@article{garcia-estrada-etal-2026-linguistic,
    title = "Linguistic Knowledge-Infused Fine-Tuning for Mitigating Gender Bias in Machine Translation",
    author = "Garcia Estrada, Luis Ernesto  and
      Mash, Audrey  and
      Escolano, Carlos  and
      Melero, Maite  and
      Basta, Christine",
    editor = "Piperidis, Stelios  and
      Bel, N{\'u}ria  and
      van den Heuvel, Henk  and
      Ide, Nancy  and
      Krek, Simon  and
      Toral, Antonio",
    journal = "International Conference on Language Resources and Evaluation",
    volume = "main",
    month = may,
    year = "2026",
    address = "Palma de Mallorca, Spain",
    publisher = "ELRA Language Resource Association",
    url = "https://preview.aclanthology.org/ingest-lrec/2026.lrec-main.685/",
    pages = "8699--8709",
    abstract = "Large Language Models (LLMs) achieve strong performance in machine translation (MT) but often encode gender bias, particularly when translating from non-gendered into gendered languages. This paper introduces a fine-tuning strategy to mitigate such bias in English-Spanish and English-Catalan translation. Using parameter-efficient LoRA fine-tuning, we apply linguistic knowledge infusion{---}a reasoning-based method that trains models to identify gendered referents and syntactic cues before generating translations. Experiments with Mistral{--}7B and Salamandrata{--}7B on MT-GenEval show that linguistically infused models improve gender accuracy by 15 percentage points and reduce gender gaps by 27 points in English-Spanish translation, with comparable trends for Catalan. Gains are strongest for Mistral, suggesting that explicit linguistic reasoning particularly benefits general-purpose LLMs. Overall, these results demonstrate that structured linguistic priors can enhance fairness and referential consistency in multilingual machine translation."
}