The AI-powered translation scoring feature helps you evaluate translation quality by identifying potential issues and assigning a score from 0 to 100 based on Multidimensional Quality Metrics (MQM).

A high score doesn't guarantee a perfect translation. It simply means that AI scoring detected fewer or less severe issues.

You can request scores for individual translations in the editor or enable scoring for eligible Pro AI translation tasks. AI scoring is optional and disabled by default for tasks created in the Lokalise interface.

This helps you focus human review where it's needed most, rather than reviewing every translation or skipping reviews entirely.

With translation scoring, you can:

Translation scoring is integrated into the Lokalise editor, automatic translation tasks, and Workflows, helping teams incorporate quality checks into their existing localization processes.

The goal of Translation Scoring isn’t to replace human reviewers: it's to help them focus on the content that truly needs attention. Instead of reviewing every string manually (slow and expensive), teams can:

In practice, teams can use translation scores to prioritize human review. Lower-scoring translations may need closer inspection, while higher-scoring ones may require less attention, depending on the content and its importance.

This can help reduce unnecessary review work, but a high score doesn't guarantee that a translation is error-free.

MQM (Multidimensional Quality Metrics) is a widely used framework for evaluating translation quality. It organizes translation issues into categories such as accuracy, fluency, terminology, and style, and distinguishes between different levels of severity.

Lokalise uses an MQM-based approach to identify potential issues in translations, explain their severity, and calculate quality scores.

This gives reviewers more actionable information than a score alone: they can see what problems were detected and decide whether corrections are needed.

To see translation scores, open your Lokalise project editor and look for a target translation value. You’ll notice a small lens icon next to it:

This icon only appears for target translations, not source (base) values.

Click the lens to trigger scoring. AI will check the translation quality and show a score. Click the score to get more details. If the translation has issues, points will be deducted, and you’ll see a list of detected problems:

You can retrieve AI scoring information programmatically using the Lokalise MCP server or REST API.

You can generate scores for many translations at once. To do this, select one or more keys in the editor, then choose Create a task from the bulk actions menu.

In the task settings, select Automatic translation as the task type. To assess the quality of translations generated by AI, enable Trigger AI quality scoring with task. This option is disabled by default.

When enabled, Lokalise evaluates eligible AI-generated translations and assigns quality scores from 0 to 100. Scoring does not consume additional AI words from your quota.

AI scoring is available for Pro AI translation tasks when the task's source language matches the project's base language. Translations retrieved from 100% translation memory matches are not scored.

You can also set up scoring as part of your workflows. To do this, go to the Workflows page and create a new workflow that includes the Review task with AI scoring step. This step comes after the AI translation step, which may be preceded by the TM step.

When setting up the AI scoring step, you’ll see the following options:

Translation scoring is powered by the MQM (Multidimensional Quality Metrics) framework — the industry standard for evaluating translation quality. MQM breaks down issues into categories such as grammar, spelling, fluency, terminology, and meaning errors. Each issue type is assigned a penalty weight based on severity:

The final score is calculated as:

Examples:

Score thresholds:

In workflows, you can configure a score threshold to automatically route lower-scoring translations for human review. Higher-scoring translations can continue through the workflow without being added to that review task, depending on your configuration.

Remember that AI Scoring provides an additional quality signal, not a guarantee that a translation is ready to publish.

Both translation scoring and AI LQA help evaluate translation quality using the MQM framework, but they are separate features that work differently and serve different purposes.

Although both features assess translation quality, they use different underlying processes. Translation scoring is designed for evaluating individual translations and supporting automated review decisions, while AI LQA focuses on producing detailed reports for broader quality assessments.