@inproceedings{torterolo-orta-etal-2026-translategemma,
    title = "{T}ranslate{G}emma for {ES}-{EN} Financial Reports: Exploring Adaptability to Variable-Sized Contexts",
    author = "Torterolo Orta, Yanco Amor  and
      Chatzi, Melina  and
      Moreno-Sandoval, Antonio",
    editor = "El-Haj, Mo  and
      Moreno Sandoval, Antonio  and
      Garcia-Serrano, Ana  and
      Chen, Chung-Chi  and
      Rayson, Paul  and
      Torterolo Orta, Yanco Amor  and
      Martinez, Paloma  and
      Porta, Jordi",
    booktitle = "The 7th Financial Narrative Processing Workshop",
    month = may,
    year = "2026",
    address = "Palma de Mallorca, Spain",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://preview.aclanthology.org/revision-workflow/2026.fnp-1.8/",
    doi = "10.63317/4mfhxunsaksk",
    pages = "87--97",
    abstract = "This paper explores bidirectional financial Machine Translation (MT) between Spanish and English, focusing on the specialized domain of annual reports from IBEX 35 companies. Fine-tuned models are compared against zero-shot scenarios through a series of experiments, testing factors such as prompting strategies and model size. On the one hand, this work studies a combination of existing fine-tuning strategies aimed at improving the adaptability of MT models to variable-sized contexts, and, on the other hand, it analyzes the limitations detected in current evaluation metrics. Results are mixed: fine-tuned models show an improvement in both short and long-context scenarios in traditional metrics, while zero-shot predictions are clearly favored by neural metrics. In fact, reference-free assessment of the source and the human reference received worse scores than the off-the-shelf prediction models. Consequently, fine-tuning on the human-made dataset hardly improves the neural metrics against zero-shot generations. This suggests that neural metrics tend to favor the fluency of MT generations and literalness over creativity, among other technical limitations regarding long-context adaptability. From a practical standpoint, the low Translation Edit Rate (TER) scores suggest that specialized fine-tuning remains the most viable path for companies to implement efficient Machine Translation Post-Editing (MTPE) workflows, given the stylistic alignment."
}