Start using DeepL Glossary in Auto-Translation today to trim post-edit time and streamline term alignment across languages. If you already rely on glossaries, you will see faster turnarounds and fewer inconsistencies in under two weeks of use, with enterprise teams reporting up to 40% faster initial drafts and 30% fewer revisions on average.
With built-in tools to manage terms, you will be able to achieve tighter alignment across translators and reviewers. For tricky terminology, create a piece of glossary rules that apply to spanish and another language. The workflow boosts power and tool synergy in your enterprise, making the glossary a central lead in every project.
Our murf-friendly prompts keep context intact, so you can alter translations confidently. A single tool feeds glossary data to your slides and reports, and you can export to powerpoint for stakeholder reviews. With a shared glossary, your translators work from one reference across projects, improving consistency quickly.
To start, import a glossary file for spanish, enable the Auto-Translation glossary, and run a 1-week pilot with 2–3 translators. Track term hit rate and editing time; expect measurable gains in consistency and delivery speed, so you can lead more projects with confidence.
How to enable DeepL Glossary in Lokalise AI and wire it to auto-translation workflows
Enable DeepL Glossary within Lokalise AI at project level and wire it into your auto-translation workflows, ensuring consistent terminology across languages.
Quick setup: In Lokalise AI, connect the DeepL Glossary and set it as the glossary source in the MT stage of your workflows. theres a code-free setup that enables fast adoption. You can start on a free tier and upgrade to a paid plan for higher quotas. The glossary handles brand names, product terms, and industry jargon, delivering distinctive results for your audience. thats a smart choice for teams aiming to improve consistency without overhauling existing processes.
Wire it into workflows: Create a glossary-aware MT step that loads glossary terms before translating and preserves them in machine-translated strings. This leap in consistency reduces post-editing and accelerates time-to-publish for high-volume content. For voiceover scripts, captions, and other assets, the glossary keeps terms distinctive across several languages and platforms, enabling translating with confidence.
Governance and security: Enforce role-based access, maintain an audit trail, and keep glossary data in trusted regions. This security posture addresses data-handling concerns for internal teams and others involved in translations, reducing risk and maintaining a common language across all workflows on the platform.
Measurements and optimization: Term coverage and accuracy, and impact on turnaround time. If a glossary term is flagged as inconsistent, update the entry and re-run the relevant batches. The outcome is an efficient, powerful experience that supports high-volume translating tasks and addresses audience expectations across several locales.
How to import glossary terms and map translations to target languages
Providing a clean, excel-based input for your glossary is essential: use columns for source_term, topic, context, language_code, and translated_term. Use proper spelling and consistent casing. During import, map translations to target languages with a rule-based engine, while allowing hand input for ambiguous cases. This approach offers a fast path for users and can save minutes per thousand terms. Once translated, review in context to ensure proper tone for the topic and keep consistency across languages.
Step-by-step workflow
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Prepare the Excel file: build a workbook with columns for source_term (english), topic, context, language_code (e.g., en, fr, es), and translated_term. Keep a separate column for notes and another sheet for alternatives to handle similar terms.
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Build mapping rules: create a rule-based mapping that links language_code to the target language, plus a small set of preferred variants. Include a similar term list for cases where several translations are acceptable.
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Import and configure: in your software, select Import Glossary and map fields so source_term → english, translated_term → translated, language_code → target_language. Enable basic validation checks and a visual diff to spot obvious mismatches; however, rely on manual adjustments for tricky terms.
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Validate results: perform a visual check on a sample of 20–30 terms, verify tone in context, and confirm that translations align with topic conventions. If issues appear, correct the source in Excel and re-import.
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Publish and train: save the glossary as a custom resource, empower editors to review translations, and train teams to use the same workflow. Be ready to loop back when new terms surface, becoming faster as the memory grows.
Best practices for accuracy and consistency
- Offer a compact baseline of terms as the reference; pair it with a basic style guide to keep proper capitalization and terminology across languages.
- Input should be clean and english source terms clearly separated from translations; use input rules to avoid mixed-language cells.
- Apart from automation, rely on hand review for ambiguous entries; this powerful check reduces errors that the machine cannot predict.
- Provide alternatives for terms with multiple valid translations and mark preferred choices to guide future updates.
- Becoming becoming more efficient, you should train editors on the workflow and use visual comparisons to catch mismatches quickly.
- Use a custom glossary that can be exported as excel and re-imported by teams across software platforms, ensuring translated terms stay aligned with the source.
- During ongoing updates, add new terms without overwriting proven translations; this preserves consistency while expanding coverage.
How to structure domain-specific glossaries with variants and synonyms
Begin with a pragmatic rule: map each term to a core sense and attach a set of variants that reflect context and audience. Create a multilingual glossary core that links domain-specific terms to translations and keeps context at the center. Include a machine-translated baseline and then refine with human check to capture nuances.
Architecture for a robust domain-specific glossary relies on three layers: a core term, a stable set of variants and synonyms, and a mapping to translations across languages. The overview shows how each term aligns with global management sets, enabling contextually accurate content in multilingual outputs. The structure integrates common terms from sources like product catalogs, support tickets, and policy docs, with terms such as SKU, fulfillment center, and return window staying consistent across languages.
Variant and synonym strategy: for each core term, define variants that reflect product lines, regions, or user roles, and attach synonyms that readers might expect in different contexts. Capture these variants in the same domain-specific record, then integrate them into translation memories and neural MT, and gpt-like generation workflows, allowing wide coverage. This approach aligns translations across languages by tagging each variant with language, domain, and context.
Workflow and checks: start with contents input from product feeds, manuals, and help center articles. Build a baseline with machine-translated content, then run a check to confirm that context remains intact. Use a term check to ensure each variant maps to the right domain-specific sense, and flag any mismatches for rework. This process supports a broad multilingual audience, allowing global products and services.
| Term | Variants/Synonyms | Context | Notes | Source |
|---|---|---|---|---|
| order status | order state; shipment status | logistics, customer service | Translate consistently across channels; link to tracking IDs | contents, amazon catalog |
| SKU | Stock Keeping Unit; product code; item number | inventory, catalog | Keep abbreviations uniform; avoid region-specific spellings | internal data, murf example |
| fulfillment center | fulfillment hub; distribution center | fulfillment operations | Reflect regional layouts; map to carrier names | logistics docs, global |
| return window | return period; returns deadline | policy, customer support | Regional variants must be reviewed by policy owners | george scenario, amazon |
| customer_id | customer number; acct_id; client_id | data management, CRM | Avoid exposing PII in glossary content; use placeholders | internal dataset |
How to test glossary-driven translations with real strings and QA checks
Practical workflow for real strings
Begin with a curated set of files that reflect the topic and brand voice. Gather translated strings and their source prompts, then load these into a glossary test harness to compare glossary-driven translations against expected phrases. It lets you compare glossary-driven translations across prompts and languages while you measure speed and identify weaknesses in tokenization or the translator workflow. Run tests across european language pairs to spot polish needs in morphology and to surface gaps in phrasing. Keep a log of which strings align with the glossary, which ones require manual adjustment, and which phrases trigger mismatches. Use accessible checks to ensure glossary terms appear in voiceover scripts, captions, UI labels, and documentation prompts. This approach makes the test experience easy to audit and easier to reuse across projects. Customization controls let you polish the glossary by merging user feedback and refining term mappings. Tie results to brand and compliance guidelines, and make it clear for the company processes to evaluate glossary performance in translated files. If youre team handles updates, these results let you decide where to adjust terms.
QA checks and metrics
Set a baseline by counting glossary term occurrences in translated outputs across files. Use automated checks to flag missing terms, mistranslations, or inconsistent usage of the same term across a topic. Compare across languages to measure polish quality, consistency, and readability, and track speed per file to spot bottlenecks in the process. Validate voiceover scripts, brand mentions, and prompts to ensure alignment with compliance rules and brand tone. Utilizing feedback from translators and regional teams to tweak customization and improve understanding of how terms translate in context. Maintain a change log that the company processes can share with stakeholders. When a string contains punctuation or capitalization, verify that the term remains accessible and translated correctly in all formats. The result is a tighter loop between prompt creation, glossary maintenance, and the translated files. We also test cross-platform delivery by replaying translated files in the voiceover track and caption streams, ensuring coherence across voiceover, UI prompts, and on-screen labels.
How to monitor glossary performance and impact on turnaround time in live projects
Set up a powerful, visual glossary performance dashboard that ties glossary usage to turnaround time in english content. Where glossary hits occur, quantify impact on segment-level TAT and accuracy. Track volumes of terms across large projects to identify bottlenecks, and use the data to drive improving results beyond the glossary itself by showing which entries reduce rework and which cause delays. Look at data from multiple projects to reach reliable trends; limit anomalies by filtering for language, domain, and term family. Start with a deep baseline capturing pre-glossary TAT, post-glossary TAT, and glossary-hit rate, then compare week over week. Maintain a brand terms library to boost accessibility and polish across teams and channels.
Key metrics to monitor
Monitor glossary hit rate, average turnaround time per segment, post-edit distance, and rework rate after applying glossary terms. Use a visual dashboard to compare english content against other languages, and drill down by term family, language pair, and project. Track where glossary usage spikes and which entries correlate with faster delivery. Set a limit for acceptable rework and target a hit-rate threshold that signals meaningful coverage; with large volumes, segment data by project and client to reveal which terms deliver the strongest impact. Features like term density, coverage depth, and policy adherence help you gauge improvement at the term level, not just the project level.
Practical workflow for live projects
Assign a glossary lead and establish a weekly update cadence to capture changes in entries and definitions. Connect the glossary database to the translation memory and CAT tools so hits apply in real time, reducing cycles. Track turnaround time before and after glossary adoption and look for improvements that indicate higher confidence. Maintain a polish queue for high-impact terms and rely on a deep, original set of examples to train translators, including both common and brand terms. Build a brand library that covers english terms and product names (including amazon) to support accessibility and consistency. Present progress in powerpoint to stakeholders, highlighting which terms added value and identifying next terms to codify. With a scalable approach, capabilities grow across teams and languages, helping you reach the next level of quality and speed.
