Have you ever been in a situation where, as a technical writer, you needed to ensure that the translated user manuals consistently use the approved terminology in five different languages? Or, as a localization manager at a global healthcare organization, did you have to ensure all medical documents were accurately translated into multiple languages and matched the localized UI of your software?

Imagine the confusion if “scalpel” is translated differently in French in different document chapters or the severe consequences of a mistranslated medical term between the software’s UI and a nurse’s checklist. These scenarios illustrate the importance of multilingual terminology and the need to manage terms carefully across languages.

But let’s start with what we mean by “terminology” or “terms.” They refer to the specific words and phrases used within a particular field or industry and are frequently called a Glossary, Term list, Vocabulary, Project Dictionary, Brand vocabulary, etc. Multilingual terminology management is the process of supervising these terms across different languages.

AI (Artificial Intelligence) is very helpful in identifying terms in large amounts of text, tagging key terms, and ensuring they are correctly used across translations.

Do you want to know more about how AI can help you manage terminology, speed up your translation process, and maintain quality? Let’s jump right in!

Building Multilingual Terminology Lists from Unstructured Files

This section discusses how AI can take a step further by compiling terms into multilingual terminology lists, especially for unstructured files in various document formats and languages.

For example, let’s say you’re a software developer working on localizing a complex application with many UI elements. You have design documents, user feedback, and code comments in various formats and languages. If you try manually extracting and compiling terminology from these sources, you can imagine the amount of time that would take. Also, because it is done manually, there’s a high possibility of errors being made.

AI to the Rescue!


Illustration of AI using NLP and language-agnostic processing

But the good part is that AI can rescue you using advanced techniques like natural language processing (NLP). It analyzes the text extracted from files in different formats like PDFs, Word documents, HTML files, and even scanned images, identifying key terms like “submit,” “cancel,” and “preferences,” and compiles them into a terminology list. So, what’s the point of this list? This list (list of terms in one language) becomes your springboard for creating the complete set of multilingual terminology, which can be utilized during translations by AI systems and translators.

Interestingly, AI can be language-agnostic, meaning that it can handle text in multiple languages. So, suppose you’re a technical writer updating user manuals for a global software release. In that case, AI can analyze documents in English, French, German, Spanish, and Japanese, extracting key terms like “settings,” “preferences,” and “dashboard” in each language.

Of course, as a result, you are getting several one-language files with different numbers of terms because AI’s output will be different between the languages.

So, another area for AI to shine is to automatically match the terms from all the files and to build a proper multilingual terminology set.

From this simple example, you can see that using AI to build multilingual terminology lists can optimize your translation process, save time, and maintain the quality and consistency of your multilingual documentation and software UI. AI’s time-saving aspect in managing multilingual terminology is a significant benefit for professionals, allowing them to focus on other critical aspects of their work.

Reviewing and Approving Terms with Translation Platforms

Moving forward, we discussed how AI can compile multilingual terminology lists from various document formats and languages. Once these terms are identified and compiled, the next important step you should take note of is ensuring the terms are accurate and appropriate. This is where translation platforms like TextUnited come into play; they also facilitate the expert review and approval of terms, not only providing AI for extracting and aligning terms from documents across different languages.


Illustration of AI using NLP and language-agnostic processing

Translation platforms provide a centralized and collaborative environment where linguists, translators, and subject matter experts can work together to make sure all the terminology across all translations is accurate in context and consistent.

For instance, if you’re a project manager overseeing a large translation project, you can create a centralized terminology database where all key terms are stored on the translation platform. All team members can access this database, making sure that everyone uses the same approved terms.

Also, in a situation where there are multiple expert reviewers, you can engage dedicated linguists to review and approve terms.

What Are the Major Benefits of Translation Platforms?

One of the significant advantages of translation platforms like TextUnited is the ability to provide real-time feedback and updates. For example, suppose you’re a translator working on translating medical documents. In that case, you can receive real-time notifications about the approved terms to use from the system and can ask for feedback from medical experts via the platform. This way, you can be confident that the medical terms are accurately translated and approved promptly.

Another major advantage of using a translation platform is that you’ll have access to automated quality assurance checks that automatically verify if your terms are accurate and consistent. If you care about speeding up the review and approval process, you can also access customizable approval workflows. You can learn more about this by reading this article on How to Optimize Translation Workflow.

Automating Term Provision to MT Engines and LLMs

In the previous section, we discussed how translation platforms facilitate the expert review and approval of terms. Once your terminology is approved, the next step is to automate its provision to machine translation (MT) engines and large language models (LLMs). You may wonder about the reason behind this automation. This automation optimizes automatically generated first-draft translation, lowering human effort to make corrections.

You should note that translation platforms like TextUnited can integrate with various MT engines and LLMs, such as Google Translate, DeepL, and OpenAI’s GPT. This integration aims to allow the machine translation system to receive the approved terminology to use from your centralized terminology database and apply the approved terms automatically.

For instance, if you’re a localization manager working on a large-scale translation project and using machine translation engines and LLMs through your translation platform, you can automate the provision of medical terms like “diagnosis,” “treatment,” and “medication” to make sure they are used accurately across all translated materials.

What Happens During the Translation Process?

During the automated translation process, the MT engine or LLM is provided with the approved terms from the centralized terminology database to inject the correct terms into the translated text.

Suppose you’re a technical writer updating user manuals in multiple languages. In that case, term injection allows the MT engine to automatically use the approved terms like “dashboard,” “settings,” and “user profile,” maintaining consistency across all documents.

That’s not all; the automation process allows for real-time term provision. As soon as a term is updated or approved in the database, it is immediately available to the MT engines and LLMs. So, if you’re a software developer working on localizing a new application, any updates to terms like “submit,” “cancel,” and “preferences” are instantly applied to make sure the UI text is always accurate and up-to-date.

Also, translation platforms like TextUnited allow you to set customizable term provision rules. As a linguist and language expert, you can specify which terms should be prioritized or how specific terms should be translated based on context to make sure they are translated appropriately and resonate with the target audience.

Quality Assurance in Terminology Management

Now that we’ve seen how automating term provision to MT engines and LLMs ensures that the correct terminology is consistently used across automated translations, let’s look at another important area. It’s equally important to ensure these terms are used correctly during the post-editing process. This is where the QA tools provided by translation platforms like TextUnited come into play, helping you maintain the highest standards of accuracy and consistency.

Translation platforms provide various QA tools designed to achieve correct term usage during post-editing. Key tools include the terminology check, which automatically verifies terms against the approved database, and the consistency check, which guarantees that terms are used uniformly across the document. For instance, if you’re a localization manager working on medical documents, terminology checks can verify that terms like “diagnosis,” “treatment,” and “medication” are used correctly. As a technical writer, consistency checks help maintain uniformity for terms like “dashboard,” “settings,” and “user profile” throughout user manuals.

QA tools also provide spell check, grammar check, and contextual review functionalities to identify errors and ensure the translations are culturally appropriate. With real-time feedback, you’ll be able to carry out immediate corrections, which will speed up the review process. At the same time, automated reports will help highlight any issues with term usage, consistency, spelling, and grammar.

Challenges of Traditional Terminology Management

We’ve discussed the quality assurance tools provided by translation platforms to ensure correct term usage during the post-editing process. Now, let’s compare the challenges of traditional terminology management methods, such as Excel glossaries, with AI-driven solutions.

Traditional terminology management methods, like using Excel glossaries, come with several challenges that hinder efficiency and accuracy. Manual updates and maintenance are time-consuming and prone to human error, leading to inconsistencies. Also, Excel glossaries lack real-time collaboration, making coordinating with team members and maintaining version control difficult. AI-driven platforms automate updates, enable real-time collaboration, and provide cloud-based access, ensuring all team members can work with the latest version from any location.

Consistent term usage and quick access to terminology are significant challenges with manual methods. Translators might overlook or misinterpret terms, leading to inconsistencies, and searching for specific terms in lengthy Excel files can take a lot of time. On the other hand, AI-driven platforms enforce consistent term usage through integration with built-in CAT tools and provide advanced search and filtering capabilities, saving time.

Wrapping Up

Using AI to manage multilingual terminology, from all that you’ve seen in this article, opens the door to many benefits, such as better consistency, accuracy, and speed in all your translations. You’ve learned that AI tools can help you automate updates, keep real-time collaboration, and achieve correct term usage, which will invariably reduce errors and increase productivity. By using AI translation tools like TextUnited, you can simplify your translation process and produce high-quality multilingual content.

If you want to see these benefits, try the TextUnited platform. Click here for a demo or more information.

FAQ: Frequently Asked Questions

Get answers to some of your questions about this topic here:


Terminology management is the systematic process of creating, maintaining, and using a controlled vocabulary of terms specific to a domain for consistent and accurate communication within and between organizations.


Translation management combines human expertise with technological tools to handle content translation across various languages, involving steps like content identification, project planning, terminology management, translation, and delivery.


Multilingual translation is the process of translating content into multiple languages.


The best AI tool for translation varies, but popular options include DeepL, Google Translate, and TextUnited.


Language localization adapts content to meet the cultural and linguistic preferences of a specific target market.


Language technology involves computational tools and techniques used to process and understand human language.


Translation software uses advanced algorithms and linguistic data to automate text translation between languages. Key features include translation memory for leveraging past translations, terminology management for uniform term usage, and post-editing tools for human review to improve quality and readability.


Machine translation software automatically translates text from one language to another using AI and machine learning.

Further Reading

To learn more about managing multilingual terminology and leveraging AI in translation, check out these articles from the TextUnited blog: