@inproceedings{jamil-etal-2026-iitpatna,
    title = "{IITP}atna{\_}{ADE} at {\#}{SMM}4{H}-{H}ea{RD} 2026: Multilingual Adverse Drug Event Detection with {L}o{RA}-{XLM}-{R}o{BERT}a, Cross-Fold Ensembles, and Post-hoc Calibration",
    author = "Jamil, Sofia  and
      Singh, Manish  and
      Dharpure, Harshal  and
      Saha, Sriparna  and
      Misra, Rajiv",
    editor = "Lopez-Garcia, Guillermo  and
      Gonzalez-Hernandez, Graciela",
    booktitle = "Proceedings of the 11th Social Media Mining for Health Research and Applications ({SMM}4{H}-{H}ea{RD} 2026) Workshop and Shared Tasks",
    month = jul,
    year = "2026",
    address = "San Diego, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl-workshops/2026.smm4h-1.29/",
    pages = "177--181",
    ISBN = "979-8-89176-432-3",
    abstract = "We describe our submission to Task 1 of {\#}SMM4H-HeaRD{~}2026: multilingual binary classification of adverse drug event (ADE) mentions in social media. Our system fine-tunes $xlm-roberta-large$ with LoRA adapters and learned language embeddings, using two-stage training (CADEC translated domain adaptation, then five-fold cross-validation on the official training set). We ensemble the five fold checkpoints by mean logits, apply temperature scaling on the development set, and tune decision thresholds to maximize the official metric. On development, the final ensemble reaches macro-F$_1$ 0.788 with a global threshold and 0.796 with per-language thresholds; our best official test submission achieves macro-F$_1$ 0.616 (ID 678990)."
}