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Machine translation feels like one of the quiet victories of the current crop of machine learning products. It's getting to be within spitting distance of bein

Machine translation feels like one of the quiet victories of the current crop of machine learning products. It's getting to be within spitting distance of being completely adequate (though not excellent) for translating long-form articles, which is amazing considering where things stood ten years ago.

Baeocystin 6y ago
I find that machine translation is getting better at producing what reads as grammatically correct text, but that it still misses a lot of what is said, even when the idioms and expressions used are common. Often what is produced is grammatical, but opposite/orthogonal of the initial intent. In a sense, I feel that this is an even worse situation, because the word-salad signal that it isn't an accurate-meaning translation isn't as strong.
- xbmcuser 6y ago
  I think the problem is when direct translation is done. Netflix paper using simplify then translating to me seems to be the better approach when you want to convey the meaning rather than just translating the exact words https://arxiv.org/abs/2005.11197 https://arxiv.org/abs/2005.11197
- userbinator 6y ago
  That's what MT based on neural networks will do: their output can look great, but the correspondence with the input can be highly inaccurate. On the other hand, traditional methods based on syntactic analysis tend to yield less grammatically correct and more literal translations, but the literalness can also result in better accuracy.
  - MiroF 6y ago
    Mm... I think you'd be hard pressed at this point to find any advantage that traditional methods have over deep methods. Maybe this was true 4-6 years ago, but no longer.
    - _-___________-_ 6y ago
      Depends what you optimise for. Neural translation fairly consistently produces incorrect results that read like they could be correct, for me. If your goal is "text that makes sense" they seem to be doing great. If your goal is "text that conveys the same meaning as the source" they're universally rubbish, at least for the language pairs I need (mostly english <-> east Asian languages).
      - MiroF 6y ago
        > english <-> east Asian languages I'm curious what the domain is and what languages you're talking about, it's hard for me to evaluate your claims otherwise. I would buy it for English -> Tibetan for instance.
        _-___________-_ 6y ago
        Mostly Chinese, Japanese, and Vietnamese, and mostly business and lightly-technical text. My observation of the failure rate (if you define failure as "meaning has been lost") is well above 80% with Google Translate for these languages and this type of text. It's literally unusable because you have no confidence that the translation has not completely reversed or garbled the meaning of the text, and the failure mode is particularly unpleasant because you may be unaware that it has failed to translate correctly, since it produces text that appears to have meaning - just the wrong meaning.
        numpad0 6y ago
        Kinda funny when people say "East Asia" it mostly means Japan and its isolationism, with China mixed in just to change the tone a bit. I don't find it offensive, just kind of funny/interesting.
        _-___________-_ 6y ago
        Well, in the context of language it's a useful term because of the close relationship many east Asian languages have.
klodolph 6y ago
For some language pairs, maybe. The problem is that as you translate between more distant languages, you find that something which is contextual in one language is textual in the other, and vice versa. The neural networks are great at faking it, and they'll make up text that looks completely reasonable all day long. If that means inventing subjects and choosing random verb tenses, that's what they'll do. If you pick closely-related languages, you might be able to get a near perfect translation just by putting each word through a dictionary, and the neural networks can disambiguate things and smooth over the problems in the output by looking at local context. If you pick distantly-related languages, you might routinely need context from paragraphs away, or context which isn't present in the text at all but relies on some kind of human experience.
- MiroF 6y ago
  > more distant languages, low resource translation is definitely still bad, you're right. > near perfect translation just by putting each word through a dictionary, and the neural networks can disambiguate things and smooth over the problems in the output Funnily enough, this is not a terrible explanation of how the current sort of approaches to low-resource MT work.
  - thaumasiotes 6y ago
    >> more distant languages, > low resource translation is definitely still bad, you're right. No, you've misunderstood. Languages that are closely related to each other are fairly easy to translate between. Languages that aren't related are not, regardless of whether you have a large corpus to work with. English - Mandarin is not an example of "low resource translation".
    - MiroF 6y ago
      > Languages that aren't related are not, regardless of whether you have a large corpus to work with The size of the corpus makes a huge difference. I don't see how can both be true that a. there is this massive failure of translation engines on these language pairs, b. human translators were unable to differentiate between human translated chinese-english sentences and machine translated sentences. It's not incredible - but it's also not awful or unusable.
_-___________-_ 6y ago
I really have to disagree. Machine translation is utterly terrible, and appears to be optimising for the wrong thing. All the progress seems to be in making it produce output that makes sense, rather than producing output that conveys the same meaning as the source document. Almost every time I've tried to use machine translation for something serious, the meaning of the text has been garbled or reversed. This is not "adequate" unless your only goal is to produce something that looks like it could be correct, without regard for whether it is.
danielscrubs 6y ago
What now? How? It’s getting better by the day but translation is one of the places where it has done more damage than good. It teaches language students incorrect grammar and hinder students from changing their thought pattern. “- Milk tea or regular tea? -Yes!” Yes is the proper response in some languages. You can either change the language or your mindset. And it’s not like these things are one offs, all languages are basically a bunch of quirks. No doubt it’s the way of the future but I’d wait for the researchers to improve translation a lot.

Baeocystin 6y ago

I find that machine translation is getting better at producing what reads as grammatically correct text, but that it still misses a lot of what is said, even when the idioms and expressions used are common. Often what is produced is grammatical, but opposite/orthogonal of the initial intent. In a sense, I feel that this is an even worse situation, because the word-salad signal that it isn't an accurate-meaning translation isn't as strong.

xbmcuser 6y ago
I think the problem is when direct translation is done. Netflix paper using simplify then translating to me seems to be the better approach when you want to convey the meaning rather than just translating the exact words https://arxiv.org/abs/2005.11197 https://arxiv.org/abs/2005.11197
userbinator 6y ago
That's what MT based on neural networks will do: their output can look great, but the correspondence with the input can be highly inaccurate. On the other hand, traditional methods based on syntactic analysis tend to yield less grammatically correct and more literal translations, but the literalness can also result in better accuracy.
- MiroF 6y ago
  Mm... I think you'd be hard pressed at this point to find any advantage that traditional methods have over deep methods. Maybe this was true 4-6 years ago, but no longer.
  - _-___________-_ 6y ago
    Depends what you optimise for. Neural translation fairly consistently produces incorrect results that read like they could be correct, for me. If your goal is "text that makes sense" they seem to be doing great. If your goal is "text that conveys the same meaning as the source" they're universally rubbish, at least for the language pairs I need (mostly english <-> east Asian languages).
    - MiroF 6y ago
      > english <-> east Asian languages I'm curious what the domain is and what languages you're talking about, it's hard for me to evaluate your claims otherwise. I would buy it for English -> Tibetan for instance.
      - _-___________-_ 6y ago
        Mostly Chinese, Japanese, and Vietnamese, and mostly business and lightly-technical text. My observation of the failure rate (if you define failure as "meaning has been lost") is well above 80% with Google Translate for these languages and this type of text. It's literally unusable because you have no confidence that the translation has not completely reversed or garbled the meaning of the text, and the failure mode is particularly unpleasant because you may be unaware that it has failed to translate correctly, since it produces text that appears to have meaning - just the wrong meaning.
        numpad0 6y ago
        Kinda funny when people say "East Asia" it mostly means Japan and its isolationism, with China mixed in just to change the tone a bit. I don't find it offensive, just kind of funny/interesting.
        _-___________-_ 6y ago
        Well, in the context of language it's a useful term because of the close relationship many east Asian languages have.

klodolph 6y ago

For some language pairs, maybe. The problem is that as you translate between more distant languages, you find that something which is contextual in one language is textual in the other, and vice versa. The neural networks are great at faking it, and they'll make up text that looks completely reasonable all day long. If that means inventing subjects and choosing random verb tenses, that's what they'll do. If you pick closely-related languages, you might be able to get a near perfect translation just by putting each word through a dictionary, and the neural networks can disambiguate things and smooth over the problems in the output by looking at local context. If you pick distantly-related languages, you might routinely need context from paragraphs away, or context which isn't present in the text at all but relies on some kind of human experience.

MiroF 6y ago
> more distant languages, low resource translation is definitely still bad, you're right. > near perfect translation just by putting each word through a dictionary, and the neural networks can disambiguate things and smooth over the problems in the output Funnily enough, this is not a terrible explanation of how the current sort of approaches to low-resource MT work.
- thaumasiotes 6y ago
  >> more distant languages, > low resource translation is definitely still bad, you're right. No, you've misunderstood. Languages that are closely related to each other are fairly easy to translate between. Languages that aren't related are not, regardless of whether you have a large corpus to work with. English - Mandarin is not an example of "low resource translation".
  - MiroF 6y ago
    > Languages that aren't related are not, regardless of whether you have a large corpus to work with The size of the corpus makes a huge difference. I don't see how can both be true that a. there is this massive failure of translation engines on these language pairs, b. human translators were unable to differentiate between human translated chinese-english sentences and machine translated sentences. It's not incredible - but it's also not awful or unusable.

_-___________-_ 6y ago

I really have to disagree. Machine translation is utterly terrible, and appears to be optimising for the wrong thing. All the progress seems to be in making it produce output that makes sense, rather than producing output that conveys the same meaning as the source document. Almost every time I've tried to use machine translation for something serious, the meaning of the text has been garbled or reversed. This is not "adequate" unless your only goal is to produce something that looks like it could be correct, without regard for whether it is.

danielscrubs 6y ago

What now? How? It’s getting better by the day but translation is one of the places where it has done more damage than good. It teaches language students incorrect grammar and hinder students from changing their thought pattern. “- Milk tea or regular tea? -Yes!” Yes is the proper response in some languages. You can either change the language or your mindset. And it’s not like these things are one offs, all languages are basically a bunch of quirks. No doubt it’s the way of the future but I’d wait for the researchers to improve translation a lot.