16 ms·

The Shallowness of Google Translate

217 points by ehudla 9y ago

ehudla 9y ago
DRH's /Le Ton Beau De Marot: In Praise Of The Music Of Language/ is a remarkable book about translation of literature (all of his books are must reads, btw). Still, it seems to me that shallowness is a feature, not a bug, of automatic translation.
ColinWright 9y ago
For those who might be interested to see the discussion by the HN on this article, it's been submitted and discussed before: https://news.ycombinator.com/item?id=16287171 https://news.ycombinator.com/item?id=16287171 (23 comments) https://news.ycombinator.com/item?id=16267363 https://news.ycombinator.com/item?id=16267363 (12 comments) There are other submissions without comments, showing that the article is of interest: https://news.ycombinator.com/item?id=16285196 https://news.ycombinator.com/item?id=16285196 https://news.ycombinator.com/item?id=16279656 https://news.ycombinator.com/item?id=16279656 https://news.ycombinator.com/item?id=16265302 https://news.ycombinator.com/item?id=16265302 https://news.ycombinator.com/item?id=16294491 https://news.ycombinator.com/item?id=16294491 https://news.ycombinator.com/item?id=16296792 https://news.ycombinator.com/item?id=16296792
- dTal 9y ago
  1 day ago, 3 days ago... there is a serious problem with the short attention span of HN. No sensible forum should split threads like that. Some interesting discussions take longer than a day to execute, and there is no reason why a thread could not be alive for years, but HN actively discourages it - there is no interface for discovery besides the novelty firehose of frontpage+new, and voting is forbidden after a certain age (possibly replying? I've never tried).
  - ColinWright 9y ago
    I see this as an inevitable consequence of several deliberate design features. It's a link-sharing site, and people get karma for popular links. The votes serve to make the items rise on the front page, getting more votes, hence more karma. So people will rush to be first, and there is no serious mechanism to check whether the same story, or even the nearly exact same link has been submitted before. But anything more sophisticated will be more complex, harder to understand, more fragile, and possibly less popular. It's not hard to see several things that might make it better, but it's uncertain, more work, and guaranteed to make the system less transparent. <fx: shrug /> To some extent it is what it is. But FWIW, I agree.
    - cheschire 9y ago
      Reddit does a simple link comparison to see if the article has been submitted before, which does limit the double-posts, however some people then intentionally subvert the process by running the link through a processor like outline.com or archive.is, or simply finding alternate articles from other sources. This is unfortunate as the thing I really want to see is an in-depth conversation about a topic more than an individual article.
      - jwilk 9y ago
        HN does that too.
  - dang 9y ago
    It's not all that common that threads get split like that, and we often merge them when they do (see https://hn.algolia.com/?sort=byDate&dateRange=all&type=comment&storyText=false&prefix=false&page=0&query=%22comments%20moved%22 https://hn.algolia.com/?sort=byDate&dateRange=all&type=comme..., https://hn.algolia.com/?sort=byDate&dateRange=all&type=comment&storyText=false&prefix=false&page=0&query=by:dang%20merged https://hn.algolia.com/?sort=byDate&dateRange=all&type=comme...). As for attention span, threads often go for longer than a day but rarely go for longer than a few days. That's just how the community behaves and if, as I assume, it's true of internet forums generally, there's little to be done about it.
- cheschire 9y ago
  Good bot.
  - ColinWright 9y ago
    Er ... Thank you? Except I'm not a 'bot ...
    - cheschire 9y ago
      Yes, that was the joke.
      - ColinWright 9y ago
        Ah. It was a joke. OK.
  - amake 9y ago
    Take that nonsense back to Reddit, please.
    - cheschire 9y ago
      An ironic joke that you don’t find funny doesn’t make it nonsense. I was aware he was not a bot and was simply commenting with tongue firmly planted in cheek that he seems to expend bot like effort on posts exactly like these.
olasaustralia 9y ago
Jeffrey Shallit has a response - http://recursed.blogspot.com/2018/02/doug-hofstadter-flight-and-ai.html http://recursed.blogspot.com/2018/02/doug-hofstadter-flight-...
- empiricus 9y ago
  I was expecting this reply to be like "yes translation is not yet perfect but we have some ideas how to fix this, and the solution is different than what Hofstadter thinks". Instead it was more like "AlphaGo is different from humans, airplanes are different from birds, and deep translation is different(better?) from human translation."
  - mtgx 9y ago
    And self-driving cars may not get drunk but they'll kill you in different ways than humans do (think software errors, or hacking). He may actually have a point there.
- randomsearch 9y ago
  I think he’s (deliberately? Give him the benefit of the doubt) misinterpreted Hofstadter’s use of the words “processing text”. What he means, which seemed quite obvious to me, is that the machine is not reading text, building up a semantic interpretation of the sentence in the way a human does. That’s because a neural net is a simple pattern recognition machine that does not work in the same way as the brain. It doesn’t have the immense life experience to draw upon (probably relatively easy to fix) but more importantly it doesn’t have a concept of what a sentence actually means. A neural network doesn’t have an understanding of what “double” means. It just pattern matches a translation. I think there’s some serious symbolic reasoning going on in the brain, which neural nets don’t yet perform. It all feels like shallow syntactic matching right now, rather than semantic reasoning. Unfortunately I don’t know how the brain works, so you end up in an absurd argument where people say “the brain is just a neural net” and it’s impossible to completely refute their claims, just as if someone said “the brain is a very large lookup table”. Well, from what I see it does seem that the brain is much smarter than that, but I can’t be certain without knowing what that extra kicker is. So whilst such an assertion seems terribly simplistic and self-evidently insufficient, it is difficult to argue with.
ddmd 9y ago
There is a new online translator, http://deepl.com http://deepl.com, which relies on deep learning techniques and provides higher (semantic) quality and accuracy of translation. Previously I had quite positive experience with http://translate.yandex.com http://translate.yandex.com (but I had to manually compare and combine their results with google translate).
- nicois 9y ago
  I just tried the following, to German, and got the predictable result, which is completely wrong. Until the proper semantic parts of the text are identified, these sorts of mistakes prevent statistical methods from being trustworthy: Sailing ships in rough seas is asking for trouble
  - romseb 9y ago
    Put a period at the end of the sentence and it changes from > Segelschiffe in rauer See verlangen Ärger. to > Segelschiffe in rauher See sind ein Problem.
    - Freak_NL 9y ago
      The translating software can't distinguish between 'sailing ships' the noun, and 'sailing' (verb) 'ships' (noun) the act. The latter is what is probably intended. It is also missing chances of using idiomatic expressions in the translations.
      - claudius 9y ago
        Well, 'is' is singular so it can’t be the correct verb associated to 'sailing ships' if the latter means multiple sailing ships rather than the activity of sailing ships.
  - ddmd 9y ago
    I like that I can clikc a word in the target box and choose an alternative translation while the system updates the result accordingly. Previously, I had to play with different translation alternatives manually in Google or Yandex translate. I think deepl also learns from such user choices.
mehrdadn 9y ago
Related heads-up: if you ever want to use or test Google Translate, don't make the mistake of assuming Gmail's translator would return its results. I have no idea what it uses, but it sure isn't the same Google Translate. I've seen it produce output far inferior to that of Google Translate for the exact same text.
chx 9y ago
Ho humm. Of course he is right but is that enough? This reminded me of a quote from Doctorow's Microsoft Research DRM talk which I remember well because I translated (hah) it to Hungarian as my last act as a Hungarian journalist -- I sort of resurrected myself as one because by that point I haven't written in years. http://craphound.com/msftdrm.txt http://craphound.com/msftdrm.txt > This is the overweening characteristic of every single successful new medium: it is true to itself. The Luther Bible didn't succeed on the axes that made a hand-copied monk Bible valuable: they were ugly, they weren't in Church Latin, they weren't read aloud by someone who could interpret it for his lay audience, they didn't represent years of devoted-with-a-capital-D labor by someone who had given his life over to God. The thing that made the Luther Bible a success was its scalability: it was more popular because it was more proliferate: all success factors for a new medium pale beside its profligacy. The most successful organisms on earth are those that reproduce the most: bugs and bacteria, nematodes and virii. Reproduction is the best of all survival strategies. If you want the Hungarian one: https://www.hwsw.hu/hirek/40796/a-digitalis-jogkezelo-rendszerek-kritikaja.html https://www.hwsw.hu/hirek/40796/a-digitalis-jogkezelo-rendsz...
- csydas 9y ago
  I think Doctorow's point is dramatically different here. He's talking about a change in medium and specifically arguing against the claims about the original experience offered by DRM controlled media (e.g., theatres, ebooks, etc). The article's point is more about while Google Translate has its impressive moments, it needs to be understood for what it is - the most basic translation services. If you've ever had to rely on Google Translate (or any automated translate service) you'll know what I mean by "basic translation services". It's great if you want to say "I want to order Fish and Rice" or "Excuse me, where is the restroom". The moment you try to get past such complexities is where the automated translations just can't get a good grip on human languages. Languages from the same families naturally are a bit easier (English to Spanish, for example usually can allow for more complexities), but you still lose a lot simply because it's a 1:1 translation from the box on the left/top to the box on the right/bottom, which doesn't happen. I'm learning Russian (in Russia no less) and one of the hardest things for me as a native english speaker is avoiding the use of "to be", which is almost never used in Russian. There are other oddities as well regarding prepositions in Russian that are handled differently than English (my poor teacher still can't satisfactorily explain to me why stuff is "on the kitchen", but it's "in the ___" for other rooms) (в гостинной versus на кухне). Now, surprisingly Google translate gets these two right, but other such preposition specialties it bungles completely, and I've had more than my fair share of confused looks when I have to resort to using translate for more complex sentences. Especially when it comes to things like words of motion, for which there are tons of different words depending on how you're going. It's good that it's accessible, but when the reverse happens (russian to english), for me it requires a fairly fast sed operation to fix Google Translate's choices for words or just some additions to help get what the person is wanting. Edit: Addendum to the main point, I can see that there may be applicability in that Translate Tools will allow for pidgin languages to crop up if it receives wide spread usage in real world settings, but I'd be curious to see from a large sample how many people use the conversation style features. I'm sure there's plenty of anecdata on the subject from those who have to use it (myself included), but I wonder if it seems as well used if you increase the sample size out further. Globally, people still pretty much stay at home and/or know enough english to get by.
paganel 9y ago
I heartily recommend Umberto Eco's text "Experiences in Translation" (full text here, a little long: https://archive.org/stream/UmbertoEcoEXPERIENCESINTRANSLATION/Umberto%20Eco%20EXPERIENCES%20IN%20TRANSLATION_djvu.txt https://archive.org/stream/UmbertoEcoEXPERIENCESINTRANSLATIO...), which perfectly describes the difficulty (and I'd say beauty) of the act of translation. > In this book Umberto Eco argues that translation is not about comparing two languages, but about the interpretation of a text in two different languages, thus involving a shift between cultures. The above short presentation of the book says it all, basically, but surprisingly enough is really hard to understand for lost of technical people who only think that translation is just "de-coding", as Hofstadter explains. We won't have proper translations until AGI is here.
jmadsen 9y ago
I use Google Translate extensively to help write things in Japanese - considered (one of) the most difficult languages in the world. I show the results to Japanese people who nearly always tell me it looks just fine to them. I have also passed off translations as my own work on tutoring sites like Lang8 and had natives correct "my work". They often will give a slightly different wording, but I have never had an "WTF does this mean?" type response. ---- I think it is important to distinguish the difference between a perfect translation with nuance, and a simple "I need to make this point" translation that is what we need most of the time.
- lifthrasiir 9y ago
  > [...] Japanese - considered (one of) the most difficult languages in the world. Just in case, one of the most difficult languages to learn by native English speakers. It is pretty easy for Koreans for example---they share the same sentence order (subject-object-verb) and many words frequently share the etymology due to throughout Chinese influence. More quantitively, Idibon had once catalogued [1] the weirdness of natural languages compared to others. English ranked 33rd place out of 239 languages. Quoting the original, "[p]art of this is to say that some of the languages you take for granted as being normal (like English, Spanish, or German) consistently do things differently than most of the other languages in the world". [1] https://corplinguistics.wordpress.com/2013/06/21/the-weirdest-languages/ https://corplinguistics.wordpress.com/2013/06/21/the-weirdes... (the original link is dead)
- 2muchcoffeeman 9y ago
  >I think it is important to distinguish the difference between a perfect translation with nuance, and a simple "I need to make this point" translation that is what we need most of the time. Isn't this exactly the point though? A different language in a different culture would convey an idea in a different way. I'd love to know what exactly you're translating. I have experience with translations from work. I've never met a native speaker say that a translation was any good. Usually, "I understand the meaning but you would never say it that way."
- vadimberman 9y ago
  Try going the other way around, and read the machine-translated result yourself.
YeGoblynQueenne 9y ago
For the past 5 years or so, as a kind of benchmark, I've been checking how Google translates a particular word from Greek, to French. The word is "χελιδόνι", meaning "swallow" (the bird) in Greek. For as long as I've been trying this tiny little experiment, Google has been translating it to the French word "avaler"- the verb "to swallow". https://translate.google.com/#el/fr/%CF%87%CE%B5%CE%BB%CE%B9%CE%B4%CF%8C%CE%BD%CE%B9 https://translate.google.com/#el/fr/%CF%87%CE%B5%CE%BB%CE%B9... Once in a while, a different translation appears- "machaon", which is a kind of butterfly, the Old World swallow-tail. This is slightly closer but still absurd. If GT can make the connection to "swallow-tail", how can it not see the connection to the bird, "swallow"? The problem seems to be that, in order to translate from Greek to French, Google goes via English. A great big chunk of context is lost in the process, especially since now the translation between two languages that have different forms for male and female nouns goes through a third language that does not. So for example, I've seen the same gender-reversal as Hofstadter reports in his article. The following string in Greek means "I saw my teacher and she said hello to me". Είδα τη δασκάλα μου και μου είπε γειά. The following are the French and English translations by GT: J'ai vu mon professeur et il m'a dit bonjour. I saw my teacher and he said hello. This seems to happen because Google's English language model has learned that "he" is found in the context of "teacher" and "said" more often than "she" is. But of course, such a statistical association is, well, meaningless and for that useless when it comes to translation, where you actually need to know when the least likely case is correct. Generally, it looks like the complete abandonment of any attempt at representing meaning, and relying instead on text statistics to do meaning-intensive work like translation, is producing a lot of nonsense. And that's not a criticism of Google Translate only. I think professor Chomsky might "win" that old debate with prof. Norvig, after all.
- dragandj 9y ago
  Are you sure that this particular translation is "nonsense"? To me, it seems almost perfect. The only mistake in the French version is the gender of the teacher, which is a mistake that many humans can and do make, in addition to many other mistakes...
  - puszczyk 9y ago
babuskov 9y ago
I don't know what translation engine Bing is using, but I find that combining Google Translate and Bing results gets me pretty close to the real thing. I wish teams making those two could work together :(
nabla9 9y ago
Hofstadter's strength is his ability to stay focused on the highest level cognition problem. His theory is that analogies are the core of human cognition. His main work revolves around discovering how extensive human ability to think using analogies is. I especially like his attempts to understand and capture the high level cognition in simple toy problems. His `copycat` and `Letter Spirit` programs illuminate the problem and his thinking in very clever way. The current AI and ML research is building things bottom up and there is still significant gap before we reach the high level cognition that Hofstadter is interested in. What is the representation that binds low level and high level cognition and allows high level fluid concepts to be used as analogies from one domain to next with just one or two examples? Style transfer, variational autoencoders and transfer learning are very limited in this regard. Challenges for deep learning: * Deep Letter Spirit. Show 1-3 examples of lowercase letters of the roman alphabet in some font and have an algorithm that understands the style and completes the rest of the alphabet in the same style. * Bongard problems solver.
pooya72 9y ago
On the other hand, it's interesting how well Google Translate translates philosophical text. Just compare the translations below. Gadamer's text from Truth and Method: Die folgenden Untersuchungen haben es mit dem hermeneutischen Problem zu tun. Das Phänomen des Verstehens und der rechten Auslegung des Verstandenen ist nicht nur ein Spezialproblem der geisteswissenschaftlichen Methodenlehre. Es hat von alters her auch eine theologische und eine juristische Hermeneutik gegeben, die nicht so sehr wissenschaftstheoretischen Charakters waren, als"vielmehr dem praktischen Verhalten des durch die Wissenschaft ausgebildeten Richters oder Pfarrers entsprachen und ihm dienten. Weinsheimer and Marshall translation: These studies are concerned with the problem of hermeneutics. The phenomenon of understanding and of the correct interpretation of what has been understood is not a problem specific to the methodology of the human sciences alone. There has long been a theological and a legal hermeneutics, which were not so much theoretical as corrolary and ancillary to the practical activity of the judge or clergyman who had completed his theoretical trainin Google translate: The following investigations have to do with the hermeneutic problem. The phenomenon of understanding and the right interpretation of the understanding is not only a special problem of the humanistic methodology. There has also been a theological and juridical hermeneutics from ancient times, which were not so much scientific-theoretical in character as they corresponded to and served the practical behavior of the scientist or pastor trained by science.
- ACow_Adonis 9y ago
  Cynically, i wonder if a translation of that text was actually fed in/available to the translation process. I mean texts already translated into other languages would presumably be one of the most obvious things to feed in as training data.
  - PeterisP 9y ago
    There's no need to wonder, one can be virtually certain that every "classic" work that has been translated to many languages is included in the training data of major translation systems. MT world runs on data, it includes as much as it can, and a book translation wouldn't be included only if its translations are not readily available in digital form - and pretty much all famous books have been digitized.
larrysalibra 9y ago
At least Google Translate is a bit better than Baidu Translate which at some point decided that my name is the English translation of 扒饭 (grilled rice). 2 years ago, I appeared on the menu of a restaurant on Huawei's campus in Shenzhen because someone apparently used Baidu Translate to translate the menu to English: https://twitter.com/larrysalibra/status/959749866036408320 https://twitter.com/larrysalibra/status/959749866036408320 And 2 years later, I'm still grilled rice: http://translate.baidu.com/#zh/en/扒饭 http://translate.baidu.com/#zh/en/扒饭 Human language is hard!
- napsterbr 9y ago
  That is... Bizarre. Any idea how that happened? Maybe you tweeted "grilled rice" before?
  - larrysalibra 9y ago
    No idea. Ha!
- CapsAdmin 9y ago
  Here it shows "Larrysalibra" or "Grilled meal" if I put a newline between the two characters.
  - brandonhorst 9y ago
    If you put a newline in the middle of a word English, it changes the meaning too.
- kiwidrew 9y ago
  Wow, that's awesome, the only way it could be more surreal is if you had been holding the menu in front of you! "扒饭" isn't, on its own, an actual Chinese word. The "扒" is basically a modifier that comes after a type of meat and indicates that it is a "steak" or "cutlet". So your menu item "美式杂扒饭" should translate as "American-style" (美式) "mixed grill" (杂扒) "on rice" (饭)... but the machine translation is getting the word boundaries wrong and translating it as "American-style" (美式) "mixed" (杂) "larrysalibra" (扒饭). Fascinating.
87ert3746 9y ago
What's going to happen is that we humans are going to become more machine-like, as we loose our appreciation of our own sophistication & subtlety, so that most people will be satisfied with machine-translation, writing, art and music never having known that there are higher levels
jvvw 9y ago
My sister is a professional translator and when companies pay her they generally do it because they want really good translations not because they just want people to understand the content. This can be for semi-legalistic reasons (though she's not a legal translator per se, but she has done a fair bit of work related to the EU) or for reasons of professional reputation. Literary translation, although not her mainstay, is also obviously really hard to do well. She's not worried about Google Translate at all yet, although she does have the advantage that one of her languages is rare for native English speakers and she is an extremely good writer in English.
- YeGoblynQueenne 9y ago
  I have a cluster of friends who are professional translators and interpreters and the translators have complained to me a lot about a machine translation program called Trados, that they are pretty much forced to use in their day-to-day jobs. The complaint I heard was that translation agencies hire translators with contracts that state that 80% of the translation work will be performed by Trados and the human translator will only be paid for 20% of it (I obviously don't know exact numbers!). Of course, in practice, Trados' translations are nowhere near 80% of the work needed to translate some text, to the point that the human translator ends up doing the bulk of the work and being paid for only a small fraction of it. It looks like translators are already in trouble with machine translation software, even if they can still do the job way better than that software. I think we can expect to see this pattern a lot more often in the future: automation taking over jobs not because it's better at them, but because it's more profitable that way.
  - petepete 9y ago
    The effectiveness of Trados or other MT software is entirely dependent on the quality of the glossaries and translation memories. Both of these are built up over time, the bigger and better (and more specific) they are, the less the translator has to be involved. If independent, having their own glossaries and TMs boost their efficiency and productivity. If they are working on the 20%, improving the company TMs is, in the short term, eating their own profit. In the long term, they are training their own replacement.
Hendrikto 9y ago
> I am not in the least eager to see human translators replaced by inanimate machines. Indeed, the idea frightens and revolts me. To my mind, translation is an incredibly subtle art that draws constantly on one’s many years of experience in life, and on one’s creative imagination. Yeah yeah... the special snowflake argument. „No, I am irreplacable, my job is special.“ Sure, machine translation isn‘t very good atm, but it‘s getting better, and I see no reason whatsoever why it shouldn‘t reach human levels with the correct technique.
- Eliezer 9y ago
  Douglas R. Hofstadter is a tad more than a translator. Try Google.
- 3pt14159 9y ago
  Do you speak a second human language that’s not closely related to your first? English and Russian, say? Because I find most data scientists that do tend to think language translation may be one of the last things we solve. I think it requires hard AI, honestly.
  - golergka 9y ago
    I speak three: Russian (native), English (mistaken for native speaker by native speakers) and Hebrew (average). I may not be an expert in AI though (couple of courses, hobby projects and worked alongside AI researchers on the non-ML side of some apps). Still, looking at the methods we already have and recent successes (like Yandex's linguistic work), it doesn't like as hard a problem as hard AI with human-like cognition.
  - jpalomaki 9y ago
    Maybe look at the progress we have made in last twenty years? Yes, there are examples of machine translation failing miserably, but it has also reached a level which allow people to actually use it for communication (as evidenced in real life and mentioned in the article). I think that's already quite an achievement. We should not be too harsh on computers. Humans are not perfect either. Instead of judging machine translation based on few random cases, we should be evaluating against number of human translators of various experience levels.
half-kh-hacker 9y ago
I've found, for Korean, at least, Naver Papago is miles ahead of Google Translate - Especially while decoding some unnatural phrases. '백조가 연못에 있지만 나는 안봐' has its [implicit] object pronoun dropped for its second verb when translated through Google, but not when translated through Papago.
Yetanfou 9y ago
Google Translate - and all the other translation engines bar some experimental ones - are a step in the right direction. No, they do not translate like a human translator does as they are better compared to transpilers than translators. Be that as it may the emergence of the likes of Google Translate has made it possible for just about anyone who can read and write to get the gist of what is written in another language without needing to get outside help. While the translation might be rickety it generally is possible to get what was written. The next step will be taken when the technology is ready for it, no sooner. Giving the rate at which machine learning or 'AI' is being pushed it won't be that long before human translators are taken out of the loop for most tasks. They'll still have a job translating literature and some legal texts [1] but most business communications will be translated by machine. [1] even though legal texts should be a prime candidate for machine translation as they are written in something resembling human byte code to start with.
jhanschoo 9y ago
In defense of Google Translate, I will have to point out two constraints of machine translation that human translation usually do not face. 1. Machine translation lacks a direct conception of the physical world: it only understands the "grammar" imposed by physical constraints indirectly through digitized verbal corpora and hand-constructed parameters. 2. Machine translation does not have the luxury of understanding their target audience's domain knowledge. Much of the jargon Google Translate does not understand comes from very specific situations that few people generally experience, e.g. Pan-Germanic. Normally, if such words were used in a news article, the journalist should have to spend a sentence or two describing what is meant by using those words. If Google Translate was tuned to favor translating jargon into jargon, it is likely that its translations would contain much jargon from various domains of experience and be very difficult to read in general.
- discreteevent 9y ago
  3. Even 'just' acting as a translator, Douglas Hofstadter is a high bar for intelligence, not to mind artificial intelligence.
vanderZwan 9y ago
>> “South study walking” is not an official position, before the Qing era this is just a “messenger,” generally by the then imperial intellectuals Hanlin to serve as. South study in the Hanlin officials in the “select chencai only goods and excellent” into the value, called “South study walking.” Because of the close to the emperor, the emperor’s decision to have a certain influence. Yongzheng later set up “military aircraft,” the Minister of the military machine, full-time, although the study is still Hanlin into the value, but has no participation in government affairs. Scholars in the Qing Dynasty into the value of the South study proud. Many scholars and scholars in the early Qing Dynasty into the south through the study. > Is this actually in English? Of course we all agree that it’s made of English words (for the most part, anyway), but does that imply that it’s a passage in English? To my mind, since the above paragraph contains no meaning, it’s not in English; it’s just a jumble made of English ingredients—a random word salad, an incoherent hodgepodge. > In case you’re curious, here’s my version of the same passage (it took me hours) I stopped reading here for now, to avoid having his translation affect what I am about to do. What Hofstadter doesn't really go into is that I can still manage to extract some information from the machine translation, compared to none for the original Chinese. Not only that, interpreting machine translations itself is a skill. In a sense, instead of learning a second language, one learns to translate poorly machine translated English. Of course, one can still ask whether that's a good thing or not. Here's my attempt: > “South study walking” is not an official position, before the Qing era this is just a “messenger,” generally by the then imperial intellectuals Hanlin to serve as. “South study walking” is GT's best attempt at labelling an unofficial position taken by intellectuals, comparable to being a messenger for the emperor. > South study in the Hanlin officials in the “select chencai only goods and excellent” into the value, called “South study walking.” It was a position only available to highly-qualified <Hanlin officials>. Quick google search for "Hanlin": The Hanlin Academy (Chinese: 翰林院; pinyin: Hànlín Yuàn; literally: "Brush Wood Court"; Manchu: bithei yamun) was an academic and administrative institution founded in the eighth-century Tang China by Emperor Xuanzong in Chang'an. https://en.wikipedia.org/wiki/Hanlin_Academy https://en.wikipedia.org/wiki/Hanlin_Academy .. so people from Hanlin academy, suggesting the position was administrative in nature. > Because of the close to the emperor, the emperor’s decision to have a certain influence. The position was close to the emperor, giving those who held it some influence over him. > Yongzheng later set up “military aircraft,” the Minister of the military machine, full-time, although the study is still Hanlin into the value, but has no participation in government affairs. "Study is still Hanlin" is likely referring to the “South study walking” position, since we established the connection to Hanlin earlier. With that, this reads as: Yongzheng set up a ministry of defence, which meant the position was excluded from direct government affairs, although there was still value in having the position. > Scholars in the Qing Dynasty into the value of the South study proud. Many scholars and scholars in the early Qing Dynasty into the south through the study. Many scholars in the Qing Dynasty have taken the position of "south study walking", and it was a prestigious position. I'm sure this is terrible, full of errors, and even the information I correctly inferred undoubtedly misses a lot of nuance, but again: it gives some sense of the information the original passage contains. So here is Hofstadter's translation. >> The nan-shufang-xingzou (“South Study special aide”) was not an official position, but in the early Qing Dynasty it was a special role generally filled by whoever was the emperor’s current intellectual academician. The group of academicians who worked in the imperial palace’s south study would choose, among themselves, someone of great talent and good character to serve as ghostwriter for the emperor, and always to be at the emperor’s beck and call; that is why this role was called “South Study special aide.” The South Study aide, being so close to the emperor, was clearly in a position to influence the latter’s policy decisions. However, after Emperor Yongzheng established an official military ministry with a minister and various lower positions, the South Study aide, despite still being in the service of the emperor, no longer played a major role in governmental decision-making. Nonetheless, Qing Dynasty scholars were eager for the glory of working in the emperor’s south study, and during the early part of that dynasty, quite a few famous scholars served the emperor as South Study special aides. Well, that definitely reads a lot better, but I wasn't that far off in terms of meaning of the text. And it didn't take me hours (writing this comment took a long time though). I absolutely agree that human translation by experts is an art, that it produces much, much better results, and that we should not let it be devalued. But the value in getting a quick impression, even if flawed, through machine translation should not be undervalued either. It has a very different application. On social platforms, for example, it is the difference between being completely out of the loop of a conversation or still somewhat following it and being able to ask a question for clarification in a shared language.
bambax 9y ago
I sell things on Amazon European platforms (UK FR DE ES IT) and they receive reviews. I only speak English and French and therefore for all other languages I try to understand the reviews using Google Translate. The result is never good. Sometimes it's barely intelligible; many times it's not really. (For some reason, GT is incapable of translating Italian; by which I mean: the resulting translation gives you no idea whatsoever about the original meaning.) It really makes one wonder if the hype/fear about AI is maybe misplaced. Google is a self-described "AI company", with all the money in the world, and staffed with the best people, and access to the most data, and this is all they can come up with? The only explanation is that the problem is simply too hard.
- narag 9y ago
  Do you happen to sell slippers? Maybe the problem is not too hard, just that statistical models are not the right way to solve it. Actually it seems like it's THE problem that exemplifies the difference between partial problems that are suitable for stats (bayesian spam filters) and the "true" AI. Also using English as an intermediate step is a no-no. I doubt anything will improve while there isn't a competitor that tries a fresh approach and gets better results.
  - bambax 9y ago
    > Do you happen to sell slippers? Not at all, why? Do you need any? ;-) Or is it a known difficult term to translate?
    - narag 9y ago
      Both. It's a kind of shoe in Spain forty years ago. Also the word might be getting obsolete, so yes difficult to source: https://www.calzadoslobo.com/tienda/bambas/bamba-de-lona-con-cordones-made-in-spain https://www.calzadoslobo.com/tienda/bambas/bamba-de-lona-con... There was a time when a lot of people in the irc added an "x" to their nicks in the fashion of unix, linux, aix, hp-ux... I can't help to make the association every time :)
  - deleted 9y ago
Stranger43 9y ago
It might be worth looking into the pidgin languages that tend to emerge when two cultures meet and start to trade without the intervention of a officially appointed/educated class of arbitrators i.e. machine translated text might be seen as a kind of pidgin language thats not really any of the two languages in question.
EvgeniyZh 9y ago
And what is "real understanding"?
lukas099 9y ago
> The bailingual engine isn’t reading anything—not in the normal human sense of the verb “to read.” It’s processing text. The symbols it’s processing are disconnected from experiences in the world. It has no memories on which to draw, no imagery, no understanding, no meaning residing behind the words it so rapidly flings around. I had forgotten how edifying Hofstadter's writing was about topics such as this.
Manishearth 9y ago
The 锺书 example matches my experiences with Chinese through Google Translate. Chinese is a language where you'll often redundantly say things because each character may have (a) multiple meanings and (b) a lot of homophones, even accounting for tone. So the word for vegetable(s) is "蔬菜", which really is two different words for "vegetable" put together. I suspect that the engine has "learned" that words can be thrown away sometimes. Which leads to fun cases where it tells you the literal opposite of what you said. An example is in https://twitter.com/ManishEarth/status/919434569446776832 https://twitter.com/ManishEarth/status/919434569446776832 -- it has since been fixed. I bet what happened with 锺书 was that Translate decided to throw away 锺 in one of the cases because it didn't know what to do with it, leaving it with 书 ("book"). ------------- Regarding the anecdote about the Danish-speaking friend, this isn't too uncommon. For example, I natively speak Marathi but spoken/written Marathi differ a lot and I don't have much practice reading/writing, so I make spelling/grammar mistakes. So using Google Translate as a crutch (and then modifying/verifying the output) is great. Though I've found out that it's not really good at non-EU languages (The EU translates all of its documents into the languages of its members so this forms an excellent corpus of professional translations for Google Translate) and that my written Marathi is often better. ------------- My favorite example of Google Translate limitations is what happens if you ask it to translate "Yes." and "No." to Chinese. You get the answer 是 and 没有, which literally translate to "am" and "don't have". (there are no conjugations and kind of no tenses, so by "am" I mean "whatever conjugation of the English word 'to be' makes sense in this context") The thing is, Chinese doesn't really have words for Yes and No. 是的/不是 sometimes works (I think this is more because there are implicit 是s in verbless sentences). But the basic idea is that if you want to answer a question, you repeat the verb. "Do you eat meat?" "Don't eat." or "Do you read books?" "Read.". 是 and 有 are pretty common (as are "to be" and "to have" in English), so it seems like it picked up on the most common yes/no answers. For some reason, it picked a different one for no than it did for yes. Now, this isn't exactly an example of the machine translation sucking, but more of the translation not having an outlet to express ambiguity at all. The article gives examples involving this as well, but the basic idea is that some languages do not allow for the same kind of ambiguity that others do. You should be able to display something about that when translating from an ambiguous term to a specific one. Yes/No is an example of a set of ambiguous terms in English where Chinese has no similar ambiguous terms. In the other direction, as I already explained, 是 (or any verb!) in Chinese is an example of an ambiguous term that doesn't translate to English because the tense need not be fully specified (Chinese does tense from context, and even then it may not be as fully specified as it is in english), and English requires you to handle tense.
cs702 9y ago
The author is Douglas Hofstadter, author of Gödel, Escher, Bach.[a] In this article, he shows with concrete examples how Google Translate falls short, and then offers two criticisms: * Feeding more data to current models won't bring them any closer to understanding, since understanding involves having ideas (including ideas about the state of the world), and this lack of ideas is the root of all the problems for machine translation today. His examples are powerful evidence of this limitation of current state-of-the-art machine translation systems. * Current machine translation systems make no attempt to go beyond the surface level of words and phrases. These systems merely discover statistical regularities that relate words to other words at multiple, hierarchical levels of composition. In Hofstadter's words, "there's no attempt to create internal structures that could be thought of as ideas, images, memories, or experiences. Such mental etherea are still far too elusive to deal with computationally." He is right. That said, AI researchers are aware of these limitations, and are exploring possible ways to overcome them. An early, crude example of such research is the multi-modal model of "One Model to Learn Them All" (https://arxiv.org/abs/1706.05137 https://arxiv.org/abs/1706.05137), which is trained to learn to perform multiple image-recognition, language-translation, image-captioning, speech-recognition, and language-parsing tasks at the same time, using representations that are shared by all tasks. While these early research efforts fall far short of the kind of "understanding of the world" Hofstadter shows is necessary for human-level language translation, it's encouraging to see AI researchers actively looking for ways to move beyond the mere discovery of 'hierarchical statistical regularities' that relate words to other words. This is an exciting time for AI research. [a] https://en.wikipedia.org/wiki/Douglas_Hofstadter https://en.wikipedia.org/wiki/Douglas_Hofstadter
- brd 9y ago
  I was under the impression that modern word embedding techniques have emergent properties that start to approach conceptual understanding. The trick is figuring out how to leverage that property properly. I believe Google Brain has a demo where they show translation between two languages without having any corpus specifically linking the two (i.e. the system learned English/Japanese translations and English/Greek translations and then translated Greek/Japanese) with a reasonable degree of accuracy. This seems to me to hint at a more conceptual understanding as opposed to a simple pattern matching activity. In either case, I 100% agree that the multi-modal model is the path forward. It feels like there's a lot of low hanging fruit in the area of model ensambles.
Houshalter 9y ago
These NNs have fewer neurons than an insect brain. They have to use so little computing power they can be provided for free to everyone in the world. Of course they have limitations. But there's been exponential progress in the last few years, and it's amazing they can do as much as they do.
btrettel 9y ago
Having used Google Translate to translate at least 6 Russian academic papers in full and about 40% of a Russian dissertation (along with a far larger number of partial translations of papers), I can say that Google Translate works decently for this task. The result won't win any awards for elegance, but it's by large intelligible. I submitted many corrections over the past years and that seems to have noticeably improved the quality, at the very least reducing the number of gibberish sentences. My contributions are limited to fluid dynamics, so perhaps they won't generalize, but I am very happy with the results. Google in particular had problems with technical phrases which it translated literally, where the literal translation does not correspond to the equivalent English phrase. In one case there was no equivalent English phrase and Google Translate returned a phrase that seemed like it had a Latin etymology. After reverse engineering the word based on the Latin, I recognized the concept and found it interesting that there was a Russian word for this. If I recall I added a footnote explaining the word. Anyway, I doubt your average human translator without subject knowledge could do this. So I don't blame Google Translate for this too much. I have a list of problem sentences that I'll take to a human translator some time in the future. Machine translation has been convenient but does not yet replace human translation even when one sets a fairly low bar as I do.
- petra 9y ago
  How did you find interesting articles in Russian ? And I assume they contained knowledge not available in English ?
  - btrettel 9y ago
    The articles absolutely contained information not available in English. I would not have translated the articles in full if they had, as my time is limited. My specific field is the breakup of liquid jets into droplets. As an example, I found a paper that applied the maximum entropy principle to the prediction of droplet size distributions in 1938 (by V. Ya. Natazon), almost 50 years before the approach was popularized for droplet size in the west (and before Jaynes). I actually was not attracted to the paper for that, though. The specific constraint used involved a measure of the strength of the turbulence, and that was what attracted me as my own research was going in that direction. The constraint would be regarded as novel if it were published in a western journal today. I am working on a conference paper right now which cites this. As for how I find the articles, there are many approaches. I found the Natanzon paper by reading an old English translation of a Russian textbook. Textbooks can be particularly fruitful in terms of foreign references. I also found bibliographies useful to find citations along with English abstracts (though few are recent). Going backwards through the citation network for a particular foreign paper you find interesting can also return good papers. It's also worth noting that in the Cold War era many translations were produced, though they may be hard to track down today. I only translated articles where no previous translation existed best I could tell. I could write more on identifying whether an article was translated and tracking down the translation if you are interested.
cannam 9y ago
In 1968 the Sunday Times ran a competition to translate a poem by Baudelaire ("Je suis comme le roi d'un pays pluvieux") into English. The poet Nicholas Moore read about the competition and, apparently angered by the fruitlessness of the task, entered it 31 separate times with 31 different poems, many submitted pseudonymously and in the styles of other poets. One of them even begins "I'm like the Winner of the Competition / the one who wrote the strong, rewarding phrase..." None of his entries won, but his anger ("All I have against translation is that it can't be done!") carried him a surprisingly long way. You can read his entries (and the original) here http://www.ubu.com/ubu/pdf/moore_spleen.pdf http://www.ubu.com/ubu/pdf/moore_spleen.pdf
kccqzy 9y ago
The author makes one point that is wrong. The current deep learning version of Google Translate specifically didn’t all of the available corpus and data that the old translation uses, simply because the new engine takes much longer to train with the all the data. Google internally looked at statistics and decided that the new engine is aleeady better so there’s no need to feed it more data. I believe they decided that a reasonable time for training (around two weeks IIRC) is more important. So any want of quality is definitely a pragmatic choice made by Google, but not for want of data.
anotherevan 9y ago
He never did explain why Frank and his Danish friend used Google Translate despite both being fluent in each other's native languages. I read right through mainly to try and find that out.
buovjaga 9y ago
Regarding the "gender blunder": https://en.wikipedia.org/wiki/Anaphora_(linguistics) https://en.wikipedia.org/wiki/Anaphora_(linguistics) I noticed Apertium is looking to solve the problem in their system: http://wiki.apertium.org/wiki/Anaphora_resolution http://wiki.apertium.org/wiki/Anaphora_resolution It's a potential GSoC project for them: http://wiki.apertium.org/wiki/Ideas_for_Google_Summer_of_Code/Anaphora_resolution http://wiki.apertium.org/wiki/Ideas_for_Google_Summer_of_Cod...
SZJX 9y ago
It already works very well, especially after its improvement in the recent years after using neural networks. I've frequently been surprised by the quality of the translation it provides to Japanese sentences, not to mention between German and English. The test cases I put in in some other languages (e.g. Chinese) are really promising too. Machine translation is a really hard problem. The messiness of language as a system, the importance of context in daily conversation etc. all play a part. Another layer of complexity is the gap between everyday usage and official, written form, which is also being tackled by researchers. You have to put this thing into perspective. Many of the old rule-based/Chomskyan software have been simply unusable for decades. New statistical approaches have been in use for barely 10 years, and industrial deep learning less than half a decade. There are still much more to come. The hype IMO is well justified.

217 points by ehudla 9y ago

DRH's /Le Ton Beau De Marot: In Praise Of The Music Of Language/ is a remarkable book about translation of literature (all of his books are must reads, btw). Still, it seems to me that shallowness is a feature, not a bug, of automatic translation.

ColinWright 9y ago

For those who might be interested to see the discussion by the HN on this article, it's been submitted and discussed before: https://news.ycombinator.com/item?id=16287171 https://news.ycombinator.com/item?id=16287171 (23 comments) https://news.ycombinator.com/item?id=16267363 https://news.ycombinator.com/item?id=16267363 (12 comments) There are other submissions without comments, showing that the article is of interest: https://news.ycombinator.com/item?id=16285196 https://news.ycombinator.com/item?id=16285196 https://news.ycombinator.com/item?id=16279656 https://news.ycombinator.com/item?id=16279656 https://news.ycombinator.com/item?id=16265302 https://news.ycombinator.com/item?id=16265302 https://news.ycombinator.com/item?id=16294491 https://news.ycombinator.com/item?id=16294491 https://news.ycombinator.com/item?id=16296792 https://news.ycombinator.com/item?id=16296792

dTal 9y ago
1 day ago, 3 days ago... there is a serious problem with the short attention span of HN. No sensible forum should split threads like that. Some interesting discussions take longer than a day to execute, and there is no reason why a thread could not be alive for years, but HN actively discourages it - there is no interface for discovery besides the novelty firehose of frontpage+new, and voting is forbidden after a certain age (possibly replying? I've never tried).
- ColinWright 9y ago
  I see this as an inevitable consequence of several deliberate design features. It's a link-sharing site, and people get karma for popular links. The votes serve to make the items rise on the front page, getting more votes, hence more karma. So people will rush to be first, and there is no serious mechanism to check whether the same story, or even the nearly exact same link has been submitted before. But anything more sophisticated will be more complex, harder to understand, more fragile, and possibly less popular. It's not hard to see several things that might make it better, but it's uncertain, more work, and guaranteed to make the system less transparent. <fx: shrug /> To some extent it is what it is. But FWIW, I agree.
  - cheschire 9y ago
    Reddit does a simple link comparison to see if the article has been submitted before, which does limit the double-posts, however some people then intentionally subvert the process by running the link through a processor like outline.com or archive.is, or simply finding alternate articles from other sources. This is unfortunate as the thing I really want to see is an in-depth conversation about a topic more than an individual article.
    - jwilk 9y ago
      HN does that too.
- dang 9y ago
  It's not all that common that threads get split like that, and we often merge them when they do (see https://hn.algolia.com/?sort=byDate&dateRange=all&type=comment&storyText=false&prefix=false&page=0&query=%22comments%20moved%22 https://hn.algolia.com/?sort=byDate&dateRange=all&type=comme..., https://hn.algolia.com/?sort=byDate&dateRange=all&type=comment&storyText=false&prefix=false&page=0&query=by:dang%20merged https://hn.algolia.com/?sort=byDate&dateRange=all&type=comme...). As for attention span, threads often go for longer than a day but rarely go for longer than a few days. That's just how the community behaves and if, as I assume, it's true of internet forums generally, there's little to be done about it.
cheschire 9y ago
Good bot.
- ColinWright 9y ago
  Er ... Thank you? Except I'm not a 'bot ...
  - cheschire 9y ago
    Yes, that was the joke.
    - ColinWright 9y ago
      Ah. It was a joke. OK.
- amake 9y ago
  Take that nonsense back to Reddit, please.
  - cheschire 9y ago
    An ironic joke that you don’t find funny doesn’t make it nonsense. I was aware he was not a bot and was simply commenting with tongue firmly planted in cheek that he seems to expend bot like effort on posts exactly like these.

olasaustralia 9y ago

Jeffrey Shallit has a response - http://recursed.blogspot.com/2018/02/doug-hofstadter-flight-and-ai.html http://recursed.blogspot.com/2018/02/doug-hofstadter-flight-...

empiricus 9y ago
I was expecting this reply to be like "yes translation is not yet perfect but we have some ideas how to fix this, and the solution is different than what Hofstadter thinks". Instead it was more like "AlphaGo is different from humans, airplanes are different from birds, and deep translation is different(better?) from human translation."
- mtgx 9y ago
  And self-driving cars may not get drunk but they'll kill you in different ways than humans do (think software errors, or hacking). He may actually have a point there.
randomsearch 9y ago
I think he’s (deliberately? Give him the benefit of the doubt) misinterpreted Hofstadter’s use of the words “processing text”. What he means, which seemed quite obvious to me, is that the machine is not reading text, building up a semantic interpretation of the sentence in the way a human does. That’s because a neural net is a simple pattern recognition machine that does not work in the same way as the brain. It doesn’t have the immense life experience to draw upon (probably relatively easy to fix) but more importantly it doesn’t have a concept of what a sentence actually means. A neural network doesn’t have an understanding of what “double” means. It just pattern matches a translation. I think there’s some serious symbolic reasoning going on in the brain, which neural nets don’t yet perform. It all feels like shallow syntactic matching right now, rather than semantic reasoning. Unfortunately I don’t know how the brain works, so you end up in an absurd argument where people say “the brain is just a neural net” and it’s impossible to completely refute their claims, just as if someone said “the brain is a very large lookup table”. Well, from what I see it does seem that the brain is much smarter than that, but I can’t be certain without knowing what that extra kicker is. So whilst such an assertion seems terribly simplistic and self-evidently insufficient, it is difficult to argue with.

ddmd 9y ago

There is a new online translator, http://deepl.com http://deepl.com, which relies on deep learning techniques and provides higher (semantic) quality and accuracy of translation. Previously I had quite positive experience with http://translate.yandex.com http://translate.yandex.com (but I had to manually compare and combine their results with google translate).

nicois 9y ago
I just tried the following, to German, and got the predictable result, which is completely wrong. Until the proper semantic parts of the text are identified, these sorts of mistakes prevent statistical methods from being trustworthy: Sailing ships in rough seas is asking for trouble
- romseb 9y ago
  Put a period at the end of the sentence and it changes from > Segelschiffe in rauer See verlangen Ärger. to > Segelschiffe in rauher See sind ein Problem.
  - Freak_NL 9y ago
    The translating software can't distinguish between 'sailing ships' the noun, and 'sailing' (verb) 'ships' (noun) the act. The latter is what is probably intended. It is also missing chances of using idiomatic expressions in the translations.
    - claudius 9y ago
      Well, 'is' is singular so it can’t be the correct verb associated to 'sailing ships' if the latter means multiple sailing ships rather than the activity of sailing ships.
- ddmd 9y ago
  I like that I can clikc a word in the target box and choose an alternative translation while the system updates the result accordingly. Previously, I had to play with different translation alternatives manually in Google or Yandex translate. I think deepl also learns from such user choices.

mehrdadn 9y ago

Related heads-up: if you ever want to use or test Google Translate, don't make the mistake of assuming Gmail's translator would return its results. I have no idea what it uses, but it sure isn't the same Google Translate. I've seen it produce output far inferior to that of Google Translate for the exact same text.

chx 9y ago

Ho humm. Of course he is right but is that enough? This reminded me of a quote from Doctorow's Microsoft Research DRM talk which I remember well because I translated (hah) it to Hungarian as my last act as a Hungarian journalist -- I sort of resurrected myself as one because by that point I haven't written in years. http://craphound.com/msftdrm.txt http://craphound.com/msftdrm.txt > This is the overweening characteristic of every single successful new medium: it is true to itself. The Luther Bible didn't succeed on the axes that made a hand-copied monk Bible valuable: they were ugly, they weren't in Church Latin, they weren't read aloud by someone who could interpret it for his lay audience, they didn't represent years of devoted-with-a-capital-D labor by someone who had given his life over to God. The thing that made the Luther Bible a success was its scalability: it was more popular because it was more proliferate: all success factors for a new medium pale beside its profligacy. The most successful organisms on earth are those that reproduce the most: bugs and bacteria, nematodes and virii. Reproduction is the best of all survival strategies. If you want the Hungarian one: https://www.hwsw.hu/hirek/40796/a-digitalis-jogkezelo-rendszerek-kritikaja.html https://www.hwsw.hu/hirek/40796/a-digitalis-jogkezelo-rendsz...

csydas 9y ago
I think Doctorow's point is dramatically different here. He's talking about a change in medium and specifically arguing against the claims about the original experience offered by DRM controlled media (e.g., theatres, ebooks, etc). The article's point is more about while Google Translate has its impressive moments, it needs to be understood for what it is - the most basic translation services. If you've ever had to rely on Google Translate (or any automated translate service) you'll know what I mean by "basic translation services". It's great if you want to say "I want to order Fish and Rice" or "Excuse me, where is the restroom". The moment you try to get past such complexities is where the automated translations just can't get a good grip on human languages. Languages from the same families naturally are a bit easier (English to Spanish, for example usually can allow for more complexities), but you still lose a lot simply because it's a 1:1 translation from the box on the left/top to the box on the right/bottom, which doesn't happen. I'm learning Russian (in Russia no less) and one of the hardest things for me as a native english speaker is avoiding the use of "to be", which is almost never used in Russian. There are other oddities as well regarding prepositions in Russian that are handled differently than English (my poor teacher still can't satisfactorily explain to me why stuff is "on the kitchen", but it's "in the ___" for other rooms) (в гостинной versus на кухне). Now, surprisingly Google translate gets these two right, but other such preposition specialties it bungles completely, and I've had more than my fair share of confused looks when I have to resort to using translate for more complex sentences. Especially when it comes to things like words of motion, for which there are tons of different words depending on how you're going. It's good that it's accessible, but when the reverse happens (russian to english), for me it requires a fairly fast sed operation to fix Google Translate's choices for words or just some additions to help get what the person is wanting. Edit: Addendum to the main point, I can see that there may be applicability in that Translate Tools will allow for pidgin languages to crop up if it receives wide spread usage in real world settings, but I'd be curious to see from a large sample how many people use the conversation style features. I'm sure there's plenty of anecdata on the subject from those who have to use it (myself included), but I wonder if it seems as well used if you increase the sample size out further. Globally, people still pretty much stay at home and/or know enough english to get by.

paganel 9y ago

I heartily recommend Umberto Eco's text "Experiences in Translation" (full text here, a little long: https://archive.org/stream/UmbertoEcoEXPERIENCESINTRANSLATION/Umberto%20Eco%20EXPERIENCES%20IN%20TRANSLATION_djvu.txt https://archive.org/stream/UmbertoEcoEXPERIENCESINTRANSLATIO...), which perfectly describes the difficulty (and I'd say beauty) of the act of translation. > In this book Umberto Eco argues that translation is not about comparing two languages, but about the interpretation of a text in two different languages, thus involving a shift between cultures. The above short presentation of the book says it all, basically, but surprisingly enough is really hard to understand for lost of technical people who only think that translation is just "de-coding", as Hofstadter explains. We won't have proper translations until AGI is here.

jmadsen 9y ago

I use Google Translate extensively to help write things in Japanese - considered (one of) the most difficult languages in the world. I show the results to Japanese people who nearly always tell me it looks just fine to them. I have also passed off translations as my own work on tutoring sites like Lang8 and had natives correct "my work". They often will give a slightly different wording, but I have never had an "WTF does this mean?" type response. ---- I think it is important to distinguish the difference between a perfect translation with nuance, and a simple "I need to make this point" translation that is what we need most of the time.

lifthrasiir 9y ago
> [...] Japanese - considered (one of) the most difficult languages in the world. Just in case, one of the most difficult languages to learn by native English speakers. It is pretty easy for Koreans for example---they share the same sentence order (subject-object-verb) and many words frequently share the etymology due to throughout Chinese influence. More quantitively, Idibon had once catalogued [1] the weirdness of natural languages compared to others. English ranked 33rd place out of 239 languages. Quoting the original, "[p]art of this is to say that some of the languages you take for granted as being normal (like English, Spanish, or German) consistently do things differently than most of the other languages in the world". [1] https://corplinguistics.wordpress.com/2013/06/21/the-weirdest-languages/ https://corplinguistics.wordpress.com/2013/06/21/the-weirdes... (the original link is dead)
2muchcoffeeman 9y ago
>I think it is important to distinguish the difference between a perfect translation with nuance, and a simple "I need to make this point" translation that is what we need most of the time. Isn't this exactly the point though? A different language in a different culture would convey an idea in a different way. I'd love to know what exactly you're translating. I have experience with translations from work. I've never met a native speaker say that a translation was any good. Usually, "I understand the meaning but you would never say it that way."
vadimberman 9y ago
Try going the other way around, and read the machine-translated result yourself.

YeGoblynQueenne 9y ago

For the past 5 years or so, as a kind of benchmark, I've been checking how Google translates a particular word from Greek, to French. The word is "χελιδόνι", meaning "swallow" (the bird) in Greek. For as long as I've been trying this tiny little experiment, Google has been translating it to the French word "avaler"- the verb "to swallow". https://translate.google.com/#el/fr/%CF%87%CE%B5%CE%BB%CE%B9%CE%B4%CF%8C%CE%BD%CE%B9 https://translate.google.com/#el/fr/%CF%87%CE%B5%CE%BB%CE%B9... Once in a while, a different translation appears- "machaon", which is a kind of butterfly, the Old World swallow-tail. This is slightly closer but still absurd. If GT can make the connection to "swallow-tail", how can it not see the connection to the bird, "swallow"? The problem seems to be that, in order to translate from Greek to French, Google goes via English. A great big chunk of context is lost in the process, especially since now the translation between two languages that have different forms for male and female nouns goes through a third language that does not. So for example, I've seen the same gender-reversal as Hofstadter reports in his article. The following string in Greek means "I saw my teacher and she said hello to me". Είδα τη δασκάλα μου και μου είπε γειά. The following are the French and English translations by GT: J'ai vu mon professeur et il m'a dit bonjour. I saw my teacher and he said hello. This seems to happen because Google's English language model has learned that "he" is found in the context of "teacher" and "said" more often than "she" is. But of course, such a statistical association is, well, meaningless and for that useless when it comes to translation, where you actually need to know when the least likely case is correct. Generally, it looks like the complete abandonment of any attempt at representing meaning, and relying instead on text statistics to do meaning-intensive work like translation, is producing a lot of nonsense. And that's not a criticism of Google Translate only. I think professor Chomsky might "win" that old debate with prof. Norvig, after all.

dragandj 9y ago
Are you sure that this particular translation is "nonsense"? To me, it seems almost perfect. The only mistake in the French version is the gender of the teacher, which is a mistake that many humans can and do make, in addition to many other mistakes...
- puszczyk 9y ago

babuskov 9y ago

I don't know what translation engine Bing is using, but I find that combining Google Translate and Bing results gets me pretty close to the real thing. I wish teams making those two could work together :(

nabla9 9y ago

Hofstadter's strength is his ability to stay focused on the highest level cognition problem. His theory is that analogies are the core of human cognition. His main work revolves around discovering how extensive human ability to think using analogies is. I especially like his attempts to understand and capture the high level cognition in simple toy problems. His `copycat` and `Letter Spirit` programs illuminate the problem and his thinking in very clever way. The current AI and ML research is building things bottom up and there is still significant gap before we reach the high level cognition that Hofstadter is interested in. What is the representation that binds low level and high level cognition and allows high level fluid concepts to be used as analogies from one domain to next with just one or two examples? Style transfer, variational autoencoders and transfer learning are very limited in this regard. Challenges for deep learning: * Deep Letter Spirit. Show 1-3 examples of lowercase letters of the roman alphabet in some font and have an algorithm that understands the style and completes the rest of the alphabet in the same style. * Bongard problems solver.

pooya72 9y ago

On the other hand, it's interesting how well Google Translate translates philosophical text. Just compare the translations below. Gadamer's text from Truth and Method: Die folgenden Untersuchungen haben es mit dem hermeneutischen Problem zu tun. Das Phänomen des Verstehens und der rechten Auslegung des Verstandenen ist nicht nur ein Spezialproblem der geisteswissenschaftlichen Methodenlehre. Es hat von alters her auch eine theologische und eine juristische Hermeneutik gegeben, die nicht so sehr wissenschaftstheoretischen Charakters waren, als"vielmehr dem praktischen Verhalten des durch die Wissenschaft ausgebildeten Richters oder Pfarrers entsprachen und ihm dienten. Weinsheimer and Marshall translation: These studies are concerned with the problem of hermeneutics. The phenomenon of understanding and of the correct interpretation of what has been understood is not a problem specific to the methodology of the human sciences alone. There has long been a theological and a legal hermeneutics, which were not so much theoretical as corrolary and ancillary to the practical activity of the judge or clergyman who had completed his theoretical trainin Google translate: The following investigations have to do with the hermeneutic problem. The phenomenon of understanding and the right interpretation of the understanding is not only a special problem of the humanistic methodology. There has also been a theological and juridical hermeneutics from ancient times, which were not so much scientific-theoretical in character as they corresponded to and served the practical behavior of the scientist or pastor trained by science.

ACow_Adonis 9y ago
Cynically, i wonder if a translation of that text was actually fed in/available to the translation process. I mean texts already translated into other languages would presumably be one of the most obvious things to feed in as training data.
- PeterisP 9y ago
  There's no need to wonder, one can be virtually certain that every "classic" work that has been translated to many languages is included in the training data of major translation systems. MT world runs on data, it includes as much as it can, and a book translation wouldn't be included only if its translations are not readily available in digital form - and pretty much all famous books have been digitized.

larrysalibra 9y ago

At least Google Translate is a bit better than Baidu Translate which at some point decided that my name is the English translation of 扒饭 (grilled rice). 2 years ago, I appeared on the menu of a restaurant on Huawei's campus in Shenzhen because someone apparently used Baidu Translate to translate the menu to English: https://twitter.com/larrysalibra/status/959749866036408320 https://twitter.com/larrysalibra/status/959749866036408320 And 2 years later, I'm still grilled rice: http://translate.baidu.com/#zh/en/扒饭 http://translate.baidu.com/#zh/en/扒饭 Human language is hard!

napsterbr 9y ago
That is... Bizarre. Any idea how that happened? Maybe you tweeted "grilled rice" before?
- larrysalibra 9y ago
  No idea. Ha!
CapsAdmin 9y ago
Here it shows "Larrysalibra" or "Grilled meal" if I put a newline between the two characters.
- brandonhorst 9y ago
  If you put a newline in the middle of a word English, it changes the meaning too.
kiwidrew 9y ago
Wow, that's awesome, the only way it could be more surreal is if you had been holding the menu in front of you! "扒饭" isn't, on its own, an actual Chinese word. The "扒" is basically a modifier that comes after a type of meat and indicates that it is a "steak" or "cutlet". So your menu item "美式杂扒饭" should translate as "American-style" (美式) "mixed grill" (杂扒) "on rice" (饭)... but the machine translation is getting the word boundaries wrong and translating it as "American-style" (美式) "mixed" (杂) "larrysalibra" (扒饭). Fascinating.

87ert3746 9y ago

What's going to happen is that we humans are going to become more machine-like, as we loose our appreciation of our own sophistication & subtlety, so that most people will be satisfied with machine-translation, writing, art and music never having known that there are higher levels

jvvw 9y ago

My sister is a professional translator and when companies pay her they generally do it because they want really good translations not because they just want people to understand the content. This can be for semi-legalistic reasons (though she's not a legal translator per se, but she has done a fair bit of work related to the EU) or for reasons of professional reputation. Literary translation, although not her mainstay, is also obviously really hard to do well. She's not worried about Google Translate at all yet, although she does have the advantage that one of her languages is rare for native English speakers and she is an extremely good writer in English.

YeGoblynQueenne 9y ago
I have a cluster of friends who are professional translators and interpreters and the translators have complained to me a lot about a machine translation program called Trados, that they are pretty much forced to use in their day-to-day jobs. The complaint I heard was that translation agencies hire translators with contracts that state that 80% of the translation work will be performed by Trados and the human translator will only be paid for 20% of it (I obviously don't know exact numbers!). Of course, in practice, Trados' translations are nowhere near 80% of the work needed to translate some text, to the point that the human translator ends up doing the bulk of the work and being paid for only a small fraction of it. It looks like translators are already in trouble with machine translation software, even if they can still do the job way better than that software. I think we can expect to see this pattern a lot more often in the future: automation taking over jobs not because it's better at them, but because it's more profitable that way.
- petepete 9y ago
  The effectiveness of Trados or other MT software is entirely dependent on the quality of the glossaries and translation memories. Both of these are built up over time, the bigger and better (and more specific) they are, the less the translator has to be involved. If independent, having their own glossaries and TMs boost their efficiency and productivity. If they are working on the 20%, improving the company TMs is, in the short term, eating their own profit. In the long term, they are training their own replacement.

Hendrikto 9y ago

> I am not in the least eager to see human translators replaced by inanimate machines. Indeed, the idea frightens and revolts me. To my mind, translation is an incredibly subtle art that draws constantly on one’s many years of experience in life, and on one’s creative imagination. Yeah yeah... the special snowflake argument. „No, I am irreplacable, my job is special.“ Sure, machine translation isn‘t very good atm, but it‘s getting better, and I see no reason whatsoever why it shouldn‘t reach human levels with the correct technique.

Eliezer 9y ago
Douglas R. Hofstadter is a tad more than a translator. Try Google.
3pt14159 9y ago
Do you speak a second human language that’s not closely related to your first? English and Russian, say? Because I find most data scientists that do tend to think language translation may be one of the last things we solve. I think it requires hard AI, honestly.
- golergka 9y ago
  I speak three: Russian (native), English (mistaken for native speaker by native speakers) and Hebrew (average). I may not be an expert in AI though (couple of courses, hobby projects and worked alongside AI researchers on the non-ML side of some apps). Still, looking at the methods we already have and recent successes (like Yandex's linguistic work), it doesn't like as hard a problem as hard AI with human-like cognition.
- jpalomaki 9y ago
  Maybe look at the progress we have made in last twenty years? Yes, there are examples of machine translation failing miserably, but it has also reached a level which allow people to actually use it for communication (as evidenced in real life and mentioned in the article). I think that's already quite an achievement. We should not be too harsh on computers. Humans are not perfect either. Instead of judging machine translation based on few random cases, we should be evaluating against number of human translators of various experience levels.

half-kh-hacker 9y ago

I've found, for Korean, at least, Naver Papago is miles ahead of Google Translate - Especially while decoding some unnatural phrases. '백조가 연못에 있지만 나는 안봐' has its [implicit] object pronoun dropped for its second verb when translated through Google, but not when translated through Papago.

Yetanfou 9y ago

Google Translate - and all the other translation engines bar some experimental ones - are a step in the right direction. No, they do not translate like a human translator does as they are better compared to transpilers than translators. Be that as it may the emergence of the likes of Google Translate has made it possible for just about anyone who can read and write to get the gist of what is written in another language without needing to get outside help. While the translation might be rickety it generally is possible to get what was written. The next step will be taken when the technology is ready for it, no sooner. Giving the rate at which machine learning or 'AI' is being pushed it won't be that long before human translators are taken out of the loop for most tasks. They'll still have a job translating literature and some legal texts [1] but most business communications will be translated by machine. [1] even though legal texts should be a prime candidate for machine translation as they are written in something resembling human byte code to start with.

jhanschoo 9y ago

In defense of Google Translate, I will have to point out two constraints of machine translation that human translation usually do not face. 1. Machine translation lacks a direct conception of the physical world: it only understands the "grammar" imposed by physical constraints indirectly through digitized verbal corpora and hand-constructed parameters. 2. Machine translation does not have the luxury of understanding their target audience's domain knowledge. Much of the jargon Google Translate does not understand comes from very specific situations that few people generally experience, e.g. Pan-Germanic. Normally, if such words were used in a news article, the journalist should have to spend a sentence or two describing what is meant by using those words. If Google Translate was tuned to favor translating jargon into jargon, it is likely that its translations would contain much jargon from various domains of experience and be very difficult to read in general.

discreteevent 9y ago
3. Even 'just' acting as a translator, Douglas Hofstadter is a high bar for intelligence, not to mind artificial intelligence.

vanderZwan 9y ago

>> “South study walking” is not an official position, before the Qing era this is just a “messenger,” generally by the then imperial intellectuals Hanlin to serve as. South study in the Hanlin officials in the “select chencai only goods and excellent” into the value, called “South study walking.” Because of the close to the emperor, the emperor’s decision to have a certain influence. Yongzheng later set up “military aircraft,” the Minister of the military machine, full-time, although the study is still Hanlin into the value, but has no participation in government affairs. Scholars in the Qing Dynasty into the value of the South study proud. Many scholars and scholars in the early Qing Dynasty into the south through the study. > Is this actually in English? Of course we all agree that it’s made of English words (for the most part, anyway), but does that imply that it’s a passage in English? To my mind, since the above paragraph contains no meaning, it’s not in English; it’s just a jumble made of English ingredients—a random word salad, an incoherent hodgepodge. > In case you’re curious, here’s my version of the same passage (it took me hours) I stopped reading here for now, to avoid having his translation affect what I am about to do. What Hofstadter doesn't really go into is that I can still manage to extract some information from the machine translation, compared to none for the original Chinese. Not only that, interpreting machine translations itself is a skill. In a sense, instead of learning a second language, one learns to translate poorly machine translated English. Of course, one can still ask whether that's a good thing or not. Here's my attempt: > “South study walking” is not an official position, before the Qing era this is just a “messenger,” generally by the then imperial intellectuals Hanlin to serve as. “South study walking” is GT's best attempt at labelling an unofficial position taken by intellectuals, comparable to being a messenger for the emperor. > South study in the Hanlin officials in the “select chencai only goods and excellent” into the value, called “South study walking.” It was a position only available to highly-qualified <Hanlin officials>. Quick google search for "Hanlin": The Hanlin Academy (Chinese: 翰林院; pinyin: Hànlín Yuàn; literally: "Brush Wood Court"; Manchu: bithei yamun) was an academic and administrative institution founded in the eighth-century Tang China by Emperor Xuanzong in Chang'an. https://en.wikipedia.org/wiki/Hanlin_Academy https://en.wikipedia.org/wiki/Hanlin_Academy .. so people from Hanlin academy, suggesting the position was administrative in nature. > Because of the close to the emperor, the emperor’s decision to have a certain influence. The position was close to the emperor, giving those who held it some influence over him. > Yongzheng later set up “military aircraft,” the Minister of the military machine, full-time, although the study is still Hanlin into the value, but has no participation in government affairs. "Study is still Hanlin" is likely referring to the “South study walking” position, since we established the connection to Hanlin earlier. With that, this reads as: Yongzheng set up a ministry of defence, which meant the position was excluded from direct government affairs, although there was still value in having the position. > Scholars in the Qing Dynasty into the value of the South study proud. Many scholars and scholars in the early Qing Dynasty into the south through the study. Many scholars in the Qing Dynasty have taken the position of "south study walking", and it was a prestigious position. I'm sure this is terrible, full of errors, and even the information I correctly inferred undoubtedly misses a lot of nuance, but again: it gives some sense of the information the original passage contains. So here is Hofstadter's translation. >> The nan-shufang-xingzou (“South Study special aide”) was not an official position, but in the early Qing Dynasty it was a special role generally filled by whoever was the emperor’s current intellectual academician. The group of academicians who worked in the imperial palace’s south study would choose, among themselves, someone of great talent and good character to serve as ghostwriter for the emperor, and always to be at the emperor’s beck and call; that is why this role was called “South Study special aide.” The South Study aide, being so close to the emperor, was clearly in a position to influence the latter’s policy decisions. However, after Emperor Yongzheng established an official military ministry with a minister and various lower positions, the South Study aide, despite still being in the service of the emperor, no longer played a major role in governmental decision-making. Nonetheless, Qing Dynasty scholars were eager for the glory of working in the emperor’s south study, and during the early part of that dynasty, quite a few famous scholars served the emperor as South Study special aides. Well, that definitely reads a lot better, but I wasn't that far off in terms of meaning of the text. And it didn't take me hours (writing this comment took a long time though). I absolutely agree that human translation by experts is an art, that it produces much, much better results, and that we should not let it be devalued. But the value in getting a quick impression, even if flawed, through machine translation should not be undervalued either. It has a very different application. On social platforms, for example, it is the difference between being completely out of the loop of a conversation or still somewhat following it and being able to ask a question for clarification in a shared language.

bambax 9y ago

I sell things on Amazon European platforms (UK FR DE ES IT) and they receive reviews. I only speak English and French and therefore for all other languages I try to understand the reviews using Google Translate. The result is never good. Sometimes it's barely intelligible; many times it's not really. (For some reason, GT is incapable of translating Italian; by which I mean: the resulting translation gives you no idea whatsoever about the original meaning.) It really makes one wonder if the hype/fear about AI is maybe misplaced. Google is a self-described "AI company", with all the money in the world, and staffed with the best people, and access to the most data, and this is all they can come up with? The only explanation is that the problem is simply too hard.

narag 9y ago
Do you happen to sell slippers? Maybe the problem is not too hard, just that statistical models are not the right way to solve it. Actually it seems like it's THE problem that exemplifies the difference between partial problems that are suitable for stats (bayesian spam filters) and the "true" AI. Also using English as an intermediate step is a no-no. I doubt anything will improve while there isn't a competitor that tries a fresh approach and gets better results.
- bambax 9y ago
  > Do you happen to sell slippers? Not at all, why? Do you need any? ;-) Or is it a known difficult term to translate?
  - narag 9y ago
    Both. It's a kind of shoe in Spain forty years ago. Also the word might be getting obsolete, so yes difficult to source: https://www.calzadoslobo.com/tienda/bambas/bamba-de-lona-con-cordones-made-in-spain https://www.calzadoslobo.com/tienda/bambas/bamba-de-lona-con... There was a time when a lot of people in the irc added an "x" to their nicks in the fashion of unix, linux, aix, hp-ux... I can't help to make the association every time :)
- deleted 9y ago

Stranger43 9y ago

It might be worth looking into the pidgin languages that tend to emerge when two cultures meet and start to trade without the intervention of a officially appointed/educated class of arbitrators i.e. machine translated text might be seen as a kind of pidgin language thats not really any of the two languages in question.

EvgeniyZh 9y ago

And what is "real understanding"?

lukas099 9y ago

> The bailingual engine isn’t reading anything—not in the normal human sense of the verb “to read.” It’s processing text. The symbols it’s processing are disconnected from experiences in the world. It has no memories on which to draw, no imagery, no understanding, no meaning residing behind the words it so rapidly flings around. I had forgotten how edifying Hofstadter's writing was about topics such as this.

Manishearth 9y ago

The 锺书 example matches my experiences with Chinese through Google Translate. Chinese is a language where you'll often redundantly say things because each character may have (a) multiple meanings and (b) a lot of homophones, even accounting for tone. So the word for vegetable(s) is "蔬菜", which really is two different words for "vegetable" put together. I suspect that the engine has "learned" that words can be thrown away sometimes. Which leads to fun cases where it tells you the literal opposite of what you said. An example is in https://twitter.com/ManishEarth/status/919434569446776832 https://twitter.com/ManishEarth/status/919434569446776832 -- it has since been fixed. I bet what happened with 锺书 was that Translate decided to throw away 锺 in one of the cases because it didn't know what to do with it, leaving it with 书 ("book"). ------------- Regarding the anecdote about the Danish-speaking friend, this isn't too uncommon. For example, I natively speak Marathi but spoken/written Marathi differ a lot and I don't have much practice reading/writing, so I make spelling/grammar mistakes. So using Google Translate as a crutch (and then modifying/verifying the output) is great. Though I've found out that it's not really good at non-EU languages (The EU translates all of its documents into the languages of its members so this forms an excellent corpus of professional translations for Google Translate) and that my written Marathi is often better. ------------- My favorite example of Google Translate limitations is what happens if you ask it to translate "Yes." and "No." to Chinese. You get the answer 是 and 没有, which literally translate to "am" and "don't have". (there are no conjugations and kind of no tenses, so by "am" I mean "whatever conjugation of the English word 'to be' makes sense in this context") The thing is, Chinese doesn't really have words for Yes and No. 是的/不是 sometimes works (I think this is more because there are implicit 是s in verbless sentences). But the basic idea is that if you want to answer a question, you repeat the verb. "Do you eat meat?" "Don't eat." or "Do you read books?" "Read.". 是 and 有 are pretty common (as are "to be" and "to have" in English), so it seems like it picked up on the most common yes/no answers. For some reason, it picked a different one for no than it did for yes. Now, this isn't exactly an example of the machine translation sucking, but more of the translation not having an outlet to express ambiguity at all. The article gives examples involving this as well, but the basic idea is that some languages do not allow for the same kind of ambiguity that others do. You should be able to display something about that when translating from an ambiguous term to a specific one. Yes/No is an example of a set of ambiguous terms in English where Chinese has no similar ambiguous terms. In the other direction, as I already explained, 是 (or any verb!) in Chinese is an example of an ambiguous term that doesn't translate to English because the tense need not be fully specified (Chinese does tense from context, and even then it may not be as fully specified as it is in english), and English requires you to handle tense.

cs702 9y ago

The author is Douglas Hofstadter, author of Gödel, Escher, Bach.[a] In this article, he shows with concrete examples how Google Translate falls short, and then offers two criticisms: * Feeding more data to current models won't bring them any closer to understanding, since understanding involves having ideas (including ideas about the state of the world), and this lack of ideas is the root of all the problems for machine translation today. His examples are powerful evidence of this limitation of current state-of-the-art machine translation systems. * Current machine translation systems make no attempt to go beyond the surface level of words and phrases. These systems merely discover statistical regularities that relate words to other words at multiple, hierarchical levels of composition. In Hofstadter's words, "there's no attempt to create internal structures that could be thought of as ideas, images, memories, or experiences. Such mental etherea are still far too elusive to deal with computationally." He is right. That said, AI researchers are aware of these limitations, and are exploring possible ways to overcome them. An early, crude example of such research is the multi-modal model of "One Model to Learn Them All" (https://arxiv.org/abs/1706.05137 https://arxiv.org/abs/1706.05137), which is trained to learn to perform multiple image-recognition, language-translation, image-captioning, speech-recognition, and language-parsing tasks at the same time, using representations that are shared by all tasks. While these early research efforts fall far short of the kind of "understanding of the world" Hofstadter shows is necessary for human-level language translation, it's encouraging to see AI researchers actively looking for ways to move beyond the mere discovery of 'hierarchical statistical regularities' that relate words to other words. This is an exciting time for AI research. [a] https://en.wikipedia.org/wiki/Douglas_Hofstadter https://en.wikipedia.org/wiki/Douglas_Hofstadter

brd 9y ago
I was under the impression that modern word embedding techniques have emergent properties that start to approach conceptual understanding. The trick is figuring out how to leverage that property properly. I believe Google Brain has a demo where they show translation between two languages without having any corpus specifically linking the two (i.e. the system learned English/Japanese translations and English/Greek translations and then translated Greek/Japanese) with a reasonable degree of accuracy. This seems to me to hint at a more conceptual understanding as opposed to a simple pattern matching activity. In either case, I 100% agree that the multi-modal model is the path forward. It feels like there's a lot of low hanging fruit in the area of model ensambles.

Houshalter 9y ago

These NNs have fewer neurons than an insect brain. They have to use so little computing power they can be provided for free to everyone in the world. Of course they have limitations. But there's been exponential progress in the last few years, and it's amazing they can do as much as they do.

btrettel 9y ago

Having used Google Translate to translate at least 6 Russian academic papers in full and about 40% of a Russian dissertation (along with a far larger number of partial translations of papers), I can say that Google Translate works decently for this task. The result won't win any awards for elegance, but it's by large intelligible. I submitted many corrections over the past years and that seems to have noticeably improved the quality, at the very least reducing the number of gibberish sentences. My contributions are limited to fluid dynamics, so perhaps they won't generalize, but I am very happy with the results. Google in particular had problems with technical phrases which it translated literally, where the literal translation does not correspond to the equivalent English phrase. In one case there was no equivalent English phrase and Google Translate returned a phrase that seemed like it had a Latin etymology. After reverse engineering the word based on the Latin, I recognized the concept and found it interesting that there was a Russian word for this. If I recall I added a footnote explaining the word. Anyway, I doubt your average human translator without subject knowledge could do this. So I don't blame Google Translate for this too much. I have a list of problem sentences that I'll take to a human translator some time in the future. Machine translation has been convenient but does not yet replace human translation even when one sets a fairly low bar as I do.

petra 9y ago
How did you find interesting articles in Russian ? And I assume they contained knowledge not available in English ?
- btrettel 9y ago
  The articles absolutely contained information not available in English. I would not have translated the articles in full if they had, as my time is limited. My specific field is the breakup of liquid jets into droplets. As an example, I found a paper that applied the maximum entropy principle to the prediction of droplet size distributions in 1938 (by V. Ya. Natazon), almost 50 years before the approach was popularized for droplet size in the west (and before Jaynes). I actually was not attracted to the paper for that, though. The specific constraint used involved a measure of the strength of the turbulence, and that was what attracted me as my own research was going in that direction. The constraint would be regarded as novel if it were published in a western journal today. I am working on a conference paper right now which cites this. As for how I find the articles, there are many approaches. I found the Natanzon paper by reading an old English translation of a Russian textbook. Textbooks can be particularly fruitful in terms of foreign references. I also found bibliographies useful to find citations along with English abstracts (though few are recent). Going backwards through the citation network for a particular foreign paper you find interesting can also return good papers. It's also worth noting that in the Cold War era many translations were produced, though they may be hard to track down today. I only translated articles where no previous translation existed best I could tell. I could write more on identifying whether an article was translated and tracking down the translation if you are interested.

cannam 9y ago

In 1968 the Sunday Times ran a competition to translate a poem by Baudelaire ("Je suis comme le roi d'un pays pluvieux") into English. The poet Nicholas Moore read about the competition and, apparently angered by the fruitlessness of the task, entered it 31 separate times with 31 different poems, many submitted pseudonymously and in the styles of other poets. One of them even begins "I'm like the Winner of the Competition / the one who wrote the strong, rewarding phrase..." None of his entries won, but his anger ("All I have against translation is that it can't be done!") carried him a surprisingly long way. You can read his entries (and the original) here http://www.ubu.com/ubu/pdf/moore_spleen.pdf http://www.ubu.com/ubu/pdf/moore_spleen.pdf

kccqzy 9y ago

The author makes one point that is wrong. The current deep learning version of Google Translate specifically didn’t all of the available corpus and data that the old translation uses, simply because the new engine takes much longer to train with the all the data. Google internally looked at statistics and decided that the new engine is aleeady better so there’s no need to feed it more data. I believe they decided that a reasonable time for training (around two weeks IIRC) is more important. So any want of quality is definitely a pragmatic choice made by Google, but not for want of data.

anotherevan 9y ago

He never did explain why Frank and his Danish friend used Google Translate despite both being fluent in each other's native languages. I read right through mainly to try and find that out.

buovjaga 9y ago

Regarding the "gender blunder": https://en.wikipedia.org/wiki/Anaphora_(linguistics) https://en.wikipedia.org/wiki/Anaphora_(linguistics) I noticed Apertium is looking to solve the problem in their system: http://wiki.apertium.org/wiki/Anaphora_resolution http://wiki.apertium.org/wiki/Anaphora_resolution It's a potential GSoC project for them: http://wiki.apertium.org/wiki/Ideas_for_Google_Summer_of_Code/Anaphora_resolution http://wiki.apertium.org/wiki/Ideas_for_Google_Summer_of_Cod...

SZJX 9y ago

It already works very well, especially after its improvement in the recent years after using neural networks. I've frequently been surprised by the quality of the translation it provides to Japanese sentences, not to mention between German and English. The test cases I put in in some other languages (e.g. Chinese) are really promising too. Machine translation is a really hard problem. The messiness of language as a system, the importance of context in daily conversation etc. all play a part. Another layer of complexity is the gap between everyday usage and official, written form, which is also being tackled by researchers. You have to put this thing into perspective. Many of the old rule-based/Chomskyan software have been simply unusable for decades. New statistical approaches have been in use for barely 10 years, and industrial deep learning less than half a decade. There are still much more to come. The hype IMO is well justified.