People who translate quickly do not merely turn on every possible warning. They configure translation QA settings so the system catches the errors that matter without drowning the translator in noise. Current search language around CAT tools and localization platforms repeatedly uses phrases such as translation quality assurance, automated QA checks, QA settings, terminology checks, tag mismatch, number mismatch, placeholder errors, and false positives. The practical reader problem is not whether automated QA exists. It is how to make automated checks useful enough that a translator can trust the warnings and move quickly.
A noisy QA system slows work because every warning demands attention, even when many warnings are harmless. A weak QA system also slows work because preventable errors survive until late review. The useful middle is a QA profile: a deliberate set of checks and tolerances matched to the content type, language pair, file structure, and risk. A software-localization profile may care intensely about placeholders, tags, character limits, and untranslated strings. A literary translation may care far less about matching source punctuation one-for-one and far more about target-language fluency. The same checklist should not govern both.
This article explains how translators configure QA profiles to translate faster, how to reduce false positives, how to distinguish critical checks from optional checks, how to test a profile before trusting it, and how to use content-specific rules without weakening quality. Its dominant reader job is narrow: configure the warning system so that automated translation QA points attention toward likely real defects. It does not replace the separate job of responding to a warning while translating; that belongs to live QA handling. This page owns profile design and signal quality.
Quick Read
Automated translation QA is fast only when its warnings have a high enough signal-to-noise ratio. If a translator sees two hundred warnings and knows that one hundred and eighty are harmless, the system trains the human to ignore it. If the system catches only spelling mistakes while missing placeholders, number changes, terminology deviations, or broken tags, it creates false confidence.
A QA profile solves this by storing a content-appropriate set of checks. The profile defines which issues are critical, which are warnings, which tolerances are acceptable, and which known patterns should be excluded. A good profile is strict where errors are mechanically dangerous and flexible where languages legitimately differ.
The central speed principle is simple: automated QA should spend human attention where machines have useful evidence. Do not force the human to investigate predictable false alarms. Do not ask the machine to judge nuance it cannot reliably infer.
One-Sentence Answer
People translate quickly with QA profiles by configuring automated checks around the real risks of a content type, suppressing predictable false positives, and escalating only warnings that are worth human attention.
Why QA Noise Is a Productivity Problem
A warning is not free. When a CAT tool flags an issue, the translator must stop, read the message, inspect the segment, decide whether the warning is real, correct it or dismiss it, and then reconstruct the translation state that was interrupted.
If the warning is valid, that interruption can save much more time later. If the warning is routinely irrelevant, it becomes pure friction.
This is why the number of enabled checks is not a good measure of QA strength. A profile with twenty well-tuned checks can outperform a profile with sixty indiscriminate checks because the translator learns that a warning usually deserves attention.
Signal quality matters because human behavior adapts. Noisy alarms create alarm fatigue. Precise alarms create fast response.
The Mechanism: Turn Generic Rules into a Content-Specific Filter
Most automated translation QA systems compare source and target or inspect the target against formal rules. They can detect things such as missing numbers, inconsistent terminology, absent placeholders, broken tags, double spaces, punctuation differences, untranslated text, capitalization differences, repeated words, or length violations.
A generic checker does not automatically know which differences are errors in your project. A source may use a full stop where the target language naturally does not. A product name may look untranslated because it is intentionally preserved. A target may contain an extra number because the language convention requires a date to be expanded. A term may be valid in two grammatical forms.
The QA profile supplies project knowledge. It tells the checker what kinds of differences deserve attention in this workflow.
Critical, Warning, and Informational Checks
One of the strongest ways to reduce QA friction is to classify checks by consequence.
Critical checks cover issues that can break functionality, alter facts, or create serious risk. Missing placeholders, invalid variables, broken tags, corrupted ICU syntax, changed numerical values in high-stakes data, or missing required text may belong here.
Warnings cover probable defects that need inspection but are not automatically wrong. Terminology deviations, punctuation differences, inconsistent translations, unusual capitalization, or target length may fit this level.
Informational checks highlight low-risk patterns or style concerns. They can be useful during polishing but should not block completion unless the project requires it.
This hierarchy lets the translator respond proportionally instead of treating a double space and a broken variable as equivalent events.
Build Profiles Around Failure Consequence
A useful QA profile begins with the question, “What can go wrong here, and what would it cost?”
For a mobile application, a missing placeholder can cause a runtime defect or display the wrong dynamic value. That deserves high priority. A button label exceeding a character limit can break the interface. A changed product code can misidentify a component.
For a long-form essay, punctuation may legitimately diverge from the source because sentence boundaries change. A strict punctuation mirror would create noise. But untranslated fragments, accidental omissions, and inconsistent names still matter.
The profile should follow the failure surface of the content.
Worked Example 1: Software Localization
Alicia translates software strings. The source contains placeholders such as {name}, %s, and {{count}}, HTML tags, short button labels, and product terminology.
Her software QA profile makes placeholder mismatch, malformed tags, and missing variables critical. It flags length overrun where hard limits exist. It checks approved terminology and untranslated text. It treats source-versus-target punctuation differences as lower priority because UI punctuation often changes legitimately across languages.
This profile is fast because it reflects the architecture of the content. When a critical warning appears, Alicia knows the string may actually break. She investigates immediately.
If every comma difference were presented with the same urgency, that trust would disappear.
Worked Example 2: Financial Reporting
Tricia translates quarterly financial reports. The dominant risks are numerical drift, date errors, currency mistakes, inconsistent financial terminology, missing table text, and accidental changes to defined labels.
Her QA profile therefore treats number mismatch as high priority. It also checks repeated terminology, percentage symbols, dates, and missing content. Tag checks remain active because the files contain formatting, but character-limit rules are irrelevant and disabled.
The profile does not assume every source punctuation mark must be mirrored. It also permits known number-format transformations, such as decimal and thousands separators, when the target locale requires them.
The result is not “less QA.” It is better-directed QA.
Worked Example 3: Marketing Copy
Kai Kai translates campaign copy where the target text often restructures sentences heavily. A source headline may become two clauses. Punctuation may change. A literal terminology check may produce false positives because brand language has approved variants.
His marketing profile still checks product names, URLs, prices, campaign codes, omitted text, and required legal wording. It lowers or disables rigid punctuation comparison and uses a smaller controlled terminology set.
The purpose is not to make marketing translation exempt from quality control. It is to stop low-value structural differences from competing with factual and brand-critical errors.
Worked Example 4: Technical Instructions
Technical instructions combine linguistic and mechanical risk. Numbers, units, warning labels, component names, step order, and tags matter. Repetition consistency may also matter because the same action should often use the same established term.
A strong technical profile can check number preservation, measurement units, terminology, missing segments, tags, repeated-source inconsistency, and perhaps imperative-style conventions if the tool supports them reliably.
It should not blindly require identical sentence length or punctuation because technical target language may expand or compress naturally.
The profile enforces what must remain stable while leaving legitimate translation freedom untouched.
Start with a Baseline, Then Tune from Real Warnings
Do not attempt to design the perfect QA profile in one sitting. Start with a sensible baseline for the content family. Run it on real completed work. Review the warning list.
Classify each warning:
- true error;
- useful warning but not error;
- predictable false positive;
- irrelevant check for this content;
- missed error that the profile should have caught.
This classification is more informative than debating QA theory. It shows how the profile behaves on the actual language and file structure.
Then tune. Disable irrelevant checks. Add exclusions for legitimate patterns. Raise the severity of high-consequence defects. Add missing checks where possible.
QA quality improves through feedback.
The False-Positive Budget
Some false positives are unavoidable. The goal is not zero. The goal is a tolerable rate.
Imagine a project produces one hundred warnings. If ninety are false positives, the system is nearly unusable. If ten are false positives and several of the remaining warnings catch real defects, the trade may be worthwhile.
You can think of this as a false-positive budget. Each noisy rule consumes attention. Keep a rule only if the value of the defects it catches justifies the review burden it creates.
High-risk checks can tolerate more noise than cosmetic checks. A placeholder rule that occasionally overflags may still be worth it. A punctuation rule that generates fifty harmless alerts probably is not.
Terminology Checks Need Linguistic Awareness
Terminology QA is powerful because approved terms often matter. It is also a major source of false positives when the system assumes words appear in one fixed form.
Languages inflect. A noun may change case. A verb may conjugate. An adjective may agree in gender and number. Compounds may split or merge. A term may appear inside another word. Tag boundaries may alter matching.
A useful terminology profile therefore uses the strongest matching capabilities available: morphology, case rules, boundary awareness, tag awareness, forbidden terms, and project-specific variants. Where the tool cannot model the language well, the termbase should avoid rules that create constant noise.
A warning that is linguistically impossible to satisfy is not quality assurance.
Tag Checks Should Be Strict Where Tags Are Structural
Tags represent formatting or embedded structure. In many CAT workflows, losing or misplacing them can damage the exported document. This makes tag QA mechanically valuable.
But not every tag must appear in the exact same visible position if the target language changes word order. Some tools distinguish tag presence from tag order, paired tags from standalone tags, and permissible movement from structural mismatch.
The profile should be strict about missing or extra structural tags while allowing legitimate reordering when the file format permits it.
Again, the goal is to detect breakage, not force the target to imitate source syntax.
Placeholder Checks Deserve High Trust
Placeholders often represent dynamic values such as usernames, prices, counts, dates, or system variables. If the source contains {user_name} and the target omits it, the application may display incomplete content. If a translator changes the variable syntax, the string may fail at runtime.
Because the relationship is mechanical, automated checking is strong here. Placeholder presence and integrity are often excellent high-priority rules.
However, profiles still need format awareness. %1, %s, {0}, {{count}}, ICU variables, and custom tokens behave differently. The checker must know what pattern to protect.
Number Checks Need Locale Intelligence
A simple number comparison can be misleading. The source 1,500.50 may correctly become 1 500,50 or another locale-specific form. Dates may reorder. Units may be converted when instructed. Phone numbers may retain country codes while spacing changes.
A useful number QA rule compares value, not merely character sequence, where the tool supports that sophistication. Where it does not, the profile may need exclusions or lower severity for formats known to transform.
Numbers are high-value QA targets, but only if the checker understands enough about legitimate representation changes.
Punctuation Checks Are Often Overconfigured
Punctuation is tempting because it is easy for software to compare. But easy detection is not the same as useful detection.
Languages differ in quotation marks, spacing around punctuation, sentence segmentation, colon usage, dashes, and whether a source sentence maps cleanly to one target sentence. A rule that demands identical ending punctuation may catch real omissions but also flag many correct translations.
Tune punctuation checks by content. In short labels or controlled technical text, they may be valuable. In freer prose, consider lower severity or narrower rules.
Do not confuse machine convenience with linguistic importance.
Untranslated-Text Checks Need Allowlists
A QA system may flag target words identical to source words. This can catch genuine untranslated content. It can also flag brand names, product codes, URLs, chemical symbols, acronyms, programming terms, and internationally shared vocabulary.
Maintain an allowlist for legitimate unchanged strings where possible. This dramatically improves signal.
The allowlist should not become a dumping ground for everything inconvenient. Each entry should have a reason. Otherwise real omissions can hide behind broad exclusions.
Repetition Consistency Checks Need Context
If the same source sentence appears five times and receives three target translations, QA may flag inconsistency. Sometimes that is valuable. Sometimes context legitimately requires variation.
A menu command repeated across the same interface should often be consistent. The word “Open” may translate differently when it is a button label, adjective, or verb in prose. Identical source text is not proof of identical function.
Use repetition consistency checks more strictly in controlled environments and more cautiously in context-sensitive prose.
Length Checks Need Real Constraints
Character-count QA is useful when a string truly has a limit: interface buttons, display panels, subtitles, database fields, signage, or constrained design components.
It is less useful when the limit is merely inherited from source length. Translation expansion is normal across languages. A rule such as “target may not exceed source by 10%” can generate noise without reflecting any real layout constraint.
Whenever possible, use actual field or product limits rather than arbitrary source-relative ratios.
Spellcheck Is Not the Same as QA Profile Design
Spellcheck can be part of a quality system, but it behaves differently from bilingual checks. It examines target words against dictionaries rather than comparing source and target structure.
A specialized translation may contain many correct technical terms that a general dictionary does not know. Add approved names and domain terms to custom dictionaries rather than accepting a sea of red underlines.
The same signal principle applies: a checker that labels correct domain language as suspicious teaches the translator to ignore it.
Failure Mode: Enable Everything
The easiest way to configure QA is to tick every box. It also produces some of the worst profiles.
Different rules are designed for different content. Enabling all of them shifts the burden from the machine to the human: the system emits every possible suspicion, and the translator must determine which categories were irrelevant from the start.
A profile is valuable precisely because it makes those choices before the live job.
Failure Mode: Disable a Noisy Rule Instead of Fixing It
A rule may be valuable but poorly configured. Terminology QA may generate noise because approved inflections are missing. Number QA may misread locale formatting. Tag QA may not recognize a permitted tag pattern.
Before disabling the rule, ask whether it can be tuned. Add variants, boundaries, allowlists, locale rules, or file-specific settings. Preserve useful protection where possible.
The right response to noise is not always less checking. Sometimes it is better modeling.
Failure Mode: One Profile for Every Language Pair
Languages differ structurally. Capitalization, punctuation, spacing, morphology, scripts, tokenization, and numbers can behave differently. A QA profile designed for English-to-French may not fit English-to-Japanese or German-to-Polish.
Shared content risks may remain, but language-sensitive rules should be adapted.
Where tools support reusable profiles, consider a content family plus language-pair layer rather than one universal configuration.
Failure Mode: One Profile for Every File Type
A DOCX report, JSON localization file, subtitle file, spreadsheet, and web UI do not expose the same mechanical risks. JSON may contain protected variables and code-like syntax. Subtitles have timing and reading constraints. Spreadsheets may contain numbers, formulas, and identifiers. DOCX may emphasize formatting and paragraph completeness.
File type is therefore a legitimate profile dimension when the structural risks differ materially.
Failure Mode: Treating Warnings as Proof of Error
Automated QA produces evidence, not verdicts. A warning says, “This pattern deserves inspection.” The translator still decides whether the target is correct.
If teams treat every warning as mandatory correction, translators may distort valid language merely to satisfy the checker. This is especially dangerous for punctuation, length, and terminology rules with imperfect linguistic awareness.
A good profile reduces this conflict by making warnings more meaningful, but human judgment remains final.
Failure Mode: Dismissing Without Learning
The opposite failure is clicking “ignore” repeatedly without asking why the warning recurs. If a rule produces the same false positive fifty times, update the profile or terminology data. If the warning is real fifty times, fix the underlying workflow that keeps creating the error.
Repeated warning patterns are process data. Use them.
Failure Mode: Allowlist Creep
Allowlists and exclusions reduce noise, but they can grow until they neutralize the checker. Someone adds one exception, then another, then an entire pattern, until real defects pass silently.
Review exclusions periodically. Remove temporary entries. Keep reasons for high-impact exceptions. Use the narrowest possible scope.
An exception should solve a known false positive, not create a blanket escape route.
Build a Small Core Profile First
A strong starter profile often includes a limited set of high-value checks:
- missing or extra placeholders;
- tag integrity;
- number and date anomalies appropriate to the locale;
- untranslated or missing text with controlled exclusions;
- critical terminology;
- obvious whitespace problems;
- file-specific constraints such as hard character limits.
Then add rules based on observed defects. This produces a profile grounded in actual risk rather than feature count.
A Three-Pass Tuning Method
Use three passes when developing a profile.
Pass 1: sensitivity. Run the profile on completed work with as many plausible rules as necessary to discover what the tool can catch.
Pass 2: noise reduction. Remove or tune rules that produce predictable false positives.
Pass 3: miss analysis. Review known errors that escaped. Add or strengthen checks where automation can realistically help.
Repeat after major content, tool, or workflow changes.
A Practical Severity Matrix
A simple matrix can guide rule severity.
Ask two questions: How severe is the consequence if this issue is real? How reliable is the automated detection?
High consequence + high detection reliability = critical.
High consequence + moderate reliability = prominent warning with human review.
Low consequence + high reliability = routine warning or auto-fix where safe.
Low consequence + low reliability = candidate for disabling.
This matrix prevents aesthetically easy checks from overshadowing functionally important ones.
Practical Check: Review Warning Density
Track how many warnings appear per thousand segments or per typical project. You do not need a perfect metric. The purpose is to notice sudden changes.
If warning density doubles after a glossary update, the terminology rules may have become noisy. If it drops to near zero despite known defects, the profile may be too permissive or not running correctly.
Warning density is a health signal, not a quality score.
Practical Check: Sample Dismissed Warnings
Periodically inspect warnings that translators dismissed. Are they truly false positives? Are people dismissing valid issues because they are rushed? Is one rule producing most of the noise?
This sample protects against silent alarm fatigue.
A profile is only as good as the human behavior it creates.
Practical Check: Sample Clean Segments
Do not review only warnings. Sample segments that passed QA. This reveals blind spots. Automated systems can only flag rules they know how to evaluate.
If clean segments contain recurring omissions or meaning errors, those problems may require another control: human review, improved source context, terminology, or different automation.
QA profiles are one layer, not the whole quality system.
Practical Check: Retest After Tool Updates
CAT and localization platforms change. Matching behavior, regex rules, terminology recognition, tag handling, and file parsers can improve or regress. A profile that behaved well last year may behave differently after an update.
When a major update affects QA or file processing, run a known test project and compare results.
Stable configuration still needs verification when the engine underneath changes.
Separate QA Profile Design from Live Warning Handling
This distinction protects the series architecture. QA profile design asks: Which checks should run, with what severity and exclusions? Live warning handling asks: A warning has appeared in this segment; what should I do now?
The first is configuration. The second is execution.
A well-designed profile makes live handling faster because the warning is more likely to matter. But the two reader jobs remain distinct and deserve separate URLs.
Separate QA Profiles from Final Human Revision
Automated QA excels at formal patterns. Human revision evaluates meaning, fluency, register, coherence, reader effect, and other qualities that are difficult to reduce to deterministic rules.
A QA profile should remove cheap detectable defects so the human reviewer can spend more attention on judgment.
The goal is not to automate quality. It is to allocate human review intelligently.
Transfer: QA Profiles as Attention Routing
The deeper mechanism is attention routing. A profile watches many mechanical details simultaneously and interrupts the translator when a pattern crosses a risk threshold.
This is the same principle used in monitoring systems elsewhere: good alerts identify exceptions worth human intervention. Bad alerts force humans to become filters for machines.
Thinking of QA as attention routing makes configuration decisions clearer. Every rule should justify the attention it consumes.
Transfer: Use Warning Data to Improve Upstream Work
If QA repeatedly catches missing placeholders, the source or CAT environment may need better placeholder protection. If terminology warnings dominate, the termbase may be incomplete or inconsistent. If number mismatches recur, the translation method may need a dedicated number check earlier.
Do not treat QA as a trash bin at the end of the process. Repeated defects should feed back into source preparation, terminology, templates, training, and tool configuration.
A mature workflow reduces the errors that QA has to catch.
When Not to Automate a Check
Do not automate a check simply because you can describe the preference. Some judgments are too contextual. “Sounds elegant,” “preserves irony,” “uses the right level of politeness,” or “does not overstate certainty” may be important, but a simple rule engine cannot reliably evaluate them.
When automation produces weak evidence, use human revision or a more appropriate evaluation method.
A smaller honest QA profile is better than an impressive list of unreliable rules.
Why This Makes People Translate Quickly
A strong QA profile shortens translation in two directions. It catches inexpensive errors while they are still cheap to fix, and it prevents the translator from wasting time on predictable false alarms.
That improves rhythm. The translator can move quickly because the warning system has a clear role. When the tool interrupts, the interruption is usually justified.
Fast translation is not the absence of checks. It is the presence of checks whose cost is lower than the rework they prevent.
Version QA Profiles So Speed Does Not Destroy Traceability
A profile can change while a long project is still running. Someone adds a terminology exception, changes a severity level, adjusts a number rule, or disables a check that became noisy. If the active configuration silently changes, two translators working on the same project may receive different warnings. That makes QA harder to interpret and can create disputes about why one file passed while another did not.
For recurring work, give meaningful profile versions or effective dates to important configurations. The exact method can be simple: Software-UI-QA v3, a dated change note, or a project template that points to a controlled profile. Record only material changes, not every minor edit. The point is to preserve enough history to answer a practical question: what rules were active when this work was checked?
Versioning also makes tuning safer. Instead of editing the only profile in place before a deadline, duplicate the current trusted configuration, test the proposed changes on a representative file, compare warning behavior, and then promote the new version. If the new profile creates unexpected noise or misses known defects, the earlier configuration remains available.
This does not require heavy governance. A two-line change log is often enough: “v4 — added ICU placeholder validation; reduced source/target punctuation mismatch to informational.” What matters is that translators can distinguish a deliberate rule change from random tool behavior.
Speed depends on confidence. If nobody knows which QA logic is running, every warning becomes harder to interpret. A small amount of traceability keeps the profile trustworthy and prevents repeated re-investigation of the same configuration decisions.
Advanced practice: regression-test the QA profile
A QA profile should be tested against known examples, not trusted because the settings look sensible.
Build a small regression set containing:
- a missing placeholder;
- a changed number;
- an approved terminology deviation;
- a forbidden term;
- a harmless punctuation difference;
- a legitimate untranslated brand name;
- a tag mismatch;
- an intentionally long target string.
Run the profile and check two things:
Recall: does the system catch the defects it is supposed to catch? Precision: does it avoid flagging legitimate target-language differences too often?
When the profile changes, rerun the same set. This prevents one adjustment from silently weakening another check.
Track false-positive families
Do not count false positives only as individual warnings. Group them by cause.
Examples:
- proper names flagged as untranslated;
- target punctuation flagged because the source convention differs;
- inflected terminology flagged because the termbase stores only a lemma;
- localized dates flagged as number mismatches.
A recurring false-positive family deserves a profile fix, exception list, or rule redesign. Repeated manual dismissal is wasted labor.
Use severity to control interruption
Critical structural checks can run live. Lower-value stylistic checks may belong in a batch pass.
A useful division is:
live blocking: placeholders, broken tags, critical variables. live warning: mandatory terminology, severe number mismatch. batch review: punctuation, spacing, stylistic consistency, length anomalies.
The exact split depends on the project, but the principle is stable: interruption power should rise with error consequence.
Version the profile
When a profile is shared across a team, record meaningful changes:
- rule enabled or disabled;
- threshold adjusted;
- exception added;
- project type affected;
- reason for change.
This makes QA behaviour explainable. If warning volume suddenly changes, the team can identify whether the content changed or the profile changed.
A mature QA profile becomes quieter over time because known false alarms are removed while high-value checks remain visible. That is the signal-quality goal: fewer interruptions, more trust, and faster response when a warning does appear.
Summary
QA profiles make translation faster by matching automated quality checks to the actual risks of the content, language pair, file type, and workflow. The best profiles emphasize mechanically reliable, high-consequence checks such as placeholders, tags, critical numbers, untranslated content, and controlled terminology, while tuning or lowering rules that create predictable linguistic false positives.
Do not enable every check by default. Start with a focused baseline, test on real work, classify warnings, reduce noise, investigate misses, and maintain narrow allowlists. Use severity levels so a broken variable does not look equivalent to a cosmetic punctuation difference.
Most importantly, remember what a QA warning is: evidence that deserves attention, not automatic proof that the translation is wrong. The profile should help the machine route attention; the translator still makes the judgment.
FAQ
What is a translation QA profile?
A translation QA profile is a reusable configuration of automated quality checks and their settings. It can define which issues are checked, their severity, tolerances, exclusions, terminology rules, tag rules, number rules, and other content-specific controls.
Why do QA tools produce false positives?
Because source and target languages legitimately differ in punctuation, morphology, word order, formatting, numbers, and terminology forms. Generic rules may not know those differences unless they are configured with language and project context.
Should I enable every QA check?
No. Enable checks that correspond to real project risks and that produce useful evidence. Too many irrelevant checks create warning fatigue and slow review.
Which QA checks are usually high value?
Placeholder integrity, tag integrity, missing or untranslated text, important number changes, critical terminology, and file-specific constraints are often high value because the machine can detect them relatively reliably. Exact priorities depend on the content.
Should punctuation mismatch be critical?
Usually not across all content. Punctuation often changes legitimately between languages. It may be useful in controlled strings or specific workflows but should be tuned to avoid excessive noise.
How can I reduce terminology false positives?
Use morphological or variant-aware matching where available, add approved inflections, set correct word boundaries and case rules, use tag-aware options where appropriate, and avoid term entries that are too broad.
What is alarm fatigue in translation QA?
It occurs when translators receive so many low-value warnings that they begin ignoring alerts in general, including valid ones. A well-tuned profile aims for a higher signal-to-noise ratio.
Is automated QA enough for final translation quality?
No. Automated QA is strong for formal and mechanical patterns. Human review is still required for meaning, fluency, register, coherence, tone, and many context-sensitive issues.
How often should a QA profile be reviewed?
Review it when content types, client requirements, terminology, file formats, or CAT-tool behavior change, and periodically when recurring false positives or missed errors become visible.
What is the difference between a QA profile and live QA warnings?
The QA profile defines which warnings the system should generate and how strict they should be. Live QA handling is the translator’s response when a particular warning appears during work.
Internal-Link Opportunities
This article can naturally link to the existing eduKateSG pages How People Translate Quickly | Live QA Warnings: Catch Cheap Translation Errors Before You Leave the Segment, How People Translate Quickly | Segment Filtering, How People Translate Quickly | Placeholders and Non-Translatables, How People Translate Quickly | TM Penalties and Match Thresholds, and Master Art of Translation | The Human Translation System. This page should remain the canonical owner of configuring translation QA profiles and reducing warning noise.