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For anyone interested in a correct translation, the Japanese reads:

"Its purpose is recidivism prevention and rehabilitation for youth offenders; the usual process leading up to a decision is: Notice or report of the fact of the youth crime to the family court > Investigation by the family court investigating officer (the "Investigator") and/or other parties > Judgment based on the results of the investigation > Protective measures or a disposition for rehabilitation, as necessary."

>Just to point out here... the text on the front page of Wikipedia tends to be very neutral, matter-of-fact, and well written

This is true in general, though perhaps not always of Japanese wikipedia, and certainly not on legal topics. I have no love lost with machine translation (hard agree on naniwaduni's comment about "insidious failure modes"), but there are very few humans who would be able to translate this particular excerpt effectively. Legal translation is my job and I still found this sentence to be reasonably challenging.

Yeah, Google Translate is hot garbage when it comes to Japanese. DeepL does quite a good job though!

> The purpose of this process is to deter and rehabilitate the juvenile delinquent, and the process of making a decision on the delinquency is usually conducted in the following manner: referral and notification of the fact of the delinquency to the family court; investigation by an investigator of the family court (hereinafter referred to as "investigator"); trial based on the results of the investigation; and, if necessary, a decision on protective measures or protective measures. There is.

Translated with www.DeepL.com/Translator (free version)

Right from the start, DeepL manages to drop information, making it seem as if it's describing a program that deters juvenile delinquency.

DeepL is doing a better job of generating text that seems to make sense, but this just helps to more effectively communicate the wrong meaning, and in a manner less detectable to people who would use it. This is a much more insidious failure mode.

> DeepL is doing a better job of generating text that seems to make sense, but this just helps to more effectively communicate the wrong meaning, and in a manner less detectable to people who would use it. This is a much more insidious failure mode.

This is something I don't understand about the use of autocorrect. Autocorrect takes misspelled words, which are essentially always easy to understand, and replaces them with random other words. It's frequently impossible to guess what original word got mangled by autocorrect into appearing in a suddenly unintelligible sentence. This is a problem that misspellings just don't have.

Autocorrect is actively making communication worse. What's it supposed to be doing? Why is it there at all?

Fully automatic autocorrect is very different from optional (i.e. user prompted) autocorrect.

The first has your stated effect. The second does not.

User-prompted autocorrect isn't "auto" at all, it's spell-check.

As I type on my phone it auto-guesses the word. I can choose to pick the guessed word, and almost always do so. I call that a lot more than spell-check and yet no words that I did not actively choose sneak in.

You aren't using autocorrect. You're using predictive input.

DeepL.com's JPN->ENG is better than Google Translate most of times, but still far from usable.

Also, it has "bugs" that sometimes it will just drop an entire sentence or half-sentence for no reason.

That's an even worse job than Google Translate did.

Admittedly I am usually translating English and east Asian languages, like this example, but I've basically given up on Google Translate (and other machine translation) at this point. It's worse than useless, because it often produces a translation that reads like it could be correct, but has the meaning reversed or otherwise garbled.

Uhhh.. I just translated the exact same sentence from wikipedia in Google translate and got:

"The juvenile protection procedure aims to deter and rehabilitate juvenile delinquents, and as a process up to the decision, "send and notify the facts of delinquency to the family court-family court investigator (hereinafter abbreviated as" investigator "). It is customary to go through the flow of "investigation by, etc.-a referee based on the investigation results-determine protective measures or protective measures as necessary"."

Not incredible (especially when it starts listing the process), but not as god awful as whatever you got. I'm curious if you omitted some part of the sentence when pasting in.

It's also worth noting that this is a pretty out-of-domain legal Japanese sentence.

One issue with Japanese specifically is that the subject is often omitted from the sentence and inferred from context. It's less common to see the equivalent pronouns of "I" or "they" in Japanese, but it's required a lot more in English. A machine translation would have to determine what pronoun to use when there isn't one in the original sentence to go off of, which is hard.

Then there are other things besides pronouns that require inference. For example, adding な to the end of a sentence could mean either a modifier expressing admiration or confirmation, or it could be indicate a demand or order not to do something. In machine translation it almost always seems to be translated to "don't" no matter the context. That only makes the translation less accurate.

I get the feeling that pronouns are also not explicit in Chinese most of the time, making it as hard to properly translate in english.

And I’d assume that could be the most common language people would want to translate from online platforms in this day and age.

The most common language people would want to translate from online platforms is most likely English, given it's ubiquitous use on the internet and the fact that there are more people who don't speak English than people who do.

Of course the details of how English is turned into incomprehensible garbage by machine translation always depend on the target language in question.

It's not nearly as bad compared to Japanese. And it pretty much only happens in conversations.

How are models trained for this, maybe Ja->En->Ja and see how the the result differs from the input?