Google Translate “Get well [Swedish firstname]” translates to “fuck you”(translate.google.se)
translate.google.se
Google Translate “Get well [Swedish firstname]” translates to “fuck you”
https://translate.google.se/?sl=auto&tl=en&text=krya%20p%C3%A5%20dig%20Bj%C3%B6rn%0A&op=translate
57 comments
DeepL gets every translation I try consistently better, at such a better rate that I don't even know why people use Google Translate anymore.
ChatGPT beats DeepL for me as well (especially GPT-4), although I like DeepL's UX better for now where it quickly shows you related words which helps me better understand the meaning.
Very impressed with GPT-4 translation though -- especially the ability to steer it between "transliteration", "keep the meaning", "keep the tone", "use local idioms where appropriate", "explain different possible meanings/intentions", etc.
Very impressed with GPT-4 translation though -- especially the ability to steer it between "transliteration", "keep the meaning", "keep the tone", "use local idioms where appropriate", "explain different possible meanings/intentions", etc.
> ChatGPT beats DeepL for me as well
I've tried it when I had the time to compare (DeepL vs GPT4), and find them to be pretty equal.
But DeepL easily win on speed. 5 paragraphs would take just some seconds with DeepL and be almost 100% correct, while GPT4 would take almost a minute (sometimes more) while being about the same amount of correct.
> Very impressed with GPT-4 translation though -- especially the ability to steer it between "transliteration", "keep the meaning", "keep the tone", "use local idioms where appropriate", "explain different possible meanings/intentions", etc.
I've found that DeepL already does this well even thought it's not a LLM (as far as I know).
I've tried it when I had the time to compare (DeepL vs GPT4), and find them to be pretty equal.
But DeepL easily win on speed. 5 paragraphs would take just some seconds with DeepL and be almost 100% correct, while GPT4 would take almost a minute (sometimes more) while being about the same amount of correct.
> Very impressed with GPT-4 translation though -- especially the ability to steer it between "transliteration", "keep the meaning", "keep the tone", "use local idioms where appropriate", "explain different possible meanings/intentions", etc.
I've found that DeepL already does this well even thought it's not a LLM (as far as I know).
> I've found that DeepL already does this well
Yes. DeepL is very good! But with ChatGPT I can "tune" it more towards one way, whereas DeepL just only does whatever it does. DeepL has very very sensible defaults and the UI is great. But in fairness, DeepL basically will never just insert an appropriate idiom. Also GPT-3.5 is still worth comparing to DeepL as well.
Yes. DeepL is very good! But with ChatGPT I can "tune" it more towards one way, whereas DeepL just only does whatever it does. DeepL has very very sensible defaults and the UI is great. But in fairness, DeepL basically will never just insert an appropriate idiom. Also GPT-3.5 is still worth comparing to DeepL as well.
I've also found that DeepL's consistently better or equal when it applies for my personal usage. Some users will care that DeepL doesn't support as many languages, doesn't have TTS, and doesn't offer transliterations (such as pinyin for Chinese).
But if you play with the name, the translation is different:
- krya på dig Helga - take care of yourself Helga
- krya på dig Dave - screw you dave
- krya på dig Mary - come on Mary
- krya på dig Linnéa - brace yourself Linnéa
- krya på dig Mohammad - fuck you Mohammad
- krya på dig Helga - take care of yourself Helga
- krya på dig Dave - screw you dave
- krya på dig Mary - come on Mary
- krya på dig Linnéa - brace yourself Linnéa
- krya på dig Mohammad - fuck you Mohammad
Its aggression is related to animals somehow.
- krya på dig katt (cat) - fuck you cat
Björn is also bear in swedish.
- krya på dig katt (cat) - fuck you cat
Björn is also bear in swedish.
There's a difference between Björn (the name) and björn (the animal).
Capitalization gives additional context in this case, if it were in the beginning of the sentence though, then one would hope it contains other clues as well
Capitalization gives additional context in this case, if it were in the beginning of the sentence though, then one would hope it contains other clues as well
- krya på dig Robert - get over it Robert
It’s a strange little circle of anomalies
It’s a strange little circle of anomalies
This is fun
krya på dig Cat - screw you Cat.
krya på dig balloon - get on you balloon.
krya på dig tacos - grab some tacos.
krya på dig applesauce - put on some applesauce.
krya på dig Cat - screw you Cat.
krya på dig balloon - get on you balloon.
krya på dig tacos - grab some tacos.
krya på dig applesauce - put on some applesauce.
i was kind of expecting Ingrid to spell out the original fyi acronym but Google gets even that wrong:
krya på dig Ingrid - get over it Ingrid
krya på dig Ingrid - get over it Ingrid
If you capitalise the first word (a signal to the algorithm to be more sentence-based than word-based, perhaps?)
- Krya på dig Björn - Get over it Björn
- Krya på dig Helga - Get over it Helga
- Krya på dig Dave - Get over it Dave
- Krya på dig Mary - Get over it Mary
- Krya på dig Linnéa - Come on Linnéa
- Krya på dig Mohammad - Get over it Mohammad
- Krya på dig Björn - Get over it Björn
- Krya på dig Helga - Get over it Helga
- Krya på dig Dave - Get over it Dave
- Krya på dig Mary - Get over it Mary
- Krya på dig Linnéa - Come on Linnéa
- Krya på dig Mohammad - Get over it Mohammad
[deleted]
Seems to me like it's ignoring context and indirectly applying literal synonyms in the wrong direction. That is, if screwing->fucking (sorry, it's necessary to make the point) but not the other way around except in one context and screwing can be synonym for tighten something in another context, then bracing->tighten and tighten->screwing, it could have "walked backwards" doing something like brace->tighten->screw->fuck, where the last synonym is not valid in the context where the first synonym could be.
Sorry about language and the poor description; I seem to have an idea of the problem, but it's not my field and have no way of describing it in formal way.
Sorry about language and the poor description; I seem to have an idea of the problem, but it's not my field and have no way of describing it in formal way.
My First Last name translates to Faithful Negro whenever I do translations from Any -> English. I always find it funny when dealing with bureaucracy.
I do not know your language, but at least in Polish the translation makes some sense.
They have the word "czarny" which means "black". "czarny kot" is "black cat", "on jest czarny" is "he is black". The latter is purely descriptive.
There is also the word "czarnuch" which is the VERY offensive word for Blacks, best translated by "negro".
Now, the last name "Czarnuch" is a normal last name, without any connotations to the color black (except probably in its etymology) and does not sound weird/offensive.
The translation of this capitalized word would naturally yield "Negro".
They have the word "czarny" which means "black". "czarny kot" is "black cat", "on jest czarny" is "he is black". The latter is purely descriptive.
There is also the word "czarnuch" which is the VERY offensive word for Blacks, best translated by "negro".
Now, the last name "Czarnuch" is a normal last name, without any connotations to the color black (except probably in its etymology) and does not sound weird/offensive.
The translation of this capitalized word would naturally yield "Negro".
Crnjak (tsr̩̂ɲak) has usages in Croatian language, one of them is "black" wine, the other is black joke.
There might be a reference somewhere where it is used as a description of a person, but probably not in Croatian.
I did notice Google Translate hallucinates words, some very amusing, when I translate from Any -> Croatian (this happens automatically when reading Google Maps place reviews). There has been quite a lot of words that naturally map to Croatian but there's no text (outside of blogspam) that uses it on the Internet.
There might be a reference somewhere where it is used as a description of a person, but probably not in Croatian.
I did notice Google Translate hallucinates words, some very amusing, when I translate from Any -> Croatian (this happens automatically when reading Google Maps place reviews). There has been quite a lot of words that naturally map to Croatian but there's no text (outside of blogspam) that uses it on the Internet.
Constance Black?
All you have to do is enter the username into Google Translate and it will make sense.
Look at the username
Honestly, that looks like a random string of letters strung together to me. If it is a name, I don’t recognize the provenance.
the "crnj" part might remind you of Chernobyl
What percent of HN readers know a Slavic language? It is true, though, in Czech at least his user name is Faithful Black
Man that cracked me up.
Why you should never use any translation automation blindly, reason #6153671...
Deepl seems to have no trouble with this: https://www.deepl.com/translator-mobile#sv/en/krya%20på%20di...
End of life planning in Japanese translates to suicide
https://www-eranda-jp.translate.goog/column/24550?_x_tr_sl=j...
https://www-eranda-jp.translate.goog/column/24550?_x_tr_sl=j...
Well, you are joking, but there is research [1] that concluded that some ailments, such as depression, can be cured by, well...
[1] https://www.albany.edu/news/releases/2002/june2002/gallupstu...
[1] https://www.albany.edu/news/releases/2002/june2002/gallupstu...
I think you are lost.
I'm trying to be helpful?
I'm just saying this comment doesn't seem to be related to OP.
https://www.reddit.com/r/lostredditors
I'm just saying this comment doesn't seem to be related to OP.
https://www.reddit.com/r/lostredditors
It was actually on-topic. I guess not many people got the joke, or maybe some found it offensive (was it?) but it was actually relevant to the topic, if only a bit tangentially.
Can you explain? I was actually about to send the link to my girl.
So no I was not offended, lol.
So no I was not offended, lol.
Google incorrectly translates "get well soon" as "eff you", but "effing" can actually heal some health problems, so it may as well mean "get well soon".
This must be the start of the singularity
Clearly a case of tranlating of the meaning instead of being literal.
”As a language model, fuck you Tony”
as well as "va te faire foutre Bjorn" in French, "vete a la mierda Bjorn" in Spanish.
"Foda-se Björn" in Portuguese, which is, interestingly, a bit different in meaning. For the lack of a better description, it's closer to disappointment than anger. "Well, fuck me, Bjorn " rather than "Fuck you, Bjorn" (which would've been "vai-te foder, Björn"), that sort of thing.
Italian too.
tangentially i’m still sad about the loss of the easter egg one used to obtain when attempting to translate “Wenn ist das Nunstück git und Slotermeyer? Ja! Beiherhund das Oder die Flipperwaldt gersput!” into english.
Few today remember Ernest Scribbler.
Maybe it is implied that you ll get well first
Fun fact about how this can bite you in the ass:
1) Make an android app and publish it
2) Write "Get well [firstname]" in sweden locale
3) Enjoy your ban because google uses google translate to look for inappropriate language in app descriptions
1) Make an android app and publish it
2) Write "Get well [firstname]" in sweden locale
3) Enjoy your ban because google uses google translate to look for inappropriate language in app descriptions
You get different translations depending on the name. Some variations I see "Screw you", "Get over it", "Brace yourself"
Between 2003 and 2005, Google translate was translating:
شعب يباد = (people are being exterminated)
To:
Iraqi People
شعب يباد = (people are being exterminated)
To:
Iraqi People
And of course ChatGPT 4.0 gets it exactly right.
What's wrong at Google lately?
What's wrong at Google lately?
Google. I feel like they were great for a while but they fell off at some point and just started copying everyone and failing at doing anything better. So yeah, I think the answer to "What's wrong at Google?" is "Google"
french trick
input 'baiser' -> Google translates to 'kiss'
now add a female first name after 'baiser', 'kiss' will become 'fuck'
input 'baiser' -> Google translates to 'kiss'
now add a female first name after 'baiser', 'kiss' will become 'fuck'
Without context, that's what it means in French though...
Can a Swedish speaker say if something similar is going here?
Can a Swedish speaker say if something similar is going here?
It works for male first names too. And last names too.
This reminds me of the recent so-called "Glitch Token" phenomenon[1]. When GPT-3 was presented with reserved tokens it never encountered during training, it reacted in extremely unpredictable ways -- often with a simple "fuck you".
For those unfamiliar with LLM architecture: "tokens" are the smallest unit of lexical information available to the model. Common words often have their own token (e.g.: Every word in the phrase "The quick brown fox jumped over the lazy dog" has a dedicated token), but this is a coincidence of compression and not how the model understands language (e.g.: GPT-3 understands "defenestration" even though it's composed of 4 apparently unrelated tokens: "def", "en", "est", "ration").
The actual mechanism of understanding is in learned associations between tokens. In other words: the model understands the meaning of "def","en","est","ration" because it learns through training that this cluster of tokens has something to do with the literary concept of violently removing a human via window. When a model encounters unexpected arrangements of tokens ("en","ration","est","def"), it behaves much like a human might: it infers the meaning through context or otherwise voices confusion (e.g.: "I'm sorry, what's 'enrationestdef'?"). This is distinctly different from what the model does when it encounters a completely alien form of stimulation like the aforementioned "Glitch Tokens".
At the risk of anthropomorphizing, try imagining if you were having a conversation with a fellow human and they uttered the following sentence "Hey, did you catch the [MODEM NOISES]?". You've probably never before heard a human vocalize a 2400Hz tone during casual conversation -- much like GPT-3 has never before encountered the token "SolidGoldMagicarp". Not only is the stimulus unintelligble, it exists completely beyond the perceived realm of possible stimulus.
This is pretty analagous to what we'd call "undefined behavior" in more traditional programming. The model still has a strong preference for producing a convincingly human response, yet it doesn't have any pathways set up for categorizing the stimulus, so the model kind of just regurgitates a learned lowest-common-denominator response (insults are common).
This oddly aggressive stock response is interesting, because it's actually the exact same type of behavior that was coded into one of the first chatbots to (tenuously) pass a Turing test. I'm of course referring to the "MGonz" chatbot created in 1989[2]. The MGonz chatbot never truly engaged in conversation -- rather, it continuously piled on invective after invective whilst criticizing the human's intelligence and sex life. People seem predisposed to interpreting aggression as human, even when the underlying language is, at best, barely coherent.
[1]: https://www.youtube.com/watch?v=WO2X3oZEJOA [2]: https://timharford.com/2022/04/what-an-abusive-chatbot-teach...
For those unfamiliar with LLM architecture: "tokens" are the smallest unit of lexical information available to the model. Common words often have their own token (e.g.: Every word in the phrase "The quick brown fox jumped over the lazy dog" has a dedicated token), but this is a coincidence of compression and not how the model understands language (e.g.: GPT-3 understands "defenestration" even though it's composed of 4 apparently unrelated tokens: "def", "en", "est", "ration").
The actual mechanism of understanding is in learned associations between tokens. In other words: the model understands the meaning of "def","en","est","ration" because it learns through training that this cluster of tokens has something to do with the literary concept of violently removing a human via window. When a model encounters unexpected arrangements of tokens ("en","ration","est","def"), it behaves much like a human might: it infers the meaning through context or otherwise voices confusion (e.g.: "I'm sorry, what's 'enrationestdef'?"). This is distinctly different from what the model does when it encounters a completely alien form of stimulation like the aforementioned "Glitch Tokens".
At the risk of anthropomorphizing, try imagining if you were having a conversation with a fellow human and they uttered the following sentence "Hey, did you catch the [MODEM NOISES]?". You've probably never before heard a human vocalize a 2400Hz tone during casual conversation -- much like GPT-3 has never before encountered the token "SolidGoldMagicarp". Not only is the stimulus unintelligble, it exists completely beyond the perceived realm of possible stimulus.
This is pretty analagous to what we'd call "undefined behavior" in more traditional programming. The model still has a strong preference for producing a convincingly human response, yet it doesn't have any pathways set up for categorizing the stimulus, so the model kind of just regurgitates a learned lowest-common-denominator response (insults are common).
This oddly aggressive stock response is interesting, because it's actually the exact same type of behavior that was coded into one of the first chatbots to (tenuously) pass a Turing test. I'm of course referring to the "MGonz" chatbot created in 1989[2]. The MGonz chatbot never truly engaged in conversation -- rather, it continuously piled on invective after invective whilst criticizing the human's intelligence and sex life. People seem predisposed to interpreting aggression as human, even when the underlying language is, at best, barely coherent.
[1]: https://www.youtube.com/watch?v=WO2X3oZEJOA [2]: https://timharford.com/2022/04/what-an-abusive-chatbot-teach...
I don't know much about Swedish, but I am learning Korean, and Google Translate is dangerous in a much more subtle way with Korean. In particular, in Korean you conjugate verbs (and often, choose different nouns) based on the relative age and social standing of the speaker and listener. In Korean specific translation tools (e.g. Naver) there is a toggle to select whether to use "honorifics" or not. But, Google tends to default to the form of speech (banmal) reserved for talking to young children or close friends. But, if I am using a translation tool, I probably don't know the person I am conversing with very well. As a result the translations tend to come off as very rude.
If I used Google Translate to talk to a shopkeeper, it would be roughly equivalent to saying "Hey, little buddy, how much for this?" as opposed to "Excuse me sir, what is the cost of this item?"
And this is all without considering all the weird mistranslations you can get because Korean is much more heavily context dependent than English. Korean speakers often leave out the subject or object if it can be understood from context (context that the translation tools are likely missing). So Google translate will insert pronouns (it, him, her...) to make the English flow better, but are not based on anything in the original Korean. So, if it guesses wrong, you could imagine the level of confusion that could ensue.
And then all the homonyms in Korean combined with the heavy context dependence makes for some weird translation. I once tried checking my Korean homework with Google translate, and before I knew it, I was drinking a car.
If I used Google Translate to talk to a shopkeeper, it would be roughly equivalent to saying "Hey, little buddy, how much for this?" as opposed to "Excuse me sir, what is the cost of this item?"
And this is all without considering all the weird mistranslations you can get because Korean is much more heavily context dependent than English. Korean speakers often leave out the subject or object if it can be understood from context (context that the translation tools are likely missing). So Google translate will insert pronouns (it, him, her...) to make the English flow better, but are not based on anything in the original Korean. So, if it guesses wrong, you could imagine the level of confusion that could ensue.
And then all the homonyms in Korean combined with the heavy context dependence makes for some weird translation. I once tried checking my Korean homework with Google translate, and before I knew it, I was drinking a car.
Deepl gets it right btw:
https://www.deepl.com/translator-mobile#sv/en/krya%20på%20di...