Terminology-Constrained Machine Translation

• Terminology-constrained machine translation is a method that guarantees accurate domain-specific term rendering using predefined lexicons and contextual constraints.
• It integrates user-specified glossaries into MT systems via training-time augmentation, constraint-token masking, weighted losses, and inference-time strategies.
• Empirical results show high term usage rates (>95%), improved BLEU scores, and enhanced fluency, proving its effectiveness in specialized and regulated domains.

Terminology-constrained machine translation (TC-MT) is the task of ensuring that machine translation (MT) output adheres to a predefined set of domain-specific term mappings, guaranteeing that specified source-language terms appear as prescribed target-language translations in the output. This requirement is pervasive in domains such as AI, law, medicine, patents, and real-time global events, where terminological consistency is critical for accuracy, interpretability, and regulatory compliance. TC-MT encompasses a suite of strategies—ranging from training-time data augmentation to inference-time constrained decoding and post-editing—that integrate user- or domain-supplied glossaries, terminology resources, or lemma-based constraints into neural and hybrid MT architectures.

1. Problem Definition and Formal Objectives

TC-MT is formally defined as follows. Given a source sentence $x$ and a terminology lexicon $T = \{ (s_i, t_i) \}$, where $s_i$ is a source term and $t_i$ its prescribed target translation, the translation $y$ must not only be fluent and faithful but also satisfy terminology constraints such that if $s_i \subseteq x$, then $t_i \subseteq y$ in the proper context. This requirement may be enforced strictly (hard constraints) or preferentially (soft constraints). The formal constraint set for hard decoding is

$$C_h(x, T) = \{ y \in \mathcal{Y} \mid \forall (s, t) \in T: s \subseteq x \implies t \subseteq y\}$$

where $\mathcal{Y}$ denotes the set of permissible output sequences (Liu et al., 24 Dec 2024).

Challenges arise from ambiguous or inflected forms, multi-word terms, and morphologically rich languages, particularly when only lemma-level resources are available or when deploying MT in low-resource and emerging domains (Bergmanis et al., 2021, Xu et al., 2021). The field further recognizes the inadequacy of enforcing all constraints via hard mechanisms due to the tension between literal term placement and translation fluency (Jaswal, 7 Nov 2025).

2. Approaches to Terminology Integration

TC-MT encompasses methodologies operating at training, decoding, and post-editing stages. These include:

• Training-Time Data Augmentation: Injecting terminological knowledge by manipulating training corpora so that models learn to prefer term-constrained outputs without inference overhead. This includes techniques such as inline annotation (append/replace), special token tagging, and lemma annotation (Ailem et al., 2021, Dinu et al., 2019, Bergmanis et al., 2021).
• Constraint-Token Masking: Replacing source terms by a [MASK] token during augmentation so that the network learns to rely only on the constraint tag and the target form, improving generalization to unseen inflections (Ailem et al., 2021).
• Weighted Losses: Further biasing the model via increased loss weights on constraint tokens (Weighted Cross-Entropy, WCE) to increase the likelihood of outputting the prescribed term (Ailem et al., 2021).
• Inference-Time Constrained Decoding: Modifying the search algorithm to guarantee insertion of terminology through finite-state automata, multi-stack (grid) or cascaded beam search, or attention-guided placement (Hasler et al., 2018, Odermatt et al., 2023). Plug-and-play frameworks enable terminology enforcement without retraining of the base MT model (Odermatt et al., 2023).
• Post-Translation Refinement (LLM Post-Editing): Utilizing LLMs in a translate-then-refine pipeline. Initial MT output is revised with explicit directives to enforce required terminology, yielding highly flexible, context-adaptive corrections (Liu et al., 24 Dec 2024, Jaswal, 7 Nov 2025, Bogoychev et al., 2023).
• Dynamic Lemma-Based Injection: Factored models or rule-based morphological inflection modules inject lemma constraints at test time and generate correctly inflected forms in context, enabling flexible integration even when glossaries contain only base forms (Bergmanis et al., 2021, Bergmanis et al., 2021, Xu et al., 2021).
• Glossary-Augmented Prompting: Conditioning the generation process (pre- or post-editing) on explicit glossaries using natural-language prompts, especially with LLMs capable of in-context learning (Liu et al., 24 Dec 2024, Moslem et al., 2023, Moslem et al., 2023).

3. Resource Construction and Domain Coverage

Terminology-constrained MT depends on high-coverage, high-quality bilingual term resources. Large-scale term banks such as GIST—a 5K-concept, five-language AI terminology dataset—are constructed by extracting terms from technical literature via LLM-driven extraction, expert filtering, hybrid human-in-the-loop translation, and LLM ranking of translation candidates (Liu et al., 24 Dec 2024). The creation process involves:

• Extraction from in-domain corpora using LLMs with contextual prompts.
• Human expert selection and disambiguation, ensuring concept validity.
• Hybrid translation pipelines where candidate translations are proposed by crowdsourced experts and selected/ranked by LLMs (GPT-4o).
• Benchmarking coverage and accuracy through pairwise human evaluation and comparison to existing resources (e.g., ACL 60-60), with demonstrated improvements in term correctness and inter-annotator agreement (Fleiss’ $\kappa$ in 0.4–0.5 range).

Effective resource construction is crucial in domains with evolving terminologies or significant out-of-vocabulary risk (e.g., new scientific fields, pandemics), and facilitates cross-lingual inclusivity and accessibility of technical literature (Liu et al., 24 Dec 2024).

4. Quantitative Evaluation and Empirical Results

TC-MT research employs both standard MT metrics and specialized terminology-adherence metrics:

• BLEU/chrF/COMET: General translation quality.
• Term-Usage Rate / Success Rate (Proper SR): Fraction of required term pairs present in the output; typically targets exceed 95% for strong systems (Ailem et al., 2021, Jaswal, 7 Nov 2025, Kim et al., 21 Oct 2024).
• Window Overlap: Contextual placement of terms measured by local n-gram concordance with reference (Alam et al., 2021).
• 1–TERm: A variant of TER penalizing term errors more heavily (Alam et al., 2021).

Empirical results establish:

• Translate-then-refine prompting with LLMs (post-editing) yields consistent BLEU and COMET gains over raw MT and word-alignment methods, especially in morphologically complex languages (Liu et al., 24 Dec 2024, Jaswal, 7 Nov 2025).
• LLM-based post-editing enables near-perfect term success rates (SR $\geq$ 0.98) while also improving fluency and global MT quality.
• Plug-and-play CBS decoding achieves both 99%+ term insertion and high sentence-level BLEU without retraining (Odermatt et al., 2023).
• Data augmentation (e.g., TAG+MASK or TADA+MASK+WCE) in supervised Transformer models yields exact-match constraint satisfaction rates exceeding 92% for en–fr, en–ru, and en–zh, outperforming constrained decoding in accuracy and efficiency (Ailem et al., 2021, Ailem et al., 2021).
• Dynamic lemma-based integration, as used in COVID-19 domain adaptation, achieves up to 94% term use accuracy on out-of-domain test sets without any in-domain finetuning (Bergmanis et al., 2021).

Tabular summary of representative results:

System / Approach	Term Usage Rate (%)	BLEU (Δ vs. Baseline)	Notes
GIST + LLM Prompting	98–99	+2 to +2.5	Requires no retraining, five languages
Data-Augment + MASK (Ailem et al., 2021)	92–97.8	+0.89 to +7.0	Baked-in at train, no inference overhead
DuTerm (NMT+LLM) (Jaswal, 7 Nov 2025)	98–99	+2 to +3	Context-driven mutation, high fluency
CBS decoding (Odermatt et al., 2023)	≈100	≈0 or small improve	Plug-and-play, linear time in #constraints

5. Architectural and Implementation Considerations

Methods must balance exactitude of term insertion with translation fluency, latency, and scaling:

• Efficiency: Training-time approaches such as TADA+MASK and trie-based term extraction in LLM translation pipelines have zero inference penalty (Ailem et al., 2021, Kim et al., 21 Oct 2024). Post-editing methods using LLM prompting are orders-of-magnitude faster than constrained beam search (Liu et al., 24 Dec 2024).
• Adaptability: Prompt-based post-editing and word-alignment substitution generalize readily to new terms/languages if LLMs have robust multilingual instruction-following (Liu et al., 24 Dec 2024).
• Coverage and Limits: Most approaches assume one-to-one term mappings, with coverage determined by the underlying glossary resource (Liu et al., 24 Dec 2024). Hard-constraint decoding can degenerate fluency, particularly in the presence of inflected or multiword expressions (Jaswal, 7 Nov 2025). Lemma-based/factored models improve inflectional appropriateness in morphologically rich targets (Bergmanis et al., 2021, Xu et al., 2021).
• Evaluation Validity: Metrics that only measure term presence can be gamed (via term appending); window overlap and terminology-specific TER variants are thus critical for realistic evaluation (Alam et al., 2021).

Implementation choices—e.g., hard grid beam decoding, TAG+MASK data augmentation, LLM-based mutators—must consider computational budget, domain adaptation needs, language morphology, and deployment latency constraints.

6. Practical Insights, Limitations, and Extensions

Major takeaways and open considerations:

• Translate-then-refine LLM prompting enables rapid integration of new terminology, minimizes retraining, and attains both high accuracy and fluency (Liu et al., 24 Dec 2024).
• Hard-constraint methods guarantee success-rate but can generate awkward or malformed outputs, especially for morphologically complex languages (Jaswal, 7 Nov 2025).
• Training-time augmentation (TAG+MASK, TADA+MASK+WCE) and trie-based glossary integration scale favorably and can be rapidly retargeted to evolving domains (Ailem et al., 2021, Kim et al., 21 Oct 2024).
• Lemma-only dictionaries can be accommodated by dynamic factor annotation or rule-based inflection modules to produce context-appropriate target forms (Bergmanis et al., 2021, Xu et al., 2021).
• Evaluation pipelines should jointly report BLEU, exact term match, and contextual window overlap to avoid misleading quality assessments (Alam et al., 2021).
• Noted limitations include coverage gaps in glossaries, the assumption of one-to-one mappings, challenges with synonym variants, and possible performance degradation when linguistic agreement (e.g., gender/case) is not handled in the target (Liu et al., 24 Dec 2024, Xu et al., 2021).

Potential research extensions include soft constraint regularization, multi-synonym mapping, morphological generalization in low-resource contexts, and hybrid strategies blending training-time and inference-time terminology injection for ultra-high-precision regulatory and legal translation scenarios (Kim et al., 21 Oct 2024, Xu et al., 2021).

7. Impact and Future Directions

Terminology-constrained MT advances the precision, inclusivity, and reliability of automated translation in specialized domains, supporting multilingual access to scientific, medical, and policy texts. Resources such as GIST signal the maturation of large-scale, human+LLM-verified terminology banks, while plug-and-play and LLM-based mutator paradigms lower the barrier to global deployment across new languages and subject areas.

Future directions include expanding coverage to more languages and domains, integrating morphological/syntactic agreement modeling, and tightly coupling TC-MT with professional translation workflows and continuous evaluation metrics that jointly measure term fidelity and contextual fluency (Liu et al., 24 Dec 2024, Alam et al., 2021). The field continues to push toward models capable of reliable, context-sensitive, and efficient terminology adherence in both high- and low-resource, static and rapidly evolving translation environments.