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        <title>Balancing Fluency and Adherence: Hybrid Fallback Term Injection in Low-Resource Terminology Translation</title>
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    <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.</abstract>
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%0 Conference Proceedings
%T Balancing Fluency and Adherence: Hybrid Fallback Term Injection in Low-Resource Terminology Translation
%A Abela, Kurt
%A Tanti, Marc
%A Borg, Claudia
%Y Ojha, Atul Kr.
%Y Liu, Chao-hong
%Y Vylomova, Ekaterina
%Y Pirinen, Flammie
%Y Washington, Jonathan
%Y Oco, Nathaniel
%Y Zhao, Xiaobing
%S Proceedings for the Ninth Workshop on Technologies for Machine Translation of Low Resource Languages (LoResMT 2026)
%D 2026
%8 March
%I Association for Computational Linguistics
%C Rabat, Morocco
%@ 979-8-89176-366-1
%F abela-etal-2026-balancing
%X 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.
%U https://aclanthology.org/2026.loresmt-1.6/
%P 78-86

Markdown (Informal)

[Balancing Fluency and Adherence: Hybrid Fallback Term Injection in Low-Resource Terminology Translation](https://aclanthology.org/2026.loresmt-1.6/) (Abela et al., LoResMT 2026)

• Balancing Fluency and Adherence: Hybrid Fallback Term Injection in Low-Resource Terminology Translation (Abela et al., LoResMT 2026)

ACL

• Kurt Abela, Marc Tanti, and Claudia Borg. 2026. Balancing Fluency and Adherence: Hybrid Fallback Term Injection in Low-Resource Terminology Translation. In Proceedings for the Ninth Workshop on Technologies for Machine Translation of Low Resource Languages (LoResMT 2026), pages 78–86, Rabat, Morocco. Association for Computational Linguistics.