Balancing Fluency and Adherence: Hybrid Fallback Term Injection in Low-Resource Terminology Translation

AbstractIntegrating 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% → 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.