Businesses expanding into international markets often face challenges with localization, especially when working with large volumes of translated text. One of the most common complications occurs when machine translation tools inadvertently break JSON key structures during bulk imports. Maintaining functional and structurally sound localization files is crucial to support seamless user experiences across platforms and languages. Tools like Lokalise have played a pivotal role in helping businesses address these issues effectively and at scale.
TLDR: Recovering from Machine Translation Errors with Lokalise
Companies using bulk machine translation imports often encounter broken JSON keys, leading to application failures or corrupted UI text. Lokalise offers features such as customizable placeholders, structured key validation, and automated QA checks that have enabled teams to identify, fix, and prevent such errors efficiently. Businesses have reported significant time savings and fewer post-deployment bugs by incorporating Lokalise into their localization workflows. Proper use of this platform has helped restore integrity across multilingual JSON files while maintaining speed in global rollouts.
The Nature of the Problem: Broken JSON Keys
JSON (JavaScript Object Notation) is commonly used for structuring localized text across applications. When businesses import translations obtained from automated tools like Google Translate or DeepL directly into JSON files, they risk corrupting syntactic structures. Some of the most frequent issues include:
- Translation of key names instead of just values
- Loss of escape characters such as backslashes or quotes
- Misplaced punctuation disrupting the hierarchy of nested objects
- Placeholder formats (e.g.,
{user_name}) being mistranslated or malformed
Upon deployment, such corruptions often trigger UI rendering failures or cause applications to crash. Detecting these issues manually in massive localization files spanning dozens of languages becomes a logistical nightmare for many engineering teams.
Why Machine Translation Tools Amplify the Issue
While machine translation has made content localization faster, many such tools are not context-aware when it comes to technical structures. By default, these systems operate on textual strings without understanding their role inside a code-based syntax. For example, they may:
- Translate a key like
"submit_button"into another language, mistaking it for user-visible content - Rewrite parameterized content such as
"Welcome, {user}!"as"Willkommen, {benutzer}!", breaking the parameter linkage
Without safeguards in place during the import process, such mistakes quickly proliferate through multiple translated files. The complexity escalates for platforms localizing content across iOS, Android, web, and backend systems concurrently.
How Lokalise Becomes the Fix
Lokalise offers a suite of features specifically designed to maintain the structural integrity of localization files. Businesses that adopted Lokalise to repair JSON files after faulty translation imports cite a combination of automation and human-friendly tools as key advantages. Here’s how:
1. Structured Import Validation
When JSON files are imported into Lokalise, the platform conducts schema-level validation, flagging keys that are:
- Improperly formatted or missing delimiters
- Inconsistent in naming conventions across languages
- Potentially altered during translation based on key identity checks
This early detection mechanism ensures that corrupted keys are caught before they make it into production codebases, reducing rollback scenarios significantly.
2. Placeholder Protection
Lokalise allows teams to define custom placeholders and variables across languages, such as {product_name} or %1$d. These placeholders are automatically identified and protected during translation, even if machine-generated. If a translated string fails to preserve a placeholder, the platform flags it for manual review.
3. Bulk Key Comparison and Restoration
Users can run side-by-side comparisons of source JSON files with the translated ones. Lokalise tracks metadata for every key—who changed it, when, and how—and allows easy restoration to previous versions. Teams frequently use this feature to identify keys that drifted from original formats post-import.
4. Integration into CI/CD Pipelines
Another critical functionality is Lokalise’s compatibility with DevOps pipelines. Developers can set automated jobs to validate updated localization keys during every commit or deployment. This prevents corrupted translation files from reaching QA or live environments in the first place.
Case Studies: Practical Implementations
Fintech Startup Reverses Botched Release
A European fintech startup imported 32 languages worth of translations via a bulk Google Translate API integration. When the release went live, users on iOS and web experienced broken buttons, rendering crashes, and missing fields in forms. Their team quickly traced the issue to the translation of JSON keys and malformed strings.
By importing their existing JSON files into Lokalise, they were able to use the QA and placeholder protection tools to identify over 218 broken keys. Within 48 hours, the team had re-validated and restored each affected file, rolling out a corrected update and using Lokalise’s webhook-based verification for future deployments.
E-commerce Giant Saves 2,000 Man-hours
One global e-commerce brand used machine translation to localize over 100,000 string keys for their consumer and vendor platforms. When deployed, discrepancies in placeholder formatting across variations in French and Arabic caused widespread checkout failures.
After transitioning to Lokalise, the company implemented automated placeholder scanning and deployed the in-context editor to QA teams. Debugging and fixing these files across 20 languages would have taken six developers close to six weeks. With Lokalise’s tools, the process was reduced to under four days including internal QA testing.
Key Takeaways for Localization Teams
Prevent Rather Than Repair
Lokalise’s value isn’t just in fixing broken keys but in preventing those breaks from occurring in the first place. Best practices include:
- Using key lock functions to ensure only values are translated, not the keys themselves
- Setting up automatic QA rules to flag malformed placeholders immediately
- Educating translators through Lokalise’s UI by using comments, screenshots, and context indicators
Establish Cross-Team Collaboration
Lokalise enables engineers, designers, translators, and QA testers to work in a unified interface. This cross-functional collaboration ensures that bugs in translation syntax are caught early. By issuing reviewer permissions and status tracking per-language, businesses can maintain stronger governance around localization pipelines.
Automate for Scale
As businesses scale internationally, relying purely on manual JSON checks is unsustainable. Automation via Lokalise integrations with GitHub, Bitbucket, or GitLab enhances visibility and control. APIs allow for export hooks that trigger builds only if localization files pass all validation tests, further streamlining the release lifecycle.
Conclusion
In an increasingly multilingual digital world, the cost of broken JSON keys due to machine translation errors is too high to ignore. From empty fields to app-breaking UI failures, the risks cascade as localization complexity grows. Lokalise presents a powerful, structured approach that lets organizations manage technical localization with the same rigor as code development. More than just a translation manager, it acts as a guardian of syntactic integrity across languages and platforms.
Adopting Lokalise can mean faster turnaround, fewer bugs, and less rework—while maintaining the precision needed in today’s global software ecosystems.
