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> My experience with the former is that the MT is far from perfect, and even frequently causes much amusement, but definitely useful. Oh fascinating, do you ha

> My experience with the former is that the MT is far from perfect, and even frequently causes much amusement, but definitely useful. Oh fascinating, do you have any idea of what things machine translation still consistently gets wrong?

_jahh 6y ago
not op but i find idiom or proper nouns can mess things up.
- Polylactic_acid 6y ago
  Another one is when one language uses a single word for two meanings which the other language has split. An example I cam across is when I translated the word "amber" meaning the orange color and the translation came out the other end as a word that purely means "fossilized sap"
- 082349872349872 6y ago
  I generally learn proper nouns first (following news is my biggest use of MT) and I've found it often helps to replace them by placeholders such as X and Y when translating. As to sibling comment, IIRC Yandex translate has a nice feature where they'll provide a number of meanings for words when clicked upon in the source text. "Invisible, Insane"
klodolph 6y ago
Oh, so much. Machine translation doesn't understand context and routinely fails at fairly basic translation tasks. Here's part of the Google translation of the front page of ja.wikipedia.org into English: > 非行少年の再非行の抑止や更生を目的としており、決定までの過程として、「非行事実を家庭裁判所に送致・通告 - 家庭裁判所調査官（以下「調査官」と略称する）等による調査 - 調査結果をふまえた審判 - 必要に応じて保護的措置あるいは保護処分を決定」という流れを経るのが通例である。 > And for the purpose of deterrence and rehabilitation of re-delinquency of juvenile delinquents, as the process of until a decision, " the fact delinquency to the family court -up Reel-notice - family court investigators (referred to hereinafter as" investigators "), or the like by the survey - survey It is customary to go through the flow of " judgment based on the result- decide protective measures or protective measures as necessary ". That's barely recognizable as English. The translation is hot garbage. Perhaps something you might use if you had an emergency. Just to point out here... the text on the front page of Wikipedia tends to be very neutral, matter-of-fact, and well written. There are no characters (like in novels), no use of slang, and nothing else that should be tricky for translation, and yet we get this garbage. It's a miracle that machine translation works at all, and when it does work, it is usually because you are translating between languages that are fairly similar to begin with. For example, English and Spanish.
- nonbirithm 6y ago
  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.
  - hrktb 6y ago
    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.
    - fireattack 6y ago
      It's not nearly as bad compared to Japanese. And it pretty much only happens in conversations.
    - yorwba 6y ago
      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.
- benrbray 6y ago
  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)
qayxc 6y ago
> do you have any idea of what things machine translation still consistently gets wrong? My biggest pet peeve with MT is the incredibly brain-dead choice of using English as an intermediate language. This effectively prevents MT from ever becoming useful for any A→B translation where neither A nor B are English. Example: put "пружи́на" into Google translate (or Bing translate, doesn't matter)and translate to German. You'll get "Frühling" as the suggested translation. This is due to the fact that internally "пружи́на" is translated to English first, resulting in "spring". This is then translated to German with the statistically highest probability being "Frühling", even though "Feder" would be the only correct match ("Frühling" would be "весна" in Russian). If you now say "But wait! This should clear itself up once you add some context, right?" you'd be wrong. Try "Die Sprungfeder kam gerade rechtzeitig." ("The coil spring arrived just in time,") and you'll get "Весна пришла как раз вовремя." ("Springtime came just in time"). It's ridiculous! EDIT: happens with DeepL as well; only Yandex gets it right (presumably because they actually use an actual German/Russian-model.
- dmurray 6y ago
  It's not brain-dead, it's a result of limited training data. For N languages you need to train N models if you go through English as an intermediate language, but N^2 if you don't use an intermediate language model. OK, computer time is cheap these days, but more importantly, for each language pair you need D high-quality documents translated in each of your two languages. There are far fewer pieces of writing that exist in both German and Russian than in both German and English or both Russian and English. There is cool-looking research on zero-shot language translation where the model learns its own internal "intermediate language". But you can be sure that if it produced reliably better results with the data available, the likes of Google would already be using it.
  - qayxc 6y ago
    > It's not brain-dead, it's a result of limited training data. That's quite literally a cheap excuse. It'snot lack of training data (remember, you still need translations in the target language anyway!), it's a result of Anglo-Saxon cultural hegemony. > There are far fewer pieces of writing that exist in both German and Russian than in both German and English or both Russian and English. Right. And that's why Russia-based Yandex gets excellent results for German/Russian translation, I see... > But you can be sure that if it produced reliably better results with the data available, the likes of Google would already be using it. Nah, you're being way too optimistic there. Google is a business and as such, they don't chase SOTA outside of research (which doubles as advertisement). Good enough is the name of the game and it's simply infinitely cheaper to just train and maintain N-models (i.e. English/X and X/English) than to do the same for all combinations of X/Y-pairs (which would be N² models). So as long as there is no incentive to do better, they simply won't, because they rely on the fact that English is the de-facto Lingua Franca and people translate to and from English more often than directly between other languages. This would be an actual chance for companies from other countries (especially European ones) to step in and do exactly that, but again - "good enough" and reliance on English stops that from happening anytime soon. It's a missed opportunity especially with low-resource languages as demonstrated by the recent "Scottish" Wikipedia fiasco...
Shorel 6y ago
> Oh fascinating, do you have any idea of what things machine translation still consistently gets wrong? Definitely anything where a word has two or more different meanings. Paper jam being my favourite example. https://translate.google.com/#view=home&op=translate&sl=en&tl=es&text=Paper%20jam https://translate.google.com/#view=home&op=translate&sl=en&t... It is translated as "(fruit) jam made out of paper"

_jahh 6y ago

not op but i find idiom or proper nouns can mess things up.

Polylactic_acid 6y ago
Another one is when one language uses a single word for two meanings which the other language has split. An example I cam across is when I translated the word "amber" meaning the orange color and the translation came out the other end as a word that purely means "fossilized sap"
082349872349872 6y ago
I generally learn proper nouns first (following news is my biggest use of MT) and I've found it often helps to replace them by placeholders such as X and Y when translating. As to sibling comment, IIRC Yandex translate has a nice feature where they'll provide a number of meanings for words when clicked upon in the source text. "Invisible, Insane"

klodolph 6y ago

Oh, so much. Machine translation doesn't understand context and routinely fails at fairly basic translation tasks. Here's part of the Google translation of the front page of ja.wikipedia.org into English: > 非行少年の再非行の抑止や更生を目的としており、決定までの過程として、「非行事実を家庭裁判所に送致・通告 - 家庭裁判所調査官（以下「調査官」と略称する）等による調査 - 調査結果をふまえた審判 - 必要に応じて保護的措置あるいは保護処分を決定」という流れを経るのが通例である。 > And for the purpose of deterrence and rehabilitation of re-delinquency of juvenile delinquents, as the process of until a decision, " the fact delinquency to the family court -up Reel-notice - family court investigators (referred to hereinafter as" investigators "), or the like by the survey - survey It is customary to go through the flow of " judgment based on the result- decide protective measures or protective measures as necessary ". That's barely recognizable as English. The translation is hot garbage. Perhaps something you might use if you had an emergency. Just to point out here... the text on the front page of Wikipedia tends to be very neutral, matter-of-fact, and well written. There are no characters (like in novels), no use of slang, and nothing else that should be tricky for translation, and yet we get this garbage. It's a miracle that machine translation works at all, and when it does work, it is usually because you are translating between languages that are fairly similar to begin with. For example, English and Spanish.

nonbirithm 6y ago
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.
- hrktb 6y ago
  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.
  - fireattack 6y ago
    It's not nearly as bad compared to Japanese. And it pretty much only happens in conversations.
  - yorwba 6y ago
    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.
benrbray 6y ago
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)

qayxc 6y ago

> do you have any idea of what things machine translation still consistently gets wrong? My biggest pet peeve with MT is the incredibly brain-dead choice of using English as an intermediate language. This effectively prevents MT from ever becoming useful for any A→B translation where neither A nor B are English. Example: put "пружи́на" into Google translate (or Bing translate, doesn't matter)and translate to German. You'll get "Frühling" as the suggested translation. This is due to the fact that internally "пружи́на" is translated to English first, resulting in "spring". This is then translated to German with the statistically highest probability being "Frühling", even though "Feder" would be the only correct match ("Frühling" would be "весна" in Russian). If you now say "But wait! This should clear itself up once you add some context, right?" you'd be wrong. Try "Die Sprungfeder kam gerade rechtzeitig." ("The coil spring arrived just in time,") and you'll get "Весна пришла как раз вовремя." ("Springtime came just in time"). It's ridiculous! EDIT: happens with DeepL as well; only Yandex gets it right (presumably because they actually use an actual German/Russian-model.

dmurray 6y ago
It's not brain-dead, it's a result of limited training data. For N languages you need to train N models if you go through English as an intermediate language, but N^2 if you don't use an intermediate language model. OK, computer time is cheap these days, but more importantly, for each language pair you need D high-quality documents translated in each of your two languages. There are far fewer pieces of writing that exist in both German and Russian than in both German and English or both Russian and English. There is cool-looking research on zero-shot language translation where the model learns its own internal "intermediate language". But you can be sure that if it produced reliably better results with the data available, the likes of Google would already be using it.
- qayxc 6y ago
  > It's not brain-dead, it's a result of limited training data. That's quite literally a cheap excuse. It'snot lack of training data (remember, you still need translations in the target language anyway!), it's a result of Anglo-Saxon cultural hegemony. > There are far fewer pieces of writing that exist in both German and Russian than in both German and English or both Russian and English. Right. And that's why Russia-based Yandex gets excellent results for German/Russian translation, I see... > But you can be sure that if it produced reliably better results with the data available, the likes of Google would already be using it. Nah, you're being way too optimistic there. Google is a business and as such, they don't chase SOTA outside of research (which doubles as advertisement). Good enough is the name of the game and it's simply infinitely cheaper to just train and maintain N-models (i.e. English/X and X/English) than to do the same for all combinations of X/Y-pairs (which would be N² models). So as long as there is no incentive to do better, they simply won't, because they rely on the fact that English is the de-facto Lingua Franca and people translate to and from English more often than directly between other languages. This would be an actual chance for companies from other countries (especially European ones) to step in and do exactly that, but again - "good enough" and reliance on English stops that from happening anytime soon. It's a missed opportunity especially with low-resource languages as demonstrated by the recent "Scottish" Wikipedia fiasco...

Shorel 6y ago

> Oh fascinating, do you have any idea of what things machine translation still consistently gets wrong? Definitely anything where a word has two or more different meanings. Paper jam being my favourite example. https://translate.google.com/#view=home&op=translate&sl=en&tl=es&text=Paper%20jam https://translate.google.com/#view=home&op=translate&sl=en&t... It is translated as "(fruit) jam made out of paper"