Localization Terminology Glossary: The Complete A–Z

If you’ve ever Googled “what is i18n?” or paused mid-project to figure out how to create a TMX file, you’re not alone.

The localization world is full of acronyms, technical terms, and phrases that can feel confusing—even if you’ve been in the industry for a while. So we’ve put together this localization terminology glossary to help. It covers everything from the basics (like translation vs. localization) to more technical concepts (like pseudo-localization, fuzzy matches, or XLIFF).

This practical, detailed and up-to-date guide is here to make localization terminology easier to understand—and more useful in your day-to-day work.

Quick Navigation: 0-9 · A · B · C · D · F · G · H · I · K · L · M · N · O · P · Q · R · S · T · U · V · X

0-9

101% match

A translation that is exactly the same as a previous one, including its context.

Use case: Used to repeat the exact same sentence in the same situation.

Example: A support article that’s been updated but remains identical in wording and context.

Why it matters: 101% matches are more reliable than 100% matches because context is verified. Some TMS platforms charge less for 101% matches since they require minimal review.

A

Adaptation

Changing content to fit local culture or habits.

Use case: Used to make content feel natural to people in different countries.

Example: Changing a winter holiday ad for Australia to show summer imagery instead of snow.

Why it matters: Adaptation goes beyond translation—it’s about cultural resonance. A literal translation might be accurate but feel foreign. Adaptation makes content feel native.

Agile localization

Adding translations during each stage of product development.

Use case: Used to keep translations in sync with regular product updates.

Example: Translating new app features every sprint instead of waiting for a full release.

Why it matters: Traditional “waterfall” localization (translate everything at the end) delays launches. Agile localization enables simultaneous global releases (simship) and faster time-to-market.

AI translation

Translation done by artificial intelligence trained on large amounts of language data.

Use case: Used for fast, high-volume translations when speed is more important than perfect style.

Example: Using AI to translate thousands of product listings, then reviewing key ones manually.

Why it matters: AI translation (neural MT) quality has improved dramatically. For many content types, AI-powered translation with human review delivers excellent quality at fraction of traditional cost.

API (Application Programming Interface)

A tool that lets software programs talk to each other.

Use case: Used to send text for translation automatically from your app or website.

Example: Connecting your CMS to a TMS using an API so content is sent for translation instantly.

Why it matters: APIs enable automation, reducing manual file uploads and downloads. Essential for continuous localization and high-volume translation workflows.

B

Back translation

Translating text back into the original language to check its meaning.

Use case: Used to double-check that a translation is correct.

Example: Translating a clinical trial consent form from English to French and back again to catch errors.

Why it matters: Back translation reveals mistranslations that aren’t obvious to monolingual reviewers. Common in medical, legal, and high-stakes content. However, it’s not perfect—some nuances are lost in the back-translation process.

C

CAT tool (Computer-Assisted Translation tool)

Software that helps translators work faster and stay consistent.

Use case: Used to suggest previous translations and save time.

Example: Using Taia’s CAT tool to auto-suggest how to translate a recurring sentence.

Why it matters: CAT tools don’t translate automatically (that’s MT). Instead, they assist human translators with translation memory, glossaries, and quality checks. Industry-standard tools include SDL Trados, memoQ, Phrase, and Taia’s platform.

CMS (Content Management System)

Software used to create and update website content without coding.

Use case: Used to manage content in multiple languages more easily.

Example: Translating and publishing blog posts from WordPress using a plugin.

Why it matters: Multilingual CMS capabilities vary widely. Some (like WordPress with WPML, Contentful, Contentstack) have strong localization features. Others require custom development or third-party integrations.

Collaborative translation

Several people working together to translate content.

Use case: Used when large projects need to be done quickly.

Example: A marketing team and translators using Smartcat to co-edit translations in real time.

Why it matters: Collaborative translation speeds up delivery but requires careful coordination. Translation memory and glossaries ensure consistency across multiple translators.

Concordance search

Searching for how a word or phrase was translated before.

Use case: Used to keep word choices consistent across different texts.

Example: Looking up how “account” was previously translated in your TM—was it “cuenta” or “perfil”?

Why it matters: Terminology consistency matters for brand voice and user experience. Concordance search helps translators find established translations and avoid creating new variants.

Continuous localization

Automatically translating content as it’s created or updated.

Use case: Used when new features or updates are released often.

Example: An e-commerce site localizing new product pages as soon as they go live.

Why it matters: Continuous localization is essential for agile development. Traditional “batch” translation creates bottlenecks. Automated workflows (triggered by CMS updates, Git commits, etc.) enable real-time translation.

Culturalization

Changing content to match the local culture more deeply.

Use case: Used to make sure content is not confusing or offensive.

Example: Rewriting a slogan so it makes sense in Japan, not just translating it directly.

Why it matters: Culturalization is deeper than localization. It involves understanding cultural values, communication styles, symbols, and sensitivities. Essential for marketing, branding, and culturally sensitive content.

D

DNT (Do Not Translate)

A note saying that a word should stay in the original language.

Use case: Used for things like product names or trademarks.

Example: Keeping “iPhone Pro Max” untranslated in all languages.

Why it matters: DNT tags (often implemented as XML tags or placeholders in TMS) prevent translators from accidentally translating brand names, product names, code variables, or other elements that must remain unchanged.

DTP (Desktop Publishing)

Adjusting document layout or format after translation so it still looks good.

Use case: Used when translated text is longer or shorter than the original, or when it’s in a design format.

Example: Resizing layout in a French brochure to fit the longer sentences.

Why it matters: Text expansion/contraction varies by language (German often 30% longer than English, Chinese 30% shorter). DTP ensures translated documents maintain professional appearance. Common tools: Adobe InDesign, Illustrator, FrameMaker.

F

Fuzzy match

A close but not exact match to something already translated.

Use case: Used to reuse parts of old translations to save time.

Example: TM suggests “Sign in to your account” for “Log in to your account.”

Why it matters: Fuzzy matches (typically 75-99% similar) reduce translation time and cost. Translators review and edit the suggested translation rather than starting from scratch. Pricing often reflects fuzzy match percentages (e.g., 85-94% match = 30% of full rate).

G

GILT

A short way to say globalization, internationalization, localization, and translation.

Use case: Used to describe the whole process of going global with content.

Example: A SaaS team planning its GILT strategy before expanding into Europe.

Why it matters: GILT represents the full spectrum of preparing products/content for global markets. Understanding each component helps teams plan comprehensively.

Globalization (G11N)

Preparing content or products to work well in any country.

Use case: Used before translation to make content flexible for different markets.

Example: Designing your checkout to support multiple currencies and languages from the start.

Why it matters: Globalization is strategic business planning for international markets. It encompasses market research, product adaptation, compliance, and go-to-market strategy—broader than just translation.

Glossary

A list of important words and how to translate them.

Use case: Used to make sure terms are translated the same way every time.

Example: Ensuring “support ticket” is always translated as “chamado de suporte” in Brazilian Portuguese.

Why it matters: Glossaries ensure terminology consistency across translators, projects, and time. Essential for brand voice, technical accuracy, and user experience. Modern TMS platforms auto-suggest glossary terms during translation.

GUI (Graphical User Interface)

The parts of software you click, tap, or see on screen.

Use case: Used to translate buttons, menus, and messages in apps and websites.

Example: Translating a “Submit” button into “Enviar” for Spanish users.

Why it matters: GUI localization (often called “software localization”) has unique challenges: character limits (buttons must fit), context ambiguity (“Save” = save document or save money?), and technical constraints (some platforms don’t support certain characters).

H

Hybrid translation

A mix of AI translation and human editing or review.

Use case: Used to speed up translation while keeping quality high—machines do the first draft, humans refine it.

Example: AI translates a user manual, and a human linguist edits it to ensure accuracy and clarity.

Why it matters: Hybrid translation (also called MTPE or AI-assisted translation) combines speed and cost-efficiency of AI with quality assurance of human expertise. Increasingly the industry standard for high-volume content.

I

In-context translation

Seeing how the text looks in its real place while translating.

Use case: Used to avoid awkward or confusing translations.

Example: Viewing the word “Home” in the header of a webpage during translation.

Why it matters: Context prevents mistranslation. “Bank” could mean financial institution or river bank. “Trunk” could be car trunk, tree trunk, or elephant trunk. Seeing the UI or document layout helps translators choose correctly.

International SEO

Helping websites rank well in search engines in different countries.

Use case: Used to bring more people to a localized website through search.

Example: Optimizing titles and keywords like “comprar zapatos” for Spanish-speaking users in Mexico.

Why it matters: Translation alone doesn’t improve search rankings. International SEO requires: localized keyword research, hreflang tags, country-specific URLs, local backlinks, and cultural content relevance. Critical for international traffic growth.

Internationalization (I18N)

Designing products so they’re easy to translate later.

Use case: Used to prepare software for multiple languages before translation starts.

Example: Moving all text out of the code and into a language file.

Why it matters: Internationalization is developer work that happens BEFORE translation. Poorly internationalized software is expensive or impossible to localize properly. Key elements: separating text from code, supporting Unicode, flexible UI layouts, locale-aware formatting.

K

Keys

Labels used in software to show text in different languages.

Use case: Used to connect a translation to the right spot in an app or website.

Example: Using a key like btn.submit to display “Submit” in English and “Soumettre” in French.

Why it matters: Keys (also called “string IDs”) are how developers reference translatable text. Good key naming conventions (descriptive, hierarchical) make translation easier and reduce errors.

L

Language code

A short code for a language, like “en” for English or “fr” for French.

Use case: Used to tag content and files with the right language.

Example: “de-DE” stands for German as spoken in Germany.

Why it matters: Language codes follow ISO 639-1 (2-letter) or ISO 639-2 (3-letter) standards. Locale codes combine language + region (en-US, en-GB, fr-FR, fr-CA) to handle regional variations.

Language pair

The two languages used in a translation, like English to German.

Use case: Used to describe what languages a translator works with.

Example: EN–JP is a language pair for English to Japanese.

Why it matters: Translation quality and cost vary by language pair. Common pairs (EN-ES, EN-FR) have more translators and lower rates. Rare pairs (IS-KO, Finnish-Japanese) are harder to source and more expensive.

Language Service Provider (LSP)

A company or freelancer that offers translation and localization.

Use case: Used when you need help with professional translation.

Example: Hiring an agency to localize your website and legal content.

Why it matters: LSPs range from individual freelancers to global agencies like Lionbridge, RWS, TransPerfect. Choose based on specialization (technical, legal, marketing), language coverage, turnaround time, and quality standards (ISO 17100 certification).

Literal translation

Translating word by word, without changing grammar or meaning.

Use case: Used for technical or legal content, not everyday writing.

Example: Translating “kick the bucket” literally instead of using the equivalent idiom for “die.”

Why it matters: Literal translation often fails because idioms, metaphors, and cultural references don’t translate directly. Professional translators balance literal accuracy with natural expression.

Locale

A language and region combination, like French (Canada).

Use case: Used to adjust spelling, dates, or currency for local users.

Example: Using “fr-CA” instead of “fr-FR” for Quebec-specific French.

Why it matters: Locales handle regional variations within same language: en-US (color, $) vs. en-GB (colour, £), es-ES (Spain Spanish, vosotros) vs. es-MX (Mexico Spanish, ustedes), zh-CN (Simplified Chinese) vs. zh-TW (Traditional Chinese).

Localization (L10N)

Translating and adapting content for a specific place or audience.

Use case: Used to make websites, apps, and content feel local.

Example: Changing currency symbols and translating product pages for the UK market.

Why it matters: Localization is more than translation—it includes cultural adaptation, visual localization, functional adjustments (payment methods, shipping), and compliance (GDPR, local regulations). Essential for international market success.

Localization ROI

The return on investment you get from localizing content, products, or experiences.

Use case: Used to measure whether localization efforts lead to more users, sales, or engagement in new markets.

Example: A company spends $5,000 localizing its app for Germany and sees a $25,000 increase in revenue from that region.

Why it matters: Localization requires investment. Measuring ROI (revenue increase, conversion rate improvement, customer acquisition cost reduction) helps justify budgets and prioritize markets. Typical ROI: 20-40% revenue increase in localized markets within first year.

Localization testing

Checking if a translated product works and looks right.

Use case: Used to catch layout problems or missing translations.

Example: Testing a mobile app in Arabic and finding a button overlaps with the text.

Why it matters: Localization QA catches issues like: UI text overflow (German 30% longer), right-to-left layout problems (Arabic, Hebrew), character encoding errors (special characters display as boxes), functional issues (date pickers, form validation), and cultural inappropriateness (imagery, colors).

M

Machine Translation (MT)

Automatic translation done by software like Google Translate.

Use case: Used for fast translation of lots of content.

Example: Translating 10,000 product listings into Spanish using AI.

Why it matters: MT has evolved from rule-based (1950s) to statistical (1990s) to neural (2016+). Modern neural MT (NMT) quality is impressive for many language pairs and content types. However, human review (MTPE) is still essential for quality, cultural appropriateness, and brand voice.

Markup language

Code that adds formatting to text, like HTML or XML.

Use case: Used to structure content on websites or apps during translation.

Example: Translating the content inside <h1> tags but leaving the tags untouched.

Why it matters: Translators must preserve markup tags while translating content. Breaking tags can break layouts or functionality. Modern CAT tools hide or protect tags, making translation easier and reducing errors.

Multilingual workflow

A system for handling translation into many languages at once.

Use case: Used by global companies to manage content across markets.

Example: Automatically sending blog posts to be translated into 12 languages on publish.

Why it matters: Scalable multilingual workflows require: centralized source content, translation management system (TMS), translation memory, glossaries, quality assurance automation, and project management. Without proper workflow, translations become bottleneck.

N

Natural Language Processing (NLP)

AI that helps computers understand and use human language.

Use case: Used to improve machine translation or analyze text.

Example: Using NLP to detect spam in different languages.

Why it matters: NLP powers many localization tools: machine translation, sentiment analysis, named entity recognition (identifying brand names, locations), and quality estimation (predicting MT quality before human review).

Neural Machine Translation (NMT)

A smarter kind of machine translation that uses AI to improve quality.

Use case: Used in tools like DeepL for smoother and more accurate translations.

Example: Translating complex sentences with better grammar and context using NMT.

Why it matters: NMT (introduced 2016) revolutionized MT quality. Unlike earlier statistical MT (word/phrase-level), NMT considers entire sentences, producing more fluent, contextually appropriate translations. Leading NMT engines: Google Translate, DeepL, Microsoft Translator, Taia AI translator.

NMTPE

Human editing of AI-generated translations to improve quality.

Use case: Used to combine speed of AI with accuracy of a translator.

Example: A human translator reviewing machine-translated product descriptions before publishing.

Why it matters: NMTPE (Neural Machine Translation Post-Editing) is increasingly the industry standard. Light PE fixes errors; full PE ensures publication-ready quality. Typically 30-60% faster than translating from scratch, with comparable quality when done properly.

O

OCR (Optical Character Recognition)

Technology that turns scanned documents or images into editable text.

Use case: Used when you need to translate content from PDFs or scanned contracts.

Example: Converting a scanned product manual into translatable text.

Why it matters: OCR quality varies by: image quality (resolution, contrast), font complexity (handwriting vs. typed), language (Latin script vs. Chinese characters), and layout complexity (tables, columns). Modern OCR (Google Cloud Vision, Adobe Acrobat) handles most typed documents well, but manual cleanup is often needed.

Over-the-Air (OTA) updates

Sending translation updates directly to an app or product without a new release.

Use case: Used to quickly push fixes or improvements to localized content.

Example: Updating a mistranslated button in a mobile app without publishing a full app update.

Why it matters: OTA updates enable continuous localization without release cycles. Mobile apps (using services like Lokalise, Phrase, POEditor) can update strings remotely. However, app store policies vary—check guidelines before implementing.

P

Pluralization

Making sure translations correctly handle singular, plural, and other number-related forms.

Use case: Used to avoid awkward text like “1 items” or “0 message.”

Example: Translating “You have 3 new messages” into Russian, which uses different word forms for each number.

Why it matters: Plural rules vary wildly across languages. English has 2 forms (1 item, 2 items). Russian has 3 (1 файл, 2 файла, 5 файлов). Arabic has 6. Polish has complicated rules. Proper internationalization handles these programmatically using ICU message format or similar.

Post-editing

Fixing and improving machine-translated text by a human translator.

Use case: Used to make sure AI-generated translations sound natural and accurate.

Example: A linguist reviews machine translations of marketing emails before sending.

Why it matters: Post-editing is distinct from translating from scratch. Light PE fixes errors and ensures comprehension. Full PE ensures publication-ready quality matching human translation standards. Requires specialized training—not all translators are good post-editors.

Proofreading

A monolingual review of the translated text to correct grammar, spelling, or flow—usually done by a native speaker.

Use case: Used as a final polish to catch surface-level issues before publishing.

Example: A native German speaker proofreads a translated brochure to fix typos and improve sentence flow.

Why it matters: Proofreading is the final quality gate. Unlike editing (bilingual, compares source and target), proofreading focuses on target language only—catching grammar, spelling, punctuation, and readability issues that slipped through earlier stages.

Pseudo-localization

Testing your app or site by replacing text with fake characters before actual translation.

Use case: Used to check if layouts break or if text expansion causes issues.

Example: Displaying ”[!!! Tèxt Êxpánded !!!]” in the UI to test spacing and encoding.

Why it matters: Pseudo-localization catches internationalization bugs before translation starts: hard-coded strings (not externalized), truncation issues (German 30% longer), encoding problems (special characters not supported), and concatenation errors (building sentences from code breaks grammar).

Q

Quality Assurance (QA)

Checking translations for errors, consistency, and formatting.

Use case: Used to make sure nothing was missed or broken during translation.

Example: Running automated QA checks to flag untranslated strings or incorrect tags.

Why it matters: Translation QA includes: linguistic review (accuracy, grammar, style), functional testing (UI works correctly), consistency checks (terminology, formatting), and compliance verification (legal, regulatory). Automated QA tools (Xbench, Verifika, ErrorSpy) catch common errors quickly.

R

Regional content preferences

Customizing text, visuals, or tone for specific regions within the same language.

Use case: Used to make content feel more familiar to local users.

Example: Using “color” for US English and “colour” for UK English.

Why it matters: Same language ≠ same content. Regional preferences include: spelling (US vs. UK English), vocabulary (“truck” vs. “lorry”), date formats, cultural references, legal requirements, and tone (American directness vs. British indirectness). Professional localization addresses these nuances.

Revision

A bilingual review of a translation that compares the source and target text to check for obvious errors in formatting or language.

Use case: Used to ensure the translation is faithful to the original and free of errors.

Example: A reviser checks a French translation against the original English document to correct any errors or inconsistencies.

Why it matters: Revision (also called “editing”) is bilingual quality control. Revisers check: accuracy (meaning preserved), completeness (nothing omitted/added), terminology (correct terms used), and style (appropriate tone, no awkwardness). Typically performed by senior translator or subject matter expert.

Right-to-Left (RTL)

Languages that are read from right to left, like Arabic or Hebrew.

Use case: Used to adjust layout, alignment, and icons to work in RTL mode.

Example: Flipping navigation menus so they flow right-to-left in Arabic.

Why it matters: RTL localization requires: mirrored UI layouts (navigation, icons, text alignment), bidirectional text support (mixing RTL and LTR content like URLs, numbers), and cultural design adaptation. Not just flipping—some elements (media player controls, charts) should NOT mirror.

S

SDK (Software Development Kit)

A collection of tools that help developers build and localize apps.

Use case: Used to add localization features or send text for translation.

Example: Using an SDK to connect your app to a translation platform.

Why it matters: Localization SDKs (from platforms like Lokalise, Phrase, Crowdin) enable: over-the-air translation updates, context screenshots, in-app translation management, and automated string extraction. Reduces developer overhead for internationalization.

Segment

A unit of text (usually a sentence or phrase) that’s translated individually.

Use case: Used in translation tools to break content into smaller, manageable parts.

Example: Translating “Add to cart” as one segment in a product UI.

Why it matters: Segmentation rules vary by tool and content type. Good segmentation: maintains context (sentences complete), enables TM matching (consistent splits), and facilitates review. Poor segmentation (mid-sentence breaks) produces awkward translations.

Segmentation

Breaking content into separate segments for easier translation.

Use case: Used to organize text logically and improve translation memory matches.

Example: Splitting a long paragraph into 3 sentences for easier reuse and quality control.

Why it matters: Segmentation rules determine how CAT tools split text. Factors: sentence boundaries (periods, question marks), abbreviations (Dr., Mr. shouldn’t split), lists (each item = segment), and tags (HTML elements influence splits). Custom segmentation rules improve TM leverage.

Simship (Simultaneous shipping)

Launching your product or content in all markets at the same time.

Use case: Used to ensure a global product launch happens without delays.

Example: Releasing a new game in English, Japanese, and Spanish on the same day.

Why it matters: Simship requires: agile localization workflows, continuous translation during development, abbreviated localization cycles, and efficient QA. Benefits: global launch momentum, prevents regional market leakage (importing products before local release), and maximizes marketing impact.

Source file

The original file that contains the text to be translated.

Use case: Used as the base for creating all translated versions.

Example: A Word document with the original English version of a report.

Why it matters: Source file format affects localization workflow. Translation-friendly formats: plain text, XLIFF, JSON, PO files. Complex formats requiring DTP: InDesign, Illustrator, PowerPoint. Some formats (scanned PDFs) require OCR before translation.

Source language

The language the content is originally written in.

Use case: Used to define the starting point of any translation project.

Example: English is the source language for a website being translated into French.

Why it matters: Source language quality affects all translations. Poorly written source content (ambiguous, inconsistent, culturally specific idioms) creates translation problems. Best practice: write for translation (clear, concise, culture-neutral) or localize source content first.

String

A piece of text (word, phrase, sentence) used in code or content.

Use case: Used to define what needs to be translated in software or websites.

Example: A string like “Forgot password?” appears in the login UI.

Why it matters: In software localization, “string” refers to any text element. String management includes: extraction (pulling strings from code), externalization (moving to resource files), contextualization (adding notes for translators), and version control (tracking changes over time).

Style guide

A document with rules on tone, voice, formatting, and preferred terms.

Use case: Used to keep translations consistent and on-brand.

Example: Telling translators to avoid contractions or use formal tone in legal content.

Why it matters: Translation style guides ensure brand voice consistency across languages. Include: tone (formal/informal), person (first/second/third), active vs. passive voice, terminology preferences, formatting conventions, and examples. Updated style guide = better translations.

Subtitling

Adding translated text to video content.

Use case: Used to make videos understandable for viewers in other languages.

Example: Subtitling a product tutorial video in Japanese.

Why it matters: Subtitling constraints: character limits (reading speed), timing (sync with audio), cultural adaptation (idioms, references), and technical specs (format, encoding). Different from dubbing (replacing audio) or voice-over (adding narration). Tools: Aegisub, Subtitle Edit, professional services for accuracy.

T

Target language

The language you are translating into.

Use case: Used to define the language of the final translated version.

Example: Translating from English to Italian—Italian is the target language.

Why it matters: Translators work into their native language (target language) for best quality. Exception: back translation (translating from native into non-native language to verify accuracy).

TEP (Translation, Editing, and Proofreading)

The standard three-step quality process for professional translation.

Use case: Used to ensure high-quality, accurate translations that read naturally.

Example: A document is translated by a native speaker, edited for accuracy by comparing it to the source, and then proofread for final polish before publication.

Why it matters: TEP is industry standard for mission-critical content. Translation handles meaning transfer. Editing ensures accuracy and completeness. Proofreading catches final errors. ISO 17100 (translation quality standard) requires minimum two-person review (translation + review).

Termbase

A database of important terms and their translations.

Use case: Used to keep translations consistent across teams and projects.

Example: Always translating “Terms of Service” the same way in every language.

Why it matters: Termbases (glossaries) are foundational for consistency. Modern termbases include: definitions, usage examples, images, forbidden terms (what NOT to use), and metadata (status, domain, part of speech). Integrated into CAT tools for real-time terminology suggestions.

Terminology management

Controlling how certain words or phrases are translated.

Use case: Used to avoid confusion or mistranslation in key parts of content.

Example: Making sure “account” always means a user profile, not a financial term.

Why it matters: Terminology management prevents: inconsistency (same term translated differently), ambiguity (context-dependent terms), and mistranslation (false friends). Requires: term extraction, validation by subject experts, termbase creation, and translator training.

Time zone localization

Showing dates and times in the user’s local format.

Use case: Used to avoid confusion for global audiences.

Example: Showing a meeting as 9 AM EST for US users and 3 PM CET for European users.

Why it matters: Time zone handling includes: automatic detection (browser/IP-based), conversion (displaying user’s local time), and formatting (12-hour vs. 24-hour, date format). Critical for: scheduling tools, event registration, delivery estimates, and real-time communications.

TM (Translation Memory)

Translation memory is a database that stores past translations for reuse.

Use case: Used to speed up future translations and keep wording consistent.

Example: Auto-suggesting a past translation of “Contact support” for reuse.

Why it matters: TM is cornerstone of modern translation. Benefits: consistency (same text translated same way), efficiency (100% matches require minimal review), cost savings (discounted rates for matches), and quality (proven translations reused). Modern TM includes: context matching, metadata, and AI-assisted suggestions.

TMS (Translation Management System)

Software that helps organize, assign, and manage translation projects.

Use case: Used to automate file handling, track progress, and collaborate.

Example: Using a TMS like Phrase or Lokalise to manage app localization.

Why it matters: TMS platforms centralize multilingual content management. Features: workflow automation, translation memory, glossaries, CAT tool integration, project management, reporting, and APIs for CMS/code repository integration. Essential for scaling localization.

TMX File (Translation Memory eXchange)

A standard file format used to store and share translation memory data.

Use case: Used to move translation memory between different tools.

Example: Exporting a TMX file from an LSP and importing it into Taia for a new project.

Why it matters: TMX is industry-standard format for TM portability. Enables: switching CAT tools without losing TM, sharing TM with clients/vendors, and consolidating TM from multiple projects. XML-based format defined by LISA/GALA standards.

Transcreation

Adapting a message for another culture while keeping its intent, tone, and impact.

Use case: Used for marketing copy or brand slogans that need more than translation.

Example: Turning “Just do it” into a locally powerful version that still inspires action.

Why it matters: Transcreation is creative adaptation for marketing/advertising. Translators (transcreators) have creative freedom to rewrite completely—goal is emotional impact and brand consistency, not word-for-word accuracy. Typically paid by hour (not word count) due to creative process.

Transcription

Writing down spoken audio as text in the same language.

Use case: Used to create subtitles, captions, or scripts for translation.

Example: Transcribing a webinar before translating it into German and Spanish.

Why it matters: Transcription precedes translation for audio/video content. Challenges: accents, background noise, overlapping speech, technical terminology. Automated transcription (Google Speech-to-Text, Rev.ai) handles clear audio well, but human review essential for accuracy.

Translatability

How easy a piece of content is to translate well.

Use case: Used to plan or write content that avoids idioms or complex phrasing.

Example: Rewriting “hit the ground running” to something more universal like “start quickly.”

Why it matters: High translatability = lower cost, faster turnaround, better quality. Translatability best practices: avoid idioms, use consistent terminology, write concise sentences, provide context, separate text from images, and use translation-friendly formats.

Translation

Changing text from one language into another while keeping its original meaning.

Use case: Used in every step of making content understandable to a new audience.

Example: Translating a product catalog from English to Polish.

Why it matters: Translation is linguistic conversion. Different from interpretation (oral), localization (cultural adaptation), and transcreation (creative adaptation). Professional translation requires: subject matter expertise, cultural knowledge, research skills, and quality assurance.

Translation memory leverage

How much of your content can be reused based on past translations.

Use case: Used to estimate effort and cost before starting a new translation project.

Example: If 70% of a document is already in your TM, only 30% needs to be translated.

Why it matters: TM leverage analysis predicts: project cost (weighted word count), timeline (new words take longer), and quality consistency. Modern TMS platforms provide leverage analysis automatically. Higher leverage = lower cost and faster delivery.

U

UI localization

Translating and adjusting the software interface for different languages.

Use case: Used to make sure apps are fully usable and clear for all users.

Example: Translating navigation menus and error messages in a CRM platform.

Why it matters: UI localization challenges: character limits (buttons, tabs), context ambiguity (short strings like “Open” = open file, open account, or open door?), and functional testing (ensuring translated UI works correctly). Requires: internationalization support, context screenshots, and native speaker testing.

User-generated content (UGC) localization

Translating or moderating content written by users.

Use case: Used in social platforms or review sites to support global audiences.

Example: Translating customer reviews into multiple languages on an e-commerce site.

Why it matters: UGC localization enables global communities. Challenges: volume (potentially millions of posts), informal language (slang, typos, emoji), moderation (detecting inappropriate content cross-culturally), and automation (MT + human moderation). Balance accessibility with authenticity (excessive editing loses voice).

V

Voiceover localization

Recording voice in another language to match original audio or video content.

Use case: Used in product videos, ads, or e-learning modules.

Example: Dubbing a product demo video from English into Korean.

Why it matters: Voiceover types: dubbing (replacing original audio, lip-sync), voice-over (narration over original audio), and UN-style (interpreter-style simultaneous translation). Requires: script translation, voice talent casting, recording/engineering, and timing synchronization. More expensive than subtitling but preferred for some markets (e.g., Germany prefers dubbing).

X

XLIFF (XML Localization Interchange File Format)

A standard format for exchanging localization files between tools.

Use case: Used to move translation files between systems while keeping structure intact.

Example: Exporting a set of website strings from a CMS in XLIFF format for translation.

Why it matters: XLIFF is XML-based standard for translation exchange. Preserves: source/target text pairs, context metadata, formatting tags, and translation status. Enables tool interoperability—send XLIFF to any CAT tool or LSP. Current version: XLIFF 2.1 (OASIS standard).

XML (Extensible Markup Language)

A format that holds structured data, often used in translation workflows.

Use case: Used to organize and deliver content in a format translators and tools can read.

Example: Translating product data stored in XML files.

Why it matters: XML structures content with tags indicating meaning and hierarchy. Translation workflow: parse XML, extract translatable content, protect tags during translation, and reconstruct XML with translated text. Common in technical documentation, software localization, and data exchange.

Final Thoughts

Localization can feel like a different language in itself. This glossary is here to make it easier. Whether you’re new to the field or just need a quick reminder, it’s a handy guide to help you work smarter and speak the same language as your team.

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