@inproceedings{alexandru-marian-ionescu-2026-alexcristea,
    title = "{\_}alexcristea@{EEUCA} 2026: A Robust Early-Fusion {ERNIE} Pipeline for Multimodal {COVID}-19 Vaccine Meme Classification",
    author = "Alexandru-Marian, Cristea  and
      Ionescu, Costin",
    editor = {H{\"u}rriyeto{\u{g}}lu, Ali  and
      Thapa, Surendrabikram  and
      Tanev, Hristo},
    booktitle = "Proceedings of the 9th Workshop on Event Extraction and Understanding: Challenges and Applications ({EEUCA} 2026)",
    month = jul,
    year = "2026",
    address = "San Diego, California, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl-workshops/2026.eeuca-1.20/",
    pages = "185--191",
    ISBN = "979-8-89176-402-6",
    abstract = "This paper presents our system for the EEUCA0022026 shared task on Multimodal Vaccine Critical Meme Detection. The task focuses on categorizing social media memes from the VaxMeme dataset into three stances: Vaccine Critical, Neutral, and Pro-Vaccine. To tackle the inherent challenges of internet sarcasm, implicit context, and high label noise, we propose a robust, heavily regularized text-fusion pipeline. Rather than relying on computationally heavy visual encoders, we extract text directly from the images via OCR and concatenate it with the user{'}s social media post, processing the unified context through an ERNIE 2.0-Large encoder. To combat the severe overfitting typical in subjective meme datasets, we replace the standard classification head with a Multi-Sample Dropout architecture, averaging predictions across five parallel dropout masks (p = 0.3). Our optimized, lightweight text-only pipeline achieves a peak Macro F1 score of 0.834. Furthermore, an ablation study utilizing Focal Loss reveals that our primary solution using standard Cross-Entropy provides superior robustness against the inherent label noise found in internet meme datasets."
}