Mirela
Everyone’s talking about machine translation post-editing, but just as important is machine translation pre-editing. Despite the advances in neural machine translation and large language models—which we can’t deny, the quality of machine-generated translations still depends on the quality of the source text. And that’s what we’re going to discuss in today’s article.
What is machine translation pre-editing?
Machine translation pre-editing is the revision of source-language content before machine translation. The process involves adapting a text to the strengths and limitations of machine translation systems by improving linguistic clarity and standardizing language use. Its main purpose is to prevent translation problems before they occur.
The primary objectives of machine translation pre-editing:
- Improving translation accuracy.
- Reducing ambiguity in the source text.
- Standardizing terminology.
- Enhancing consistency across documents.
- Lowering post-editing costs and effort.
- Increasing efficiency in multilingual content production.
The two types of pre-editing
Machine translation pre-editing can be done either manually by human editors or automatically through software. Human pre-editing is performed by writers, editors, or translators who revise source texts before translation. It’s more time-consuming and can get expensive when working on large-scale projects, but there’s no doubt about it—you get better results.
Automatic pre-editing relies on software tools and natural language processing technologies to modify texts before translation. The advantage here is that you can can process large volumes of text rapidly and consistently, so it would be more suitable for companies that regularly produce multilingual content on a large scale.
The disadvantage of automatic pre-editing is that you can’t rely on it for sensitive content. Human pre-editing remains valuable in specialized fields like technical communication, medicine, law, and engineering, where inaccuracies can lead to serious consequences. In these contexts, editors work according to predefined style guides, terminology databases, and controlled-language rules to ensure consistency.
Many companies combine the two for better results. For example, automated tools can perform initial revisions and identify potential issues, and then human editors come in to review and refine the results. With future advances in artificial intelligence, we can only assume the automatic pre-editing tools will improve.
Pre-editing techniques
| Technique | Description | Purpose |
|---|---|---|
| Simplifying sentence structure | To rewrite long or syntactically complex sentences as shorter, more direct statements. | Reduces the likelihood of syntactic errors and improves machine processing. |
| Eliminating ambiguity | To replace words or expressions that have multiple possible interpretations with clearer alternatives. | Helps machine translation systems select the intended meaning. |
| Standardizing terminology | To use the same approved term consistently throughout a text or document set. | Improves terminological consistency and reduces variation in translation output. |
| Removing idiomatic language | To replace idioms, metaphors, and culturally specific expressions with literal language. | Prevents mistranslation of figurative or culture-bound expressions. |
| Expanding acronyms and abbreviations | To define abbreviations or write them out in full, particularly when they are domain-specific. | Ensures that machine translation systems correctly interpret specialized terminology. |
| Correcting language errors | To eliminate grammatical, spelling, and punctuation errors in the source text. | Improves source-text quality and supports more accurate translation output. |
Here are a few examples:
❌ “The device, which may be configured by authorized personnel under specific operating conditions, should be restarted if an error occurs.”
✅ “Authorized personnel can configure the device under specific operating conditions. Restart the device if an error occurs.”
❌ “Press the right button.”
✅ “Press the button on the right side.”
❌ “customer”, “client”, and “account holder” are used interchangeably.
✅ “customer” is used consistently throughout the document.
❌ “We’re in the home stretch.”
✅ “We’re almost finished.”
❌ “Update the CRM and send it to PM.”
✅ “Update the Customer Relationship Management (CRM) system and send it to the Project Manager (PM).”
❌ “User click save then system process request.”
✅ “The user clicks Save. Then the system processes the request.”
Bottom line
Machine translation pre-editing helps machine translation systems generate more accurate, consistent, and reliable outputs, which means less work required during post-editing. Content like technical communication, legal documentation, healthcare, and regulatory content benefits the most from this process, because this is where accuracy, consistency, and terminological precision are essential.