Pushing the boundaries
of quality evaluation
At Phrase, we have reimagined how translation quality is evaluated, giving you greater control over AI translation performance.
Unlock AI translation at scale, with confidence.
AI translation is transforming global content delivery, but to deploy it safely across enterprise use cases, quality control must be as scalable and customizable as AI translation itself.
Phrase’s industry-leading quality evaluation capabilities deliver exactly that, enabling you to manage quality risks, increase automation, and ensure translations consistently meet defined standards.
A Leader in Localization
AI translation quality control in action
Customizable, fit-for-purpose quality checks
Phrase’s Quality Profiles let you define tailored quality requirements and automatically evaluate translations against them.
- Define company, content, or language-specific requirements with configurable checks
- Run comprehensive quality checks automatically
- Route only flagged segments to human or AI post-editing
Standardized quality insight in real time
QPS rates AI translation quality from 0 to 100 based on the standardized MQM methodology.
- Measure MT quality consistently across projects
- Track performance with QPS dashboards in Phrase Analytics
- Set QPS thresholds to balance quality and cost, routing only segments below the threshold to human or AI post-editing
Use AI to assist your LQA process
For high-risk, high-visibility content with the strictest quality requirements, consider linguistic quality assessment (LQA). Phrase Auto LQA uses AI to surface potential issues for experts to validate, so they don’t have to start from scratch. This can cut LQA time and cost by up to 80%.
The impact of scalable translation quality control
Phrase QPS and Quality Profiles work together to enable smarter, more automated workflows.
Reduce post-editing effort
Automatically lock high-quality segments and send only flagged content to human or AI post-editing.
Increase automation
Scale AI translation volumes with confidence, enabled by fully customizable, automated quality checks.
Control quality risks at scale
Ensure company- and content-specific requirements are consistently enforced, even across large volumes.
Lower costs and faster delivery
Less manual review means reduced post-editing costs and shorter turnaround times.
Frequently asked questions about machine translation quality evaluation
What is translation quality control and why does it matter?
Translation quality control is the process of evaluating and managing translation quality to ensure content meets linguistic, brand, and compliance standards.
In AI and machine translation workflows, quality control is essential to reduce risk, protect brand integrity, and enable automation. Reliable quality evaluation allows organizations to scale multilingual content production while maintaining consistent standards.
What is machine translation quality evaluation?
Machine translation quality evaluation assesses how accurate, fluent, and fit-for-purpose machine-translated content is.
It determines whether content can be published automatically or requires AI or human post-editing. Effective machine translation quality evaluation enables safe automation, reduces manual review, and supports enterprise-scale localization.
What is the Phrase Quality Performance Score (QPS)?
The Phrase Quality Performance Score (QPS) is an automated translation quality metric in the Phrase Platform, based on the MQM 2.0 framework.
QPS generates immediate quality scores that support AI translation quality control and automated workflow routing. By setting customizable quality thresholds, organizations can automatically lock high-scoring segments and route lower-scoring content for review, improving efficiency, transparency, and control.
How does AI translation quality control enable automated workflow routing?
AI translation quality control combines automated quality scoring with customizable thresholds and AI checks.
With this approach:
- High-quality content can be automatically approved and delivered
- Lower-quality machine-translated content can trigger AI or human post-editing
This intelligent routing reduces unnecessary post-editing, increases straight-through processing, and lowers localization costs while maintaining quality standards.
Can translation quality evaluation be customized?
Yes. Phrase allows organizations to tailor translation quality evaluation to their business needs.
Teams can:
- Define custom quality thresholds
- Configure AI-based quality checks by domain or language
- Align evaluation with brand voice and terminology requirements
This ensures quality control reflects real business risk rather than generic benchmarks.
What is MQM and how does it relate to translation quality evaluation?
MQM (Multidimensional Quality Metrics) is a standardized framework for categorizing and weighting translation errors.
Modern translation quality evaluation systems use MQM-based methodologies to produce structured, transparent scores that can be integrated into automated workflows. Phrase QPS is built on the MQM framework.
How does automated quality evaluation reduce post-editing effort?
Automated quality evaluation identifies which segments meet predefined quality thresholds.
This enables organizations to:
- Approve high-quality content without review
- Route only flagged segments for editing
- Focus linguist effort on higher-risk material
The result is lower post-editing volume and more efficient resource allocation.
What is rule-based QA in translation?
Rule-based QA (quality assurance) automatically detects predefined errors in translated content.
In Phrase, built-in QA checks in the TMS CAT Editor help translators identify issues such as:
- Terminology inconsistencies using term base checks
- Spelling errors
- Incorrect or missing tags
Rule-based QA strengthens translation quality control by catching objective errors before delivery.
How does software localization QA protect product quality?
Software localization QA ensures that translated UI content meets technical and brand requirements.
In Phrase Strings, QA checks help:
- Flag broken or missing placeholders
- Detect translations that exceed maximum character length
- Identify terminology that does not follow approved term bases
This prevents UI issues, protects syntax, and ensures consistency across localized software experiences.
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