It Takes Two: A Dual Stage Approach for Terminology-Aware Translation (2025)

Abstract: This paper introduces DuTerm, a novel two-stage architecture for terminology-constrained machine translation. Our system combines a terminology-aware NMT model, adapted via fine-tuning on large-scale synthetic data, with a prompt-based LLM for post-editing. The LLM stage refines NMT output and enforces terminology adherence. We evaluate DuTerm on English-to German, English-to-Spanish, and English-to-Russian for the WMT 2025 Terminology Shared Task. We demonstrate that flexible, context-driven terminology handling by the LLM consistently yields higher quality translations than strict constraint enforcement. Our results highlight a critical trade-off, revealing that an LLM’s intrinsic knowledge often provides a stronger basis for high-quality translation than rigid, externally imposed constraints.

## introduction the accurate and consistent translation of domainspecific terminology is a challenge in the field of machine translation and is of importance in domains such as law, medicine, and engineering, where precision is critical @xcite . while modern neural machine translation systems based on architectures like the transformer have achieved remarkable fluency and quality on general text, their performance in terminologyconstrained texts remains a critical area for improvement @xcite @xcite . this issue is particularly relevant given findings of recent wmt shared tasks, which have consistently highlighted the need for systems that can effectively handle domainspecific vocabulary @xcite . the wmt 2025 terminology shared task @xcite provides a focused platform to evaluate mt systems ability to handle domain-specific terminology under controlled conditions across multiple language pairs: english to german, english to spanish, and english to russian. previous research into terminology-constrained mt can be broadly categorized into two main approaches: inference-time methods and trainingtime methods. inference-time approaches incorporate terminology constraints directly into the decoding process, often through techniques like constrained beam search or by re-ranking n-best lists of candidate translations @xcite . while these methods are highly effective at enforcing constraints, they can be computationally expensive and may compromise the overall fluency and grammatical correctness of the output by forcing the model to generate awkward or unnatural phrases. recent work has explored ways to make these methods more efficient, but the trade-off between enforcing terminology constraints and fluency remains a key consideration @xcite . alternatively, training-time methods aim to teach models how to handle terminology constraints by integrating the terminology information into the training data itself. this is commonly done through the use of special tags that surround the terms to be translated @xcite . this approach allows the model to learn how to produce more natural and grammatically correct output, but it provides no guarantee that all constraints will be respected during inference @xcite . we present duterm, a two-stage architecture that addresses these limitations by combining the strengths of both training-time and inference-time methodologies. we recognize that terminologyconstrained translation is not merely a lexical substitution problem but requires a deeper understanding of linguistic context, especially when dealing with the complex morphology of languages like german and russian. our system is specifically designed to tackle the multifaceted evaluation framework of the wmt 2025 shared task. ## results we evaluate the system using three complementary metrics used by the wmt organizers: bleu for overall translation adequacy, chrf2++ for characterlevel fluency and robustness, and terminology success rates (proper and random) to directly measure constraint satisfaction @xcite . results are reported for @xmath0, @xmath1, and @xmath2: noterm, proper, and random. overall, these results demonstrate that while strict enforcement maximizes terminology accuracy and boosts surface-level quality metrics, it can occasionally reduce flexibility. in contrast, unconstrained approaches produce more natural translations but risk terminology inconsistency. ## conclusion this paper presents our approach to the terminology shared task, focusing on english to german, spanish, and russian translation directions. our system leverages llms to improve existing translations with varying terminology handling strategies. our results demonstrate that allowing the llm to flexibly handle terminology often yields higher translation quality than strict terminology enforcement. these findings highlight the potential of prompt-based llm systems for technical and business translation tasks, and provide insights into effective strategies for terminology management in neural translation workflows. the intution behind why this approach works so well is that nmts often excel at strict word-level translations however they can struggle with context-dependent nuances. our approach leverages post-editing with a llm on top of the initial nmt outputs. by starting from a reliable nmt translation, the post-editing model receives a structured, partially correct target sentence, which allows it to focus on higher-level improvements resolving ambiguities, adjusting word order, and refining context. this guided refinement is often more effective than translating directly from scratch with an llm or nmt which must simultaneously handle term accuracy and contextual fluency. for future work, exploring adaptive learning mechanisms that integrate terminology dynamically, rather than relying on static prompts, could enhance robustness across domains and languages. end-to-end or memory-augmented architectures that maintain consistency across sentences and documents hold promise for more coherent outputs. expanding evaluations to other language models and diverse, domain-specific corpora would help validate the approach's generalizability and reveal domain-dependent challenges. incorporating hybrid strategies, such as combining prompt guidance with fine-tuning or reinforcement learning, and enabling user-driven interaction for terminology control could further improve usability and accuracy. together, these directions offer a pathway toward more flexible, context-aware, and widely applicable terminology-aware translation systems.