Machine Translation (MT) combined with post-editing has emerged as a popular solution for businesses needing fast and affordable multilingual content. But beneath the surface of rapid delivery lies a complex trade-off: speed often comes at the expense of quality, and that compromise can be costly. This article explores the real costs—both visible and hidden—of relying on MT + post-editing workflows, and how to strike the right balance between speed and accuracy.
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What is MT + post-editing, and how does it work?
MT + post-editing is a two-step translation process:
- Machine Translation (MT): Text is automatically translated using AI tools like Google Translate, DeepL, or proprietary neural engines.
- Post-editing: A professional human linguist reviews and revises the output to improve grammar, terminology, fluency, and accuracy.
There are two levels of post-editing:
- Light post-editing: Basic corrections to ensure readability and comprehension.
- Full post-editing: In-depth revision to make the translation equivalent in quality to a human-produced one.
This method is used to save time and lower costs while still producing usable translations—but the results vary depending on content, context, and workflow.
Is MT + post-editing truly faster than human translation?
In many cases, yes—but not always. The speed gain depends on several factors:
Text complexity
Machine translation handles simple, repetitive texts well, which can reduce post-editing time significantly. However, for technical, legal, or marketing content, human translators may end up rewriting entire sections—negating time savings.
Language pair
MT performs better with high-resource languages like English-Spanish or English-French. For less common pairs or non-Latin scripts, the machine output may be too inaccurate to use efficiently.
Editor expertise
Post-editors with domain knowledge and MT experience work faster and more efficiently. If not, the editing process may take longer than translating from scratch.
Output quality of the engine
Some engines are more advanced than others. A weak MT engine can result in messy drafts that require extensive corrections.
In other words, MT + post-editing is not automatically faster—it depends on context, content, and who’s doing the editing.
What are the accuracy limitations of machine-generated content?
While MT has improved, it still faces major challenges:
Terminology inconsistency
MT may translate the same term in multiple ways within the same document. This can confuse readers and damage brand credibility, especially in medical, legal, or technical contexts.
Lack of contextual understanding
Machines struggle with idioms, metaphors, tone, and cultural nuance. For example, marketing slogans or humor may be translated literally or incorrectly.
Structural issues
Sentence structure may be awkward, ambiguous, or grammatically incorrect—especially in long or complex sentences.
Factual errors
MT may misconstrue or misinterpret context, such as confusing “interest” as a financial term or emotional state.
The post-editor is responsible for fixing all errors. When the output is poor, the time and effort required to amend it can be substantial.
How does quality impact the cost of post-editing?
The cleaner the MT output, the less time it takes to fix—and the lower the post-editing cost. But that’s not always what happens in practice:
Low-quality MT = higher editing costs
If the machine makes too many mistakes, editors spend more time revising than they would translating from scratch—especially if the sentence structure needs to be rebuilt.
High-volume + tight deadlines = risk of missed errors
Under pressure, editors may overlook subtle errors that compromise the integrity of the translation. These can later lead to brand, legal, or compliance issues.
Invisible costs
Even when the edited translation is readable, subtle flaws in tone, terminology, or cultural relevance can weaken impact or cause misunderstandings—leading to rework or lost trust.
Quality Assurance (QA)
MT + post-editing often requires an additional QA round, especially when targeting regulated industries. This adds time and cost that may not be factored into initial estimates.
In short: faster doesn’t always mean cheaper—especially if quality matters.
When does MT + post-editing make sense—and when doesn’t it?
MT + PE works well when:
- The content is low-risk, such as internal communications or bulk product listings.
- Deadlines are tight and take precedence over quality.
- The language pair is well-supported by strong MT engines.
- The post-editors are trained professionals familiar with MT output.
- The goal is to understand rather than publish the content.
MT + PE is a poor fit when:
- The material is high-risk (legal, medical, technical specifications).
- Brand tone or creativity is essential (marketing, public-facing websites).
- Cultural sensitivity is required.
- The target audience expects flawless, native-level communication.
- The cost of errors is high—legally, financially, or reputationally.
Organizations need to weigh the cost of mistakes against the value of speed.
MT + post-editing offers a promising middle ground between speed and quality—but it’s not a magic solution. When done strategically, it can boost efficiency without sacrificing accuracy. But if used carelessly, it can lead to poor output, higher costs, and reputational risk. Understanding the strengths and limits of this approach is key to using it wisely—and balancing speed with quality in a way that truly serves your goals.
References:
- ISO/IEC 18587:2017 – Post-editing of Machine Translation Output
This international standard outlines the requirements for full and light post-editing of machine-translated content by professional linguists.
https://www.iso.org/standard/62970.html
- Common Sense Advisory / CSA Research
Market research and cost-benefit analyses of machine translation and post-editing across industries (paid access).
https://csa-research.com/
- American Translators Association (ATA)
Educational resources on the ethics, quality implications, and limitations of relying solely on MT.
https://www.atanet.org
