Google launches Latin translation tool
Google Translate already handled more than 50 languages, including small ones such as Welsh and Haitian Creole. Then it added Latin, and announced the fact in a blog post written entirely in Latin, which is the kind of joke an engineering team makes exactly once.
Jakob Uszkoreit, a senior engineer at Google, used the post to make a claim that irritated some classicists and delighted others: Latin, he argued, is far from a dead language. The evidence he had in mind was practical rather than sentimental. Latin is still read, still taught, still written in certain corners, and still generates enough demand that a translation engine is worth building for it.
Why Latin is unusually good for a machine
Google expected translations to and from Latin to be among the most accurate the service offered, which sounds like a boast until you look at how statistical translation actually works.
An engine of that era learned by aligning enormous quantities of text that already existed in two languages. It did not understand grammar. It counted. Give it millions of sentence pairs and it learns which strings tend to correspond to which other strings, and how words are ordered on each side.
Latin is peculiarly well supplied for this. Almost everything ever written in it has been translated, often several times, and the translations are careful, because they were made by scholars for other scholars. There is no slang problem, no drift, no argument about what the current usage is, because there is no current usage. The corpus is closed and it is clean. Compare that with a living language, where the training data is full of forum posts, typos, sarcasm and words invented last Tuesday.
Where the machine still breaks
The trouble is that Latin is also structurally hostile to the way these systems work. It is heavily inflected, which means the job done in English by word order is done in Latin by endings. The subject, object and possessor of a sentence can appear in almost any sequence, and the reader works out who did what to whom from the case of each noun.
Poets exploited that freedom mercilessly. A line of Virgil can scatter an adjective four words away from the noun it modifies, and the reader is expected to hold both in mind until the sense closes. Statistical machine translation, which learned its habits from word order, has no reliable way to handle that, and the results on classical poetry ranged from wooden to comic.
- Prose does better than verse, and Caesar does better than Cicero
- Ecclesiastical and legal Latin, being formulaic, does best of all
- Ambiguous cases, where the same ending serves two functions, are where errors cluster
- Anything with rhetorical inversion tends to be flattened into nonsense
The text-to-speech question
Google also added a Latin text-to-speech function to help with pronunciation, which promptly reopened a fight that has run for a century. There is no agreement on how Latin sounded. The restored classical pronunciation, reconstructed from Roman grammarians and from how Latin words were transcribed into Greek, gives a hard c in Caesar and a w sound for v. The ecclesiastical pronunciation used in the Church gives Caesar a soft ch and turns v into a v.
Both are taught. Both are defended vigorously. A synthesised voice has to pick one, and whichever it picks, a substantial constituency will be annoyed. Any tool that speaks Latin aloud is making a scholarly argument whether it wants to or not.
What people actually use it for
Latin remains one of the more heavily searched translation pairs, and the traffic is not coming from professors. It comes from students facing a set text, from people choosing a motto for a crest or a tattoo, from novelists reaching for something that sounds ancient, and from anyone who has run into a legal phrase and wants to know what habeas corpus literally says.
The tattoo cases are where machine translation does the most damage, because the person requesting it cannot check the output. A Latin phrase that means something noble in English can come out, once the endings are wrong, meaning something closer to gibberish. Permanently.
What changed when the engines changed
The Latin tool launched in the statistical era, and the technology underneath it has since been replaced twice over. Neural translation, which arrived a few years later, learns representations of meaning rather than counting string pairs, and it handles free word order considerably better because it is not anchored to position in the same way. Large language models went further again, having read most of the surviving classical corpus and most of the commentary written about it.
The improvement is real and it is uneven. Modern engines will produce a fluent, plausible English paragraph from a Latin one, which is precisely the danger: fluent output feels correct. Classicists who test these systems report that the errors have not disappeared, they have become harder to spot, because the machine now writes confident English around the part it has misunderstood. A student who cannot read the Latin cannot see the seam.
Latin's afterlife
The language never quite stopped. The Vatican still issues documents in it and maintains a body that coins Latin terms for modern objects. Finnish public radio broadcast news bulletins in Latin for decades. There are Latin Wikipedia articles, Latin-speaking summer schools, and a small international community that conducts conversations in it for pleasure.
Uszkoreit's point, then, was defensible. A dead language is one nobody reads, and by that standard Latin is in better health than most of the languages actually disappearing each year. Adding it to a translation engine was partly marketing, but it was marketing aimed at a real audience.
The service's own history is documented at Google Translate, and the living use of the language is catalogued under Contemporary Latin. Students who want a translation checked by a human, rather than trusting an engine with something they intend to have inked onto their forearm, tend to end up at r/latin, which is generally the wiser route.