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        <title>Data Augmentation Based on Selective Masking of Language Models for One Health Context</title>
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            <namePart type="given">Vera</namePart>
            <namePart type="family">Danilova</namePart>
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    <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.</abstract>
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%0 Conference Proceedings
%T Data Augmentation Based on Selective Masking of Language Models for One Health Context
%A Mahdoubi, Youssef
%A Idrissi, Najlae
%A Roche, Mathieu
%A Valentin, Sarah
%Y Danilova, Vera
%Y Kurfalı, Murathan
%Y Söderfeldt, Ylva
%Y Reed, Julia
%Y Burchell, Andrew
%S Proceedings of the 1st Workshop on Linguistic Analysis for Health (HeaLing 2026)
%D 2026
%8 March
%I Association for Computational Linguistics
%C Rabat, Morocco
%@ 979-8-89176-367-8
%F mahdoubi-etal-2026-data
%X 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.
%U https://aclanthology.org/2026.healing-1.23/
%P 266-276

Markdown (Informal)

[Data Augmentation Based on Selective Masking of Language Models for One Health Context](https://aclanthology.org/2026.healing-1.23/) (Mahdoubi et al., HeaLing 2026)

• Data Augmentation Based on Selective Masking of Language Models for One Health Context (Mahdoubi et al., HeaLing 2026)

ACL

• Youssef Mahdoubi, Najlae Idrissi, Mathieu Roche, and Sarah Valentin. 2026. Data Augmentation Based on Selective Masking of Language Models for One Health Context. In Proceedings of the 1st Workshop on Linguistic Analysis for Health (HeaLing 2026), pages 266–276, Rabat, Morocco. Association for Computational Linguistics.