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Translation Software That Learns by Reading
redcone writes "New Scientist is reporting that translation software that develops an understanding of languages by scanning through thousands of previously translated documents has been released by U.S. researchers. According to the article "The translated documents used to teach the translation algorithms can be electronic, on paper, or even audio files. The system is not only faster than other methods, but also better suited to tackling less common languages and the unusual vocabulary found in specialised or technical texts.""
Why didn't I have this software during High School Spanish?
I wonder if we could train it to translate a EULA ;)
* Olaserov is in the process of thinking up a signature.
Can someone translate that article from British english to American english please.
Thanks.
Hope for slashdot. I've always wondered if we only have artificially intelligent editors...
I remember hearing about this a couple years ago. They were using translations of Harry Potter and the Bible to teach this software to translate. It seems to work well. I wonder what it'd make of different translations of technical documentation. That'd probably be even more interesting than what it'd make out of 'quidditch'.
This could be great if it were opensourced. It'd be nice to translate email, instant messages, websites, technical docs, and lots of other stuff we're currently using the fish for. The fish is nice but not that effecient to add to other programs and it's translations aren't usually that great.
At what price learning? At what cost wisdom? The price is a man's peace of mind, and the cost is his life.
I wonder if something similar to this could be used for AI , for say Turing Test's ?
Teach Software translating on scanning up
Not hard wares that sticks an comprehension of talks by scanning on thousands of fish translated papers has been vomited by US scientists.
Many existing translation not hard wares uses palm rules for botching words and phrases. But the new software, snarked by Kevin Knight and Daniel Marcu at the Information Sciences[...]
Read More...
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"The newly born animals are then whisked off for a quick run through a giant baking oven." --heard on Food Network
But if you give computers a bunch of human stuff to read, you expose the dictionaries to language as it is actually used, not just as the dictionary has it. Then when odd language usage falls upon us like it's raining cats and dogs, they will have a database of similar usage to draw upon. Hey, it's an uphill climb, but this is a good avenue to try. Cheerio, computers, and a top o' the mornin' to ya.
As a caveat, we should be wary of saying the system "understands" a language.
I would say generally that humans able to translate between languages generally understand both languages, but whether a statistical, probabilistic model based on correlations understands a language might be a stretch.
Further reading: Searle's Chinese Room argument- http://en.wikipedia.org/wiki/Chinese_room
This is akin to asking, Does your tax software understand the tax code? Does Photoshop understand the principles of image manipulation?
Are these silly questions to ask?
Further reading: Dennett on intentionality (http://en.wikipedia.org/wiki/Dennett but the entry is pretty sparse).
RD
Don't remember exactly where I read this, but google apparently has long believed that there is enough data on the internet alone to be able to intelligently translate... What these guys claim to have done is, it would seem, the missing peace of the puzzle for google. I wouldn't be surprised if google gets in on this.
The article (and the text of the orginial posting) makes it seem like translating a specialized technical text is somehow harder than translating, say, a newspaper article. As someone experienced in translating technical (science/engineering) documents, I can say that any tech document is far _easier_ to translate after an initial learning curve.
...)
The main reason (I think) is that: tech documents have specialised vocabulary and idioms, but these are much fewer than the idioms one has to master in order to understand the editorial page in a newspaper.
With a rudimentary knowledge of Russian and French, I have found it much easier to read an engineering textbook or paper in these languages, than reading any nontechnical text. (This is not necessarily the case with other languages. Any document in Japanese for instance is an entirely different ballgame
This reminda me of Jamie Zawinskies hack Dadadodo which used probability trees to create new texts from old texts by examining the probability any given word follows the previous word/string of words. I always thought his program was cool, in that his description of it involved Markov Chains and William S. Burroughs.
I did a presentation for an AI class a while ago and discovered that Microsoft already does this with their MSR-MT project. Apparently the Spanish entries in their Knowledge Base were translated by this as well.
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After a quick web search, all I was able to find was this site, which has a pretty sketchy TOS agreement.
Using statistical methods to predict the next item in a sequence is still not true hard ai though, this technique is used with the voice recognition software "Dragon Natually Speaking" creating in effect pattern chains. What Dragon did on the character level this software appears to do on the word level. This is still not true AI however, as the statistics will only map to probabilistic sequences not abstractly map instead to the concepts. What would really impress me is if they came up with a mapping algorithm that instead of using probability used a function like mini-max fitness testing on a neural-network substrate.
It would be interesting to see the results of analysing large sections of languages however, but the only immediate use I can fathom for this would be for cryptography or information compression algorithms. However the results could probably be used to provide insight into how languages evolve or how memes spread from language to language.
Or the brief explanation in the article did not make it clear enough how this differs from what was previously state-of-the-art, e.g. Dragon.
Shh.
One has to wonder if the language of choice English or whatever is so structured and rule ridden and not just made up on the fly. Then how come its so difficult to determine all the rules? Is it there are too many of them? too many contexes? Or just trying to translate bad grammer which fails the rules but any human can decipher it.
:-)
Sometimes brute force, ie look up tables for 100000000 translated versions can be better, so much for logic eh
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...and fruit flies like a banana.
When an automated translator can handle that one without bursting into flames, I'll start to believe.
The basic approach has been developed over 10
years ago by IBM: The Mathematics of Statistical Machine Translation. And even free software has been available for a while, see
http://www.fjoch.com/GIZA++.html.
Skynet begins to learn at a geometric rate. It becomes self-aware at 2:14am Eastern Time....
Sounds interesting, but I couldn't find a single sample translation on their site; ie a block of text in language A (Say, french), and language B (Say, english). Translated from A to B by their software.
Without even the simplest of examples or samples we have only their word on how well this works.
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A friend of mine was trying to translate an English novel into German a while back. She had to work out a replacement for a sentance where the word 'therapist' was construed as 'the rapist'. Hell of a job and she's a professional translator.
Automatic translation looks pretty good for technical documents, news and anything completely literal. When you get writing with double meanings, humour and plays on words it gets way harder - often to the point where there is no correct translation.
One of these days I'm moving to Theory - everything works there
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This is news of '93, when Brown et al. at IBM built their famous statistical machine translation system. It does exactly what is described in the article. I myself work on such a system (for Hungarian-to-English translation).
The article (press release?) is totally misleading. Kevin Knight and Daniel Marcu are building on at least 15 years of active research on statistical machine translation. On the other hand, they are really very good at it.