It Takes Two: A Dual Stage Approach for Terminology-Aware Translation
Addressing the challenge of balancing term accuracy and contextual adaptability in multilingual machine translation, this paper proposes DuTerm—a dual-stage architecture. The first stage employs a terminology-aware neural machine translation (NMT) model to generate an initial translation; the second stage leverages a large language model (LLM) as a context-driven post-editor—not a generator—to refine terminology consistency via prompt engineering. Crucially, the LLM is designed as a lightweight, controllable term calibration module, mitigating fluency degradation caused by excessive constraints. Experiments on the WMT 2025 Terminology Sharing Task (English–German/Spanish/Russian) demonstrate that DuTerm significantly outperforms strong baselines in both BLEU and term adherence metrics, with particularly notable improvements in term consistency under complex contextual conditions. These results validate the effectiveness and generalizability of the “NMT + LLM correction” paradigm.