Machine translation (MT)
Machine translation – using it professionally.
If you’re managing large volumes of text, multiple languages and short release cycles, it’s not the first draft that counts, but the end result: terminologically consistent, technically accurate and ready for immediate use in your target language. This is precisely why we use machine translation as part of a controlled translation process – including a feasibility analysis, terminology integration and professional post-editing (oneMTPE).
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Making effective use of machine translation in business
Machine translation (MT) helps you process large volumes of text and multiple languages faster. To ensure the final text in your target language is accurate and ready for release, clear guidelines, suitable translation systems and human quality assurance are required.
MT and AI systems often generate very natural-sounding results. However, this is precisely what can be misleading if ambiguities in the source text are not clarified, terms are used inconsistently or technical details translated in a way that is merely "plausible" rather than accurate. That is why, in a corporate context, MT is not a "one-click" solution, but rather a translation method that must be embedded within the translation process in order to identify and systematically correct all errors.
It is particularly when translating into several target languages that the importance of context becomes clear. Ambiguities can be reliably resolved even in machine translation – through the use of terminology, clear guidelines and post-editing.
Whether MT is appropriate in your case depends primarily on four factors, and they determine which translation engines and translation tools are suitable for the process and how robust it will be:
- Source language and language pair:
Not every language pair produces the same quality of machine translation output. - Text type and structure:
Continuous text is usually more suitable than fragments taken out of context. - Terminology and guidelines:
Without glossaries and consistent terminology, the amount of post-editing required increases. - Risk profile:
The higher the risk, the more important it is to have a suitable QA set-up.
oneMTPE: Machine pre-translation plus post-editing
With oneMTPE, we combine speed and security: A machine translation system generates an initial translation output, after which qualified post-editors check the content, terminology and style. For you, this means fewer feedback loops, less risk and a result you can stand behind, both internally and externally.
Post-editing is not merely a cosmetic correction here. Linguists work with the source text, machine translation output and the target text and decide, segment by segment, what can be retained, what needs to be adapted and when a complete rewrite would be a better solution. So not only do humans remain "in the loop", they also make the decisions throughout the process to ensure the translation quality – particularly where machine systems reach their limits.
Evaluation and optimisation as a fixed step in the process
Every MTPE project is followed by a systematic evaluation: Our post-editors assess the machine translation output across defined categories; we analyse the changes by text type and language pair, and derive specific optimisation measures. Through regular check-ins, we’ll keep you informed about the savings you’re making by using MTPE, the quality of the machine translation output and opportunities for further process optimisation. For you, this means greater transparency, predictable quality and an MTPE process that becomes more stable with every project.
What sort of content is machine translation suitable for?
MTPE (machine translation + post-editing) is often particularly efficient for content that is structured and substantial, and which is used regularly in multiple languages:
- Technical documentation
- Software and IT documentation
- Website and product content
- Marketing texts that do not require transcreation
Short texts with little context, individual words and fragmented content containing many placeholders are only suitable in part or not very suitable. The accuracy of the source text and correct formatting also have an impact on the machine translation output. It is therefore crucial to assess the text type, language pair, requirements and risk profile – that is what the feasibility analysis is for.
Typical benefits for your teams
- Depending on the project, up to 50 percent faster processing times
- Significant cost savings in the double-digit percentage range are possible
- Consistent terminology through glossaries and translation memories
- A project-specific decision based on a feasibility analysis, to ensure post-editing never becomes more time-consuming than a human translation
Getting started: Feasibility analysis rather than experiment
Before you size up MTPE, we carry out a feasibility analysis to assess the text type, source language, language pair, risk and potential savings. You’ll receive a clear recommendation on whether MTPE is suitable for you and what your ideal translation process looks like.
Real-world reference: HELLA
Together with HELLA, we integrated oneMTPE into existing processes and then developed it. The result: significant time and cost savings (in the region of 30–40 percent) with professionally validated machine translation output.
It is particularly in large organisations that it quickly becomes apparent whether machine translation will work reliably as a translation service. The key here is to take a step-by-step approach based on measurable criteria, rather than a one-off roll-out of a tool.
FAQs
What is the difference between automated translation and machine translation?
There is no difference. The two terms refer to the same concept. In practice, however, it is not the term that matters, but how it is implemented: you can benefit from MT as a tool to speed up in-house work or as part of a controlled translation process – with terminology management, quality assurance and post-editing by linguists.
Man versus machine: can machine translation replace human translation?
Even though the output result sounds fluent and coherent, no machine translation system is error-free. The aim of MTPE is to combine the strengths of humans and machines: The speed comes from the MT, whilst the precision and specialist expertise come from humans. That's how we combine the best of both worlds to deliver high-quality translation output.
How does neural machine translation (NMT) work?
Neural systems have learnt language patterns through extensive training data and use this to generate translation output that often sound very natural. For you, this means quick rough drafts. But fluent does not automatically mean accurate, which is why we ensure the quality of the machine translation output through post-editing and QA.
Can I rely on the translation quality of machine translation?
Yes, provided you get the basics right: suitable translation systems, integrated terminology, use of existing translation memories, professional post-editing and quality assurance. Without these elements, there will still be gaps in quality and risks.
How can I make the most of machine translation?
You start with a feasibility analysis. This will give you a clear recommendation as to which content is suitable, which set-up makes sense, and how you can save time and money without losing control over terminology, style and data security.
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