What is LQA? Complete Guide to Linguistic Quality Assurance
Linguistic Quality Assurance (LQA) is a systematic process for evaluating the quality of translated content. It goes beyond simple proofreading to provide objective, measurable assessments of translation accuracy, fluency, and overall fitness for purpose.
In this comprehensive guide, you'll learn everything about LQA: what it is, how it works, and how to implement it effectively in your translation workflow.
What is LQA?
LQA (Linguistic Quality Assurance) is the process of evaluating translated content against defined quality standards. Unlike general proofreading, LQA uses structured methodologies—typically based on error typologies like MQM—to identify, categorize, and score translation errors.
LQA vs. Proofreading vs. Editing
| Activity | Focus | Output |
|---|---|---|
| Proofreading | Fixing surface errors | Corrected text |
| Editing | Improving style and clarity | Enhanced text |
| LQA | Evaluating quality objectively | Quality score + error report |
The key difference: LQA measures quality rather than just fixes problems. This measurement enables:
- Vendor performance comparison
- Quality trend tracking
- Objective feedback for translators
- SLA compliance verification
Why LQA Matters
1. Quality Consistency
Without LQA, quality varies unpredictably between translators, projects, and languages. LQA establishes consistent standards across all translations.
2. Cost Control
Poor translations are expensive. Rework, customer complaints, and brand damage cost far more than proper quality assurance. LQA catches issues early.
3. Vendor Management
When working with multiple translators or agencies, LQA provides objective data for performance evaluation and improvement.
4. Compliance
Industries like healthcare, legal, and finance require documented quality processes. LQA provides the audit trail.
5. Continuous Improvement
By tracking error patterns, LQA identifies systemic issues that can be addressed through training or process changes.
The LQA Process
A typical LQA workflow includes these steps:
Step 1: Define Quality Criteria
Before evaluation, establish:
- Error categories to track (accuracy, fluency, terminology, etc.)
- Severity levels (critical, major, minor)
- Passing threshold (e.g., MQM score ≥95)
- Sample size (100% or representative sample)
Step 2: Select Evaluators
LQA evaluators should be:
- Native speakers of the target language
- Subject matter experts (for specialized content)
- Trained in the LQA methodology
- Independent from the original translators
Step 3: Perform Evaluation
Evaluators review translations segment by segment, identifying errors and categorizing them by:
- Error type (mistranslation, omission, grammar, etc.)
- Severity (how much impact does this error have?)
- Location (which segment contains the error?)
Step 4: Calculate Quality Score
Using a scoring model like MQM:
Quality Score = 100 - (Penalty Points / Word Count × 100) Where penalty points depend on error severity:
- Critical: 25 points
- Major: 5 points
- Minor: 1 point
Step 5: Generate Reports
LQA reports typically include:
- Overall quality score
- Error breakdown by category
- Error breakdown by severity
- Specific error annotations with examples
- Comparison with historical performance
Step 6: Feedback Loop
Share LQA findings with translators for continuous improvement. Focus on:
- Patterns in error types
- Specific examples with correct alternatives
- Recognition for high-quality work
LQA Error Categories
Based on the MQM framework, common LQA error categories include:
Accuracy Errors
| Error Type | Description | Example |
|---|---|---|
| Mistranslation | Meaning incorrectly conveyed | "annual" → "monthly" |
| Omission | Content missing from translation | Skipped sentence |
| Addition | Extra content not in source | Unexplained additions |
| Untranslated | Source text left as-is | English term in Spanish text |
Fluency Errors
| Error Type | Description | Example |
|---|---|---|
| Grammar | Grammatical mistakes | "The datas is..." |
| Spelling | Misspelled words | "recieve" |
| Punctuation | Incorrect punctuation | Missing comma |
| Typography | Font, spacing issues | Double spaces |
Terminology Errors
| Error Type | Description | Example |
|---|---|---|
| Wrong term | Incorrect terminology | "mouse" as animal vs. device |
| Inconsistency | Same term translated differently | Varying product names |
| Unapproved term | Term not in client glossary | Using alternative without approval |
Style Errors
| Error Type | Description | Example |
|---|---|---|
| Register | Wrong formality level | "you" vs. formal equivalent |
| Unidiomatic | Awkward phrasing | Literal translation that sounds wrong |
| Inconsistent style | Varying tone within document | Mixing formal and casual |
Locale Errors
| Error Type | Description | Example |
|---|---|---|
| Date format | Wrong date convention | 12/31/2025 vs. 31/12/2025 |
| Number format | Wrong decimal/thousand separator | 1.000 vs. 1,000 |
| Currency | Incorrect currency handling | Wrong symbol or format |
LQA Severity Levels
Critical Errors
Errors that could cause:
- Legal liability
- Safety risks
- Financial loss
- Severe misunderstanding
Examples: Medical dosage errors, legal term mistakes, safety instruction omissions
Typical Penalty: 25 points
Major Errors
Errors that significantly impact:
- Comprehension
- User experience
- Professional appearance
Examples: Wrong meaning, confusing sentence structure, inappropriate tone
Typical Penalty: 5 points
Minor Errors
Errors with minimal impact:
- Noticed but don't affect understanding
- Minor stylistic issues
- Small typos
Examples: Minor punctuation, slight awkwardness, capitalization
Typical Penalty: 1 point
LQA Metrics and KPIs
Quality Score (MQM-based)
The primary metric. Calculated as:
Score = 100 - (Total Penalty / Word Count × 100) Error Rate
Errors per 1000 words:
Error Rate = (Total Errors / Word Count) × 1000 Pass Rate
Percentage of translations meeting quality threshold:
Pass Rate = (Passing Translations / Total Translations) × 100 Error Distribution
Breakdown of errors by:
- Category (accuracy, fluency, etc.)
- Severity (critical, major, minor)
- Translator or vendor
AI-Powered LQA
In 2025, AI is transforming LQA:
Traditional LQA vs. AI LQA
| Aspect | Traditional LQA | AI LQA |
|---|---|---|
| Speed | Hours per document | Minutes per document |
| Cost | High (human evaluator time) | Lower per evaluation |
| Consistency | Varies by evaluator | Highly consistent |
| Scalability | Limited | Virtually unlimited |
| Nuance | Excellent | Good and improving |
How AI LQA Works
Modern AI LQA tools use large language models to:
- Compare source and target texts
- Identify potential errors
- Classify errors by type and severity
- Calculate quality scores
- Generate detailed reports
AI LQA Limitations
While powerful, AI LQA has limitations:
- May miss subtle cultural nuances
- Can struggle with highly creative content
- Requires human validation for critical content
- Not yet suitable for final quality decisions in regulated industries
Best Practice: Hybrid Approach
Combine AI and human LQA:
- AI first - Quick initial assessment at scale
- Human review - Verify AI findings, especially critical errors
- Random sampling - Human spot-checks on AI-passed content
- Continuous calibration - Use human feedback to improve AI accuracy
Implementing LQA in Your Organization
Step 1: Choose Your Framework
Select an error typology:
- MQM - Industry standard, highly customizable
- LISA QA - Legacy but still used
- Custom - Based on specific needs
Step 2: Define Quality Tiers
Not all content needs the same quality level:
| Tier | Content Type | LQA Intensity | Pass Threshold |
|---|---|---|---|
| Premium | Legal, medical, marketing | 100% review | 98+ |
| Standard | Business, documentation | 20% sample | 95+ |
| Basic | Internal, user-generated | AI-only | 90+ |
Step 3: Select Tools
LQA tools range from:
- Spreadsheets - Simple but manual
- Dedicated LQA tools - Purpose-built for translation QA
- TMS-integrated - Built into your translation management system
- AI-powered platforms - Like KTTC with automated LQA
Step 4: Train Your Team
Ensure evaluators understand:
- Error category definitions
- Severity criteria
- Tool usage
- Calibration processes
Step 5: Establish Calibration
Regular calibration ensures consistency:
- Evaluate same content independently
- Compare results and discuss discrepancies
- Update guidelines based on learnings
FAQ
What does LQA mean in translation?
LQA stands for Linguistic Quality Assurance. It's the process of systematically evaluating translation quality using standardized error categories, severity levels, and scoring systems. LQA provides objective, measurable quality assessments rather than subjective opinions.
What is the difference between LQA and QA?
QA (Quality Assurance) is a broad term covering all quality-related activities. LQA specifically focuses on linguistic aspects of translation quality—accuracy, fluency, terminology, style, and locale conventions. Technical QA might cover formatting, functionality, or user experience issues that aren't linguistic.
How is LQA score calculated?
LQA scores are typically calculated using the MQM (Multidimensional Quality Metrics) model. Errors are identified, categorized by type and severity, and assigned penalty points. The score equals 100 minus the total penalty divided by word count times 100. For example: 100 - (15 penalty points / 1000 words × 100) = 98.5.
What is a good LQA score?
A good LQA score depends on content type and use case. Generally: 99-100 is excellent (publishable as-is), 95-98 is good (minor review needed), 90-94 is acceptable (corrections required), and below 90 typically requires significant revision. Critical content like legal or medical documents often requires 98+.
Can AI replace human LQA evaluators?
Not entirely. AI LQA is excellent for initial screening, consistency, and scale, but human evaluators remain essential for nuanced judgment, cultural adaptation assessment, and validation of critical content. The 2025 best practice is a hybrid approach combining AI efficiency with human expertise.
Conclusion
Linguistic Quality Assurance is essential for professional translation. By implementing structured LQA processes, organizations can ensure consistent quality, manage vendors effectively, and continuously improve their translation operations.
Whether you're using traditional human LQA, AI-powered tools, or a hybrid approach, the key is having a systematic, measurable process that provides actionable insights.
Ready to implement LQA in your translation workflow? Try KTTC for AI-powered linguistic quality assurance with MQM-based evaluation and comprehensive error reporting.