7 Translation Quality Assessment Techniques

Want to ensure your video translations hit the mark? Here's a quick guide to 7 key methods:

  1. BLEU scores: Fast automated checks

  2. Human Translation Edit Rate (HTER): Measures human editing needed

  3. Multidimensional Quality Metrics (MQM): Detailed quality breakdown

  4. SAE J2450 standard: Tailored for automotive content

  5. LISA QA Model: Ideal for software localization

  6. Error types and weighted scoring: Customizable approach

  7. Dynamic Quality Framework (DQF): Handles large-scale projects

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Quick Comparison

MethodSpeedDepthBest For
BLEUFastLowInitial filtering
HTERMediumMediumSubtitle projects
MQMSlowHighVaried content
SAE J2450MediumMediumTechnical videos
LISAMediumHighUI/UX projects
Weighted ScoringCustomizableHighBrand-sensitive content
DQFSlowVery HighLarge-scale projects

Each method has its strengths. Often, combining approaches works best. Pick based on your video's needs, audience, and resources.

Remember: Good translations keep viewers hooked, protect your brand, and avoid legal issues. Bad ones? They can cost millions to fix (just ask HSBC).

Let's dive in and find the right quality check for your video content.

Basics of translation quality assessment

What it is and aims to do

Translation quality assessment (TQA) checks translations for accuracy, consistency, and cultural fit. It's not just about typos - it's making sure your message lands in any language.

TQA aims to:

  • Fix errors before they cause issues

  • Keep your brand voice consistent across languages

  • Ensure translations work for target audiences

For videos, this means checking subtitles, voiceovers, and on-screen text work together smoothly.

Common problems

Even great translators face challenges. Here are some TQA headaches:

1. Source content issues

Poor original writing can create bigger problems when translated.

2. Lack of context

Translators often work without seeing the full picture, leading to misunderstandings.

3. Cultural mismatches

What works in one culture might flop (or offend) in another.

4. Technical limitations

Some TQA tools struggle with video elements like time-synced subtitles.

5. Subjective quality

Reviewers often disagree on what's good.

Many companies use both automated tools and human reviewers. Netflix, for example, uses machine learning and human experts to check subtitle quality across its huge content library.

Setting clear standards is key. Without them, you're guessing. As Nataly Kelly, VP of Localization at HubSpot, says:

TQA isn't one-size-fits-all. Your approach depends on your content, audience, and goals. A global ad campaign might need multiple human reviews. Internal training videos might only need automated checks.

7 key translation quality assessment methods

Let's dive into seven ways to check if your video translations are up to snuff:

1. BLEU scores

BLEU compares machine translations to human ones, scoring from 0 to 1. Higher is better:

BLEU ScoreWhat it means
< 0.10Useless
0.10 - 0.19Confusing
0.20 - 0.29Gets the idea, but messy
0.30 - 0.40Decent to good
0.40 - 0.50Pretty great
0.50 - 0.60Top-notch
> 0.60Might beat humans

It's quick, but not perfect. BLEU cares more about words than meaning, which can be tricky for videos.

2. Human Translation Edit Rate (HTER)

HTER shows how much editing a machine translation needs. It's great for subtitles, showing how much work it'll take to make them usable.

Fun fact: HTER cuts down editing by 33% compared to older methods.

3. Multidimensional Quality Metrics (MQM)

MQM looks at translation quality from different angles:

  • Accuracy

  • Fluency

  • Terminology

  • Style

  • Local flavor

You can tweak it for different video types and audiences.

4. SAE J2450 standard

This standard spots seven types of translation errors:

  1. Wrong word

  2. Grammar goof

  3. Missing stuff

  4. Word agreement issues

  5. Typos

  6. Punctuation problems

  7. Miscellaneous mess-ups

It's great for how-to videos or product demos.

5. LISA QA Model

LISA checks:

  • Language quality

  • Formatting

  • Functionality

It's perfect for making sure subtitles or voiceovers fit technical limits like character counts or timing.

6. Error types and weighted scoring

This method ranks errors by how bad they are. For videos, it might look like:

Error TypeHow bad is it?
Meaning change3
Terminology2
Language quality2
Style1
Formatting1

You can adjust based on your video. A flashy ad might care more about style, while a tech tutorial needs spot-on terminology.

7. Dynamic Quality Framework (DQF)

DQF is flexible and plays nice with other methods. It lets you:

  • Create custom error categories

  • Compare to industry standards

  • Evaluate machine translations

It's great for balancing quality with time and money, especially for big projects.

Often, using a mix of these methods works best. Pick based on your video's needs, audience, and resources.

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Comparing the 7 assessment methods

Let's look at how these translation quality assessment techniques stack up:

Comparison chart

MethodProsConsBest Use Case
BLEU scoresFast, automatedWord-focused, not meaningFiltering poor translations
HTERShows editing neededNeeds human referenceSubtitle projects
MQMFlexible, multi-aspectCan be complexVaried content types
SAE J2450Industry-specificLimited error typesTechnical videos
LISA QA ModelChecks language, format, functionMay miss subtle errorsUI/UX projects
Error types and weighted scoringCustomizable prioritiesSubjective weightingBrand-sensitive content
DQFAdaptable, integrates wellResource-intensiveLarge-scale projects

BLEU scores are quick but lack depth. One expert said: "BLEU can filter out bad translators, but the highest-BLEU ones might not be the best to human eyes."

HTER works great for subtitles, showing how much human touch-up machine translations need. It's perfect for video content with tight timing and character limits.

MQM is flexible enough for casual YouTube videos or formal corporate presentations. But it might be too much for smaller projects.

SAE J2450 is the go-to for automotive and manufacturing content. It's great for how-to videos in these industries, but not much else.

The LISA QA Model is ideal for technical accuracy. It's perfect for subtitles or voiceovers with specific requirements, but might miss subtle language issues.

Error types and weighted scoring let you customize based on project needs. Style might matter more for an ad, while accuracy is key for a tech tutorial.

DQF shines in large projects where you need to balance quality, time, and budget. But it can be a lot of work to set up.

Many companies mix these methods. They might use BLEU scores to filter, then apply MQM or weighted scoring for final checks.

Remember Kmart's 2020 Mother's Day "#mamaste" mishap? Using methods that focus on cultural context, like MQM, could have caught this before it became a PR problem.

Tips for using quality assessment

Setting quality standards

Start with clear, measurable goals for your translation quality assessment. For video content, focus on accuracy, cultural fit, and technical aspects like timing and lip-sync.

Netflix sets the bar high:

They demand 95% or higher accuracy for subtitles and dubbing in all languages. This covers linguistic accuracy, cultural adaptation, and technical precision.

Training and consistency

To keep your quality checks on point:

  1. Create a solid training program

  2. Use real examples in training

  3. Do regular calibration exercises

  4. Use tech to stay consistent

Spotify's approach is worth noting. They train both in-house and freelance translators rigorously.

Consider using a quality assessment tool like MemoQ. It has built-in QA features that catch inconsistencies automatically.

AspectBenefitExample
Clear standardsConsistent qualityNetflix's 95% accuracy rule
Structured trainingSkilled checkersSpotify's quarterly sessions
Tech useAuto consistency checksMemoQ's QA features

What's next in translation quality assessment

AI and machine learning uses

AI and machine learning are changing how we check translation quality for video content. Here's what's coming:

Smarter error detection

AI tools can spot mistakes humans might miss. They're getting better at catching subtle errors in context, tone, and cultural fit.

Faster quality checks

AI can review translations much quicker than humans. This means faster turnaround times for video projects.

Improved consistency

Machine learning helps keep translations consistent across large projects. It learns from past work to suggest better translations.

Future changes

The translation industry is changing fast. Here's what to expect:

Hybrid human-AI workflows

Humans and AI will work together more. AI will handle initial translations and quality checks, with humans fine-tuning the results.

Cultural adaptation focus

As AI gets better at basic translation, human experts will spend more time on cultural nuances and context.

New quality metrics

We'll see new ways to measure translation quality that combine AI insights with human judgment.

Personalized translations

AI might learn individual preferences, leading to more tailored translations for specific audiences or brands.

Ethical and privacy concerns

As AI use grows, so will debates about data privacy and ethical use of translation tech.

AspectCurrent StateFuture Trend
SpeedHuman-pacedAI-accelerated
AccuracyHuman-checkedAI-assisted
ConsistencyManual effortAutomated learning
Cultural fitHuman expertiseAI + Human collaboration
Quality metricsStaticDynamic and personalized

These changes will reshape how we assess and improve translation quality for video content.

Wrap-up

Translation quality assessment is crucial for video content projects. Here's what you need to know:

  • Mix methods: Use BLEU scores, HTER, MQM, SAE J2450, LISA QA Model, error types scoring, and DQF together.

  • Accuracy and culture matter: Check both word-for-word translation and cultural fit.

  • Strong QA process: Combine human expertise with tech tools. Smartling's AI can catch subtle errors humans might miss.

  • Clear standards: Define "good" for your project to keep quality consistent.

  • Team training: Regular training can boost accuracy by up to 40%.

  • Use data: Track error rates and user feedback to improve over time.

  • AI + humans: AI is smart, but humans are key for cultural nuances.

Assessment AreaKey Focus
LinguisticGrammar, spelling, terminology
CulturalAppropriateness, local references
VisualLayout, formatting
FunctionalFeatures work in translated version

FAQs

What should you look out for when assessing translation quality of the app?

When checking app translation quality, keep an eye on:

  • Accuracy: Does it say what it's supposed to?

  • Consistency: Are terms used the same way throughout?

  • Language: Any grammar or spelling slip-ups?

  • Cultural fit: Does it make sense for the target audience?

What criteria can be used to evaluate the quality of a translation?

Here's what to look for:

CriterionWhat it means
MeaningDoes it say the same thing as the original?
WordingIs the language right for the audience?
ErrorsAre there any mistakes?
ConsistencyDo terms and style stay the same?

What are the types of error in translation?

Translation errors come in two flavors:

1. Surface errors:

  • Meaning mix-ups

  • Wrong word choices

  • Incorrect word forms

  • Grammar goofs

2. Deep errors:

  • Cultural clashes between languages

Spotting these helps translators and reviewers up their game.

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