When a term base requires rendering X and the sentence context demands Y, a translation system can do exactly three things: enforce the glossary silently, follow the model silently, or flag the conflict and record the decision. Only the third is defensible in regulated work, because the first two produce output that looks finished and hides the one place a reviewer needed to look. Silent enforcement yields grammatically broken sentences that a fluent reader will notice; silent override yields fluent sentences carrying the wrong term, which nobody notices until a counterparty does.
This guide covers how each enforcement architecture behaves under pressure, the four situations where conflicts reliably cluster, what a usable enforcement report contains, and how to triage flags without stalling delivery.
What a Terminology Conflict Actually Is
A conflict is not a bug. It is the predictable result of two systems making claims about the same span of text.
A term base asserts a mapping decided in advance, out of context: this source term takes this target term, always. A translation model produces the most probable continuation given the surrounding sentence, in context. These agree most of the time. Where they disagree, the disagreement is usually information — either the entry is under-specified, or the sentence is doing something the entry's author did not anticipate.
The mistake is treating conflicts as noise to suppress. In a set of 200 pages, a handful of conflicts on defined terms in operative clauses is the highest-value review queue you will ever get, because each one marks a place where meaning is genuinely contested. A system that resolves them invisibly has thrown away its own best quality signal and replaced it with a clean-looking file.
Three Enforcement Architectures, Three Failure Signatures
Post-hoc find-and-replace. The text is translated, then terms are swapped in afterwards. It is the cheapest implementation and the most destructive: it replaces inside longer words, ignores case and agreement, and frequently breaks inline formatting tags because the replacement span does not align with the original run boundaries. Its signature is a sentence with one word in the wrong grammatical form and a lost italic.
Hard constraint at decoding. The required target string is forced into the output during generation. The sentence is built around the term, so formatting and agreement usually hold — but where the term genuinely cannot fit the construction, the model distorts the rest of the sentence to accommodate it. Its signature is an odd but grammatical clause.
Soft constraint. The term is supplied as guidance and the model usually complies. Its signature is silent non-compliance at a low, unpredictable rate — which is why soft constraints without reporting are unverifiable.
Ask which of the three you are buying. The answer determines what you have to review.
Why Silent Resolution Fails an Audit
Someone signs the translated document. In a regulated context that signature attaches to specific claims: that the terminology basis was applied, that departures were identified, and that a competent person reviewed them.
Silent resolution makes all three unprovable after the fact. There is no record of which entries fired, so you cannot demonstrate the term base was used at all, and no record of overrides, so a reviewer cannot be pointed at the passages that need judgement. The output is uniformly fluent, which means review cost is spread evenly across 200 pages instead of concentrated on the twelve places it belongs.
This is precisely the boundary ISO 18587 draws around post-editing of machine translation output: the human step exists to catch what the automated step could not resolve, and it can only do that if the unresolved items are visible. Note that ISO 17100, the standard more often cited in procurement, explicitly excludes machine-translation post-editing from its scope — a distinction worth getting right before a vendor conversation, not during one.
Where Conflicts Cluster
Four patterns account for most of them, and knowing the patterns lets you seed test documents that provoke them deliberately.
Inflection and agreement. The entry stores a citation form; the sentence needs a genitive, an instrumental, a plural or an adjectival agreement. German, Polish, Russian, Czech and Finnish generate these constantly, and they are the reason a term base entry should record a lemma plus permitted forms rather than a single string.
Homonyms across sections. Provision is an accounting balance in the notes and ordinary language in the narrative. Security is collateral, a traded instrument, or a safety concept — sometimes within one paragraph. One source string, two concepts, one entry that must be wrong somewhere.
Overlapping entries. Material Adverse Effect and Material Adverse Change; Board inside Board of Directors. Without deterministic longest-match precedence, output varies with import order.
Grammatical impossibility. The required target term is a noun phrase and the source uses the term as a verb, or the target form exists only in the plural. No resolution satisfies both constraints; the entry has to change.
Setting Defaults by Term Type
A single global policy is the wrong shape. Conflict behaviour should be set per term class, because the classes carry different risk.
Do-not-translate entries: enforce and flag. Identity is always grammatically safe for the string itself, and a frozen party name should never be quietly translated. Flag the instance so a reviewer can check the surrounding grammar, and see the do-not-translate list guide for how carrier nouns absorb the inflection.
Defined terms and locked renderings: enforce and flag. These carry the document's internal logic; consistency beats naturalness, and the flag tells the reviewer where naturalness was sacrificed.
General preferred terms: defer and flag. Where the entry is a house-style preference rather than a legal requirement, letting the model produce a grammatical sentence and recording the departure is the better trade.
Forbidden terms: block, always. A blocklist entry that can be overridden is not a blocklist. If a forbidden term is the only fit, the job stops and a human decides.
Record the policy per term class in the term base itself, not in a vendor's account settings.
What an Enforcement Report Must Contain
The report is what makes terminology auditable, and most platforms either lack it or produce a summary too coarse to act on. At instance level, each row needs: the source term, the entry identifier and term base version, a locator precise enough to find the passage — page, clause number or segment ID, not just "document" — the action taken, the target string emitted, and a reason code for any departure.
At document level: coverage counts, overrides grouped by reason, and a list of entries that never fired. That last one is the most underrated line in the report. A defined term that appears fourteen times in the source and zero times in the enforcement log means the matching is broken, the language tag is wrong, or the term never made it through import.
The format should be diffable — CSV or JSON attached to the output — so two document versions can be compared. Bluente returns terminology enforcement data alongside the translated file through its API, which allows this to run per batch rather than per procurement cycle.
Triaging Flags Without Stopping Delivery
Flags are only useful if someone can work through them at the speed the matter moves. Severity should follow document zone rather than term count.
Highest priority: a deferred or overridden defined term inside an operative clause, a representation, a warranty, an indemnity or a financial covenant. These change obligations. Next: terms in the notes to financial statements and anywhere a figure is captioned. Lowest: recitals, headings and correspondence, where a departure is a style question.
A practical rule is that overrides on locked entries block delivery while overrides on preferred terms travel with the file as annotations. That keeps the queue small enough to clear. The human judgement involved here is the part that does not automate, which is the honest framing of the automated versus human translation trade-off in regulated industries — and it matches what post-editors describe about their own work in this r/TranslationStudies thread on MTPE.
Closing the Loop Into the Term Base
An unresolved conflict that recurs on every job is a maintenance failure, not a translation failure. Every adjudicated flag should produce one of four outcomes, written back into the base.
Add the missing inflected form or mark the entry lemma-matched. Split a homonym into two concepts distinguished by subject field, so each fires only in its domain. Narrow a scope so an entry applies to contracts and not to marketing summaries. Or retire the entry entirely, with a status change rather than a deletion, so the history of the decision survives.
Structured formats support all four; a two-column spreadsheet supports none of them, which is why the storage question and the conflict question are the same question. The mechanics of getting a structured base in without losing those fields are covered in the TBX import checklist. Terminology maintained this way gets quieter every quarter. Terminology that is never fed back generates the same twelve flags forever.
Sources and Further Reading
ISO 18587 — requirements for full post-editing of machine translation output
ISO 17100 — translation services requirements, which exclude MTPE
MQM — analytic error typology for classifying terminology departures
r/TranslationStudies on MTPE in practice — post-editors describing which machine output errors require human adjudication
Related Reading
Terminology Control in Legal and Financial Document Translation
Automated vs Human Document Translation in Regulated Industries
Last reviewed 24 August 2026 by the Bluente document engineering team, who build and test the pipeline described here. We update these guides when the underlying standards, regulations or file formats change.
A conflict you can see is a review task; a conflict you cannot see is a liability. Try BluTranslate free.