What most people managing translations don’t get is that they are essentially using the tools that translators use, but skipping the value adding step.

I’ve been doing translation as a side gig for years. Lately I’ve been doing some translations for an NGO that deals with addiction management, of which I’m part.

The materials have a lot of nuances, and need the translator to understand them, to properly convey the concepts.

The usual process for translation is to feed the original to a machine language translation software, and then work with both versions side by side, in a translation management software, tools that make editing and proofing faster and easier by a human, to achieve the best result.

Last time, someone in the organization, mono lingual, decided to do a handbook translation with ChatGPT, or something like that. They then gave the result to a colleague and me.

The resulting translation was exactly what we expected.

A problem was that some bilingual people were shown the results, and reported that the results were amazing, without realizing that they were commenting on the wow factor, not on the accuracy of the result, especially because they had not done a critical side by side comparison.

My colleague and I did the editing work, were paid less, but the end result was the usual translation quality.

The commissioning person at the org boasted that AI translation was great, obviating our work, to get their brownie points.

TLDR: translation has used machine translation as a first step for a long time, with results edited and polished by humans. Ignorant decision makers are skipping that crucial step, getting sub-par results, oblivious to the fact.

My company thankfully still employs simultaneous interpreters for meetings and has one translator on staff. I think, at least in part, because of how bad translation tools can be from EN <> JA.

I know someone who was a translator between two (less widely spoken) languages, and some specifics I recall from our conversations about work:

• Sometimes the translations use many technical terms, and getting those wrong (trusting LLMs) is not an option. (This was for some patents IIRC)
• Some terms simply do not exist in another language, and it could be up to the translator to invent a term to define and carry the information across. (This was for some government digital service, and the term was similar to “digital queue”)
• Tone and nuances are very difficult to translate. Phrasing can have implications and connotations. (Simplest example: “i am afraid” does not imply fear, it’s an established politeness phrase) Neutral in one language could be viewed as hostile in another, too. (And with politicians being petty, could have consequences)

None of those would be addressed with LLMs. Small training set for language (and language being similar to a few others) is an issue. Anything technical or non-existing would be prone to hallucinations. And tone is difficult enough to convey through text to begin with, let alone with LLM translation.

I believe this will pass. Sooner or later, the AI companies will have to stop losing money and adjust their pricing. And then it’ll turn out that using AI for everything gets you worse results than humans, at the same cost. And that will be that. I hope I can hang on until then.

such hope, i wish i had

The world would be soo easy if there was 1 lang. No more translations no more burning money water electricity

I’d expect any translation requiring zero mistakes and translator’s official responsibility wouldn’t be hurt by this.

It‘s terrible and sad. Even more so because AI still gets things wrong all the time.