Generative AI models face a fundamental limitation when translating specialized terminology in low-resource language pairs: highly specialized vocabulary appears too infrequently in training corpora. This paper addresses this problem by proposing a hybrid framework that combines computational lexicography with neural machine translation to resolve systematic terminological inconsistencies. The approach implements a four-stage automated extraction pipeline: dictionary-based matching, phonetic similarity analysis, statistical significance detection, and proper noun resolution. Following expert validation, these four stages construct a metadata-rich terminological database. The database supports a two-stage translation system: first, standard neural fine-tuning handles style and syntax, followed by automated post-processing based on metadata (denomination, tradition, period) to achieve terminological control. In tests on biblical translation from ancient languages to Chinese (Hebrew, Greek, Latin), the system achieved 1.13× to 1.68× improvements in terminology coverage over dictionary baselines. Rather than competing with commercial large language models, this framework proposes a complementary approach: metadata-driven terminological control addresses systematic gaps that current models cannot resolve. Such methodology could be integrated directly into future model training or inference pipelines. The modular architecture could extend to other specialized domains with similar characteristics: limited parallel corpora and high terminological variability, including legal, medical, and technical fields.
Key words
neural machine translation /
computational lexicography /
low-resource languages /
terminology extraction /
specialized translation
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