Monte-Carlo
number-crunching methods have long been used as a “brute” method of finding mathematical
answers. A classic example may be Deep Blue’s seemingly infinite “random paths”
for deciding the best possible next chess move when pitted against Gary
Kasparov. I suspect many scientists were hoping the computer would win,
especially those who created the beast, but most humans probably had a secret
desire to see the human come out triumphant. In fact, the Russian did win one
match and drew three, but lost two. So few results are statistically
insignificant, but obviously Kasparov, with the “disadvantage” of being human,
was unable to generate billions of potential moves like his opponent and yet
was capable of winning. How was this possible? I believe the answer, and the
analogy, can be applied to machine translation.
Take
the translator tool provided by a well-known search engine. With the
astronomical amount of words passing through their servers every day, they
certainly have a lot of words to crunch through their translation software (if
indeed they do). By a process of choosing the mode, that is to say the “most
used word” on the Internet, the machine is able to choose what it considers the
most likely translation for a specific word by comparing/aligning texts in
different languages. (Incidentally, take a good read of the policy regulations
before setting up a mail account with such large internet service providers and
you’ll understand why you should never send your translated texts via these –
you may simply be adding to the company's huge TMs). This is not so different
from the most widely used dictionaries in the world conducting surveys to see
how many people use a word before officially accepting it in their
dictionaries. However, there are two problems with this, where we human
translators can undoubtedly outwit the machine just like Kasparov. The machine
trips up with exceptions (Kasparov did the unexpected by not
always choosing the "best" move) and with poetic licence. The
metaphor, for example, can derail it.
Clearly,
we human translators must take enormous care in using such tools, and always
only as an aid. For example, after translating a text you may find it useful to
run a section (never the whole text) through to see what words the computer
chooses and give you some new ideas that may not have occurred to you, like a
thesaurus. But even in this case, one should never forget that the median may
not always be correct. One has only to look at the examples in human history of
landslide majorities voting for thuggish dictators to realise that the majority
can often be wrong. Just because a word is used more frequently does not mean
it is correct; one has to check the official sources and consider the specific
context. There is also the threat of plagiarism; a scientific author with a new
patent will not be pleased to find their closely guarded secret floating around
a famous search engine’s TM.
Incidentally,
computers can also be used to intelligently “generate” their own literature. In
his classic book “Fooled by Randomness”, Nassim Nicholas Taleb tells of how he
used Andrew C. Bulhak’s Dada Engine to come up with phrases like this: “Many
narratives concerning the role of the writer as observer may be revealed. It
could be said that if cultural narrative holds, we have to choose between the
dialectic paradigm of narrative and neoconceptual Marxism. Sartre’s analysis of
cultural narrative holds that society, paradoxically, has objective value.”
Such pseudo-intellectual drivel may sound familiar to anyone who has translated
for low-brow art critics.
Then
we have the tantalising prospect of machine interpreting, which may not be so
far-fetched as many still believe. Whenever you talk on the phone, your voice
is digitized before reaching the receiver, and this has been so for many years
now. It's not your mother you hear, it's a computer copying her. As seen at the
last Proz conference in Barcelona, a computer can “learn” an individual human’s
voice and reproduce it with new sentences of its own. The “Terminator” films
may start to ring a bell. This may be old hat to James Bond or the CIA, which
leads one to think that the next step can now be taken. In fact, I believe the
BBC are well on the way to doing it, albeit unwittingly. The corporation has
been using live subtitling for years now. To do so, one may either employ an extremely
fast typist or…a computer. Basically, the computer recognises the voice and
flashes the words it has understood onto the screen. From what I have seen, I’d
say it gets about 90 % right, which in my opinion is quite impressive,
especially when faced with so many accents. Imagine a Glaswegian interviewee
saying “I cannae. D’you see? D’you ken?” The machine may well understand this
to mean “A can o’ juicy chicken,” for example. However, this hiccup can also be
overcome. After crunching thousands of interviews in Glasgow, the computer has
only to be told where or who it is
translating to get the gist. Taking another leap forward, a GPS / satnav device
could also be employed to automatically inform the computer when it is in
Glasgow or Los Angeles, so it can adjust its voice recognition and vocabulary
accordingly.
So there we have it: our hand-held interpreter of the not-so-far future.
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