More on Statistical Language Translation

DrLudicrous writes "The NYTimes is running an article about how statistical language translation schemes have come of age. Rather than compile an extensive list of words and their literal translations via bilingual human programmers, statistical translation work by comparing texts in both English and another language and 'learning' the other language via statistical methods applied to units called 'N-grams'- e.g. if 'hombre alto' means tall man, and 'hombre grande' means big man, then hombre=man, alto=tall, and grande=big." See our previous story for more info.

Comments

Re:So statiscally...

[Matthias Wiesmann]

Actually, using this technology to translate from english to english could be quite interesting. Imagine you could automatically translate legalese, or marketing speak to plain english. Or translate an article with a given political bias towards another political bias.

If this happens, I suspect this technology will be illegal...

Re:unfortunately doomed

[plasticmillion]

This is definitely true. At the same time, the results of statistical natural language processing are surprisingly good. Really this should not be so surprising, since they function in a way similar to the human brain. A neural network like the brain is designed to deduce a complex function from training data. I believe strongly that the best way to get intelligent(-seeming) behavior out of machines is to mirror this process.

Artificial neural nets are one way to do this, but statistical methods are more or less analogous and have the advantage of being highly optimizable. Personally I don't understand the details, but Very Smart Mathematicians have found ways to optimize models like Singular Value Decompositions (SVDs) so that they can be calculated orders of magnitude faster than models that cannot be represent as formally using mathematics.

The bottom line is that statistical methods are probably the way that we will end up producing brain-like behavior on computers, and the fact that there are promising results already is heartening. Yes, for truly intelligent behavior a lot of domain knowledge will also be needed, as you point out. But I don't see any reason why the extraction and mapping of this knowledge couldn't also be achieved with large training corpora and statistical methods, rather than hand-crafting.

Re:unfortunately doomed

[capologist]

It may be possible for this approach to address that issue somewhat. Statistics can be collected not only on associations of words with other words, but also on associations of groups of words or phrases with others. So if the translator has learned from documents in which the phrase "put it down" appears near the word "ill" and the word "dog," and from other documents in which the phrase is associated with the word "heavy," it can make a good guess.

Clearly, it would need to learn from a tremendous amount of input data before it could begin to approach the experience of a human, and hence make guesses of similar quality to a human translator. However, the amount of available source material is increasing so rapidly that it may be possible for a translator to get pretty darn smart this way.