@inproceedings{mahdoubi-etal-2026-data,
    title = "Data Augmentation Based on Selective Masking of Language Models for One Health Context",
    author = "Mahdoubi, Youssef  and
      Idrissi, Najlae  and
      Roche, Mathieu  and
      Valentin, Sarah",
    editor = {Danilova, Vera  and
      Kurfal{\i}, Murathan  and
      S{\"o}derfeldt, Ylva  and
      Reed, Julia  and
      Burchell, Andrew},
    booktitle = "Proceedings of the 1st Workshop on Linguistic Analysis for Health ({H}ea{L}ing 2026)",
    month = mar,
    year = "2026",
    address = "Rabat, Morocco",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-eacl/2026.healing-1.23/",
    pages = "266--276",
    ISBN = "979-8-89176-367-8",
    abstract = "This study focuses on improving the performance of language models for two critical applications within the One Health context, specifically in epidemiological monitoring using textual data: (i) thematic classification across syndromic surveillance, biomedical and plant health domains, and (ii) detection of epidemic misinformation. A key challenge in these tasks is the limited availability of labeled textual data, which constrains the effectiveness of supervised learning methods. To overcome this limitation, we introduce two families of selective masking{--}based data augmentation strategies: lexical and non-lexical. Each family is implemented in a standard variant (Aug-SM-Lex and Aug-SM-NonLex), and a TF-IDF-weighted variant (Aug-SM-Lex-TFIDF and Aug-SM-NonLex-TFIDF). We perform two complementary experiments: the first determines the optimal masking rate, while the second evaluates the proposed strategies against LLM-based text reformulation. Experimental results indicate that selective masking-based augmentation outperformed both LLM-based reformulation (Mistral-7B and GPT-Neo-1.3B) and baseline models trained on original data alone across three of the five evaluated datasets, with the best performance achieved at a masking rate of 20{\%}. This suggests that selective masking is a promising approach, potentially more effective than computationally expensive LLM-based reformulation."
}