February 1, 2025 / by Margarita Núñez Estimated read time: 5 minutes
The decision of whether to perform post-editing after documents have undergone AI translation frequently arises.
The emergence of AI translation has undoubtedly simplified your translation projects and requirements. This technology provides fast and cost-effective translations for the documents or files involved in your projects.
Let's delve into the necessity of post-editing the raw output from AI translation.
What is Artificial Intelligence Language Translation?
Advanced artificial intelligence (AI) translation is the process of using artificial intelligence (AI) engines to automatically translate content from one language to another, without human input.
What Is Post-Editing?
Post-editing is the correction of AI-translated text to ensure that it meets the "agreed level" of publishable quality requested by the client. Post-editing is not the same as editing. Editing is the process of correcting human-generated text.
In localization, we also talk about ‘light post-editing’ which means using human linguists to lightly post-edit the AI output, simply to make it understandable. On the other hand, a ‘full post-edit’ means that linguists go the extra mile to make it not only understandable by the end user but also stylistically and industry appropriate.
AI-Generated Content
The translated text typically has a combination of two things. Segments from an existing translation memory (TM) that have been leveraged against the source (if you are lucky enough to have a previous TM for that language combination) and the raw translated text that comes from the AI system itself.
The resulting translated content is a combination of both, a very literal translation of the documents from AI system mixed up with correct translations from a TM system. This means that local colloquialisms, common idioms, and some domain-specific terminology can get lost! This necessitates the need for post-editing of the raw output by native post-editors to achieve a publishable quality.
You Betcha
Imagine you need to convey expressions such as ‘youbetcha’ or ‘how's it going?’ as part of your project. These terms will most likely not be applied correctly, or if they are, the other words around them will be in the wrong place within the segment. This is because raw output may not interpret the meaning behind the expression and give a direct and very raw translation that misses the sentiment; the subtext, if you will, of the source segment.
This is where the need for post-editing using human linguists arises. There is no amount of rules and DNT (do not translate) glossaries that will fix certain issues. The use of human post-editors allows you to correct any mistranslations and correct the overall message by ensuring the subtext of the message is accurate and within the local lexicon and domain you are translating into.
This step often requires the use of post-editors that have the language skills, the subject matter expertise, and the product knowledge so when you use nuances and colloquial terms in the source language, they don't get lost in translation.
Two for the Price of One
Post-editing can also be useful for the training of AI systems. For instance, we can instruct post-editors to give structured feedback on common errors while post-editing a project. So the AI system can be improved over time with this type of feedback.
As obvious as it sounds, the need for a post-editing step after AI-generated translations should always be considered, especially if you desire human-translated quality content at the end of the process.
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This blog article was first published in 2016 and has been updated.
