@inproceedings{abela-etal-2026-balancing,
    title = "Balancing Fluency and Adherence: Hybrid Fallback Term Injection in Low-Resource Terminology Translation",
    author = "Abela, Kurt  and
      Tanti, Marc  and
      Borg, Claudia",
    editor = "Ojha, Atul Kr.  and
      Liu, Chao-hong  and
      Vylomova, Ekaterina  and
      Pirinen, Flammie  and
      Washington, Jonathan  and
      Oco, Nathaniel  and
      Zhao, Xiaobing",
    booktitle = "Proceedings for the Ninth Workshop on Technologies for Machine Translation of Low Resource Languages ({L}o{R}es{MT} 2026)",
    month = mar,
    year = "2026",
    address = "Rabat, Morocco",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-eacl/2026.loresmt-1.6/",
    pages = "78--86",
    ISBN = "979-8-89176-366-1",
    abstract = "Integrating domain-specific terminology into Machine Translation systems is a persistent challenge, particularly in low-resource and morphologically-rich scenarios where models lack the robustness to handle imposed constraints. This paper investigates the trade-off between static dictionary-based data augmentation and dynamic inference constraints (Constrained Beam Search). We evaluate these methods on two high-to-low resource language pairs: English-Maltese (Semitic) and English-Slovak (Slavic). Our experiments reveal a dichotomy: while dynamic constraints achieve near-perfect Terminology Insertion Rates (TIR), they drastically degrade translation quality (BLEU) in low-resource settings, breaking the fragile fluency of the model. Conversely, static augmentation improves terminology adherence on unseen terms in Maltese (4{\%} $\rightarrow$ 19{\%}), but fails in the context of a highly inflected language like Slovak. To resolve this conflict, we propose Hybrid Fallback Term Injections, a strategy that prioritizes the fluency of static models while using dynamic constraints as a safety net. This approach recovers up to 90{\%} of missing terms while mitigating the quality degradation of pure constraint approaches, providing a viable solution for high-fidelity translation in data-scarce environments."
}