One of the questions I am asked most often is whether AI or machine translation can replace the need for human translators. The answer is yes and no, and the extent to which you can rely less on people depends on the type of content involved.
The translation industry has been using machine translation for a long time, and indeed the technology is as old as the computing industry (see Systran). Today’s large language models are direct descendants of neural machine translation platforms that were introduced in the past decade. So people working in the translation industry are well acquainted with MT and its uses.
LLMs are only as good as the material used to train them, and that is the crux of the issue with machine translation as well. In order to train a translation engine, you need to have aligned texts with direct translations from one language to the other. This type of material is hard to come by, varies wildly in quality, and for some language pairs it isn’t available at all or is polluted by AI generated texts. Because of this, translation quality is variable depending on the type of content being translated and the languages involved. That said, machine translation is a lot better today than it was a few years ago.
<aside> 👉 Update: I put together this spreadsheet which allows users to forecast savings based on the mix of human and machine translation workflows they utilize. Feel free to play around with this.
Hybrid Workflow Cost Estimator Sheet.xlsx
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Machine translation is super useful when you need to obtain information in your language. For example, a Japanese user might use MT to translate a Help Center article. He or she won’t expect the translation to be perfect. As long as it is reasonably accurate, their questions will be answered even if the translation is awkward. The translation widget embedded in Google Chrome often does a good job with this.
However, if you are translating content to be published on your website, the user’s quality expectations will be higher. The problem isn’t that machine translation will make terrible errors, it’s that it can produce text that is awkward to read as well as get factual information wrong (which is important for things like instructions and help center material). This reflects poorly on your brand and in general is not a great user experience.
A good compromise is to leverage people to correct and fine tune machine translations. We used this type of workflow at Lyft and Notion, and were able to reduce unit costs while also delivering translations with speed. The real benefit of machine translation is speed, as it returns results in near real-time. What we did at both companies was implement a fix-forward workflow where machine translations were used as placeholders until people had time to review and edit them. These post-edits would then over-write the machine translations.
Modern translation platforms also continuously retrain the machine translation engines with the edits humans provide. This feedback loop allows machine translation quality to continuously improve over time. This works especially well when you have a large volume of content to translate.
For low visibility content, you can use a workflow called MTPE (machine translation + post-edit), where human reviewers approve or post-edit machine translations. This typically costs 10 cents per word, sometimes significantly less, depending on the languages involved. This is a good approach for things like your help center back catalog, transcripts, etc.
For high visibility content, you’ll probably also want language leads to take a second pass through the output of the MTPE process. They know your product and brand and are better able to capture your brand voice, jargon, etc.
For high impact content like marketing copy, signup flows, etc, you may find that you are better off not using MT at all. High end translators and copywriters often find that machine translations slow them down, and that it is quicker to write from scratch than to try to overhaul a machine translation.
The adage that you get what you pay for definitely applies here.
The leading TMS platforms make it easy to include any number of automated translation services in your translation workflows. More importantly they continuously measure the quality of these services and route translation requests to the best performing services.
This is important because it means you don’t have to be an expert in AI translation, and can rely on the platform to sense which services are doing well (and since this is a moving target, requests that might go to one provider could go to another next month).
These systems use a measure called edit distance as a proxy for quality. If human reviewers need to post edit the translations, that will ding that providers quality score. By doing this, the TMS has a view of how providers compare across languages and can automatically adjust the routing rules to favor the current winners. This becomes even more important if you need to operate in many languages because different service providers will typically do best in certain languages.
One area where we are seeing a lot of experimentation is AI based QA. Here the AI is asked to evaluate a translation and even to identify issues. The output from this step is then used to score the translation, and to flag translations that need human review. If nothing else, this type of process can be used to prioritize which translations human reviewers look at first (as well as to skip over obviously good translations). AI post-editing is also pretty good for cleaning up translations to fix grammar problems, mismatched gender, etc.