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> ..I've been checking how Google translates a particular word from Greek, to French. I have a couple of these in Japanese. One is "七輪" (brazier), which Google

> ..I've been checking how Google translates a particular word from Greek, to French. I have a couple of these in Japanese. One is "七輪" (brazier), which Google Translate thinks is "tambourine"; the other is "ちゃぶ台" (tea table), for which it returns "Shabu-bashi". No idea what's going on in either case - the latter doesn't even mean anything in Japanese. It's always nice to read Hofstadter, but when Google Translate trips up over single words, delving into subtleties of contextual grammar seems like overkill.

YeGoblynQueenne 9y ago
>> It's always nice to read Hofstadter, but when Google Translate trips up over single words, delving into subtleties of contextual grammar seems like overkill. Ah, but see, the very counterintuitive reasoning is that it's actually easier to translate a sentence than a single word- because the context provided by the other tokens in the sentence can help determine the intended usage of the word. For instance, the following sentence is translated pretty much 100% correctly by GT: Το χελιδόνι είναι αποδημητικό πτηνό. The swallow is a migratory bird. Which in a way makes the mistake on the single word even harder to understand (I mean, for users who don't know anything about how machine translation works). Of course, when sentences start getting more complex, looking at the near context, or even the far context (that LSTM nets are good at) is not going to help you. That's even more so when you have no way to tell which of multiple possible meanings of a word you should use to derive the correct context and find the correct translation. So, yes, the inability of GT to discern between meaning at the word-level affects its ability to translate more complex passages, but you may miss that with simpler sentences- which I think may explain why many people think that GT translations are actually pretty good, when they mostly really suck.
- fenomas 9y ago
  I understand what you're saying, but there's no subtlety or dual meaning in my examples - Google Translate is just taking in a word with a single, unambiguous meaning, and returning a wholly unrelated word (or in the latter case gibberish). In other words they're cases where GT is less useful than a plain lookup database - which makes comparisons to human translators seem rather beside the point!
  - YeGoblynQueenne 9y ago
    Fair point. My hunch is that there is in fact some subtle ambiguity associated with your example words, that is not immediately apparent for human spearkers of Japanese and English, simply because they have all sorts of "filters" that wring out the right meaning even for single words, filters that are missing from simple dumb statistical translation. However, it's often very hard to rev-eng the errors committed by statistical machine translation (and, generally, language processing) systems because they're basically just vectors of numbers that are completely meaningless to us. Which also makes it very hard to identify patterns of errors and correct them. Another thing that's very wrong with machine translation is the measures of "goodness" of translations. This is usually something like the BLEU score, that work by comparing the words in machine translation to those in human translation. Those are very problematic in all sorts of ways, not least the inability to select a "good" candidate translation among the many possible. In other words, it's not just that gathering statistics over words in context is not a very good way to model translations. On top of that, the results of such translations are very difficult to evaluate objectively. In general, machine translation is a very vaguely defined problem- which lets GT claim to be really, really good at it even when it's easy to see it really, really isn't.

YeGoblynQueenne 9y ago

>> It's always nice to read Hofstadter, but when Google Translate trips up over single words, delving into subtleties of contextual grammar seems like overkill. Ah, but see, the very counterintuitive reasoning is that it's actually easier to translate a sentence than a single word- because the context provided by the other tokens in the sentence can help determine the intended usage of the word. For instance, the following sentence is translated pretty much 100% correctly by GT: Το χελιδόνι είναι αποδημητικό πτηνό. The swallow is a migratory bird. Which in a way makes the mistake on the single word even harder to understand (I mean, for users who don't know anything about how machine translation works). Of course, when sentences start getting more complex, looking at the near context, or even the far context (that LSTM nets are good at) is not going to help you. That's even more so when you have no way to tell which of multiple possible meanings of a word you should use to derive the correct context and find the correct translation. So, yes, the inability of GT to discern between meaning at the word-level affects its ability to translate more complex passages, but you may miss that with simpler sentences- which I think may explain why many people think that GT translations are actually pretty good, when they mostly really suck.

fenomas 9y ago
I understand what you're saying, but there's no subtlety or dual meaning in my examples - Google Translate is just taking in a word with a single, unambiguous meaning, and returning a wholly unrelated word (or in the latter case gibberish). In other words they're cases where GT is less useful than a plain lookup database - which makes comparisons to human translators seem rather beside the point!
- YeGoblynQueenne 9y ago
  Fair point. My hunch is that there is in fact some subtle ambiguity associated with your example words, that is not immediately apparent for human spearkers of Japanese and English, simply because they have all sorts of "filters" that wring out the right meaning even for single words, filters that are missing from simple dumb statistical translation. However, it's often very hard to rev-eng the errors committed by statistical machine translation (and, generally, language processing) systems because they're basically just vectors of numbers that are completely meaningless to us. Which also makes it very hard to identify patterns of errors and correct them. Another thing that's very wrong with machine translation is the measures of "goodness" of translations. This is usually something like the BLEU score, that work by comparing the words in machine translation to those in human translation. Those are very problematic in all sorts of ways, not least the inability to select a "good" candidate translation among the many possible. In other words, it's not just that gathering statistics over words in context is not a very good way to model translations. On top of that, the results of such translations are very difficult to evaluate objectively. In general, machine translation is a very vaguely defined problem- which lets GT claim to be really, really good at it even when it's easy to see it really, really isn't.