PARIS, Oct 13, 2019 (BSS/AFP) – Designers of machine translation tools
still mostly rely on dictionaries to make a foreign language understandable.
But now there is a new way: numbers.
Facebook researchers say rendering words into figures and exploiting
mathematical similarities between languages is a promising avenue — even if
a universal communicator a la Star Trek remains a distant dream.
Powerful automatic translation is a big priority for internet giants.
Allowing as many people as possible worldwide to communicate is not just an
altruistic goal, but also good business.
Facebook, Google and Microsoft as well as Russia’s Yandex, China’s Baidu
and others are constantly seeking to improve their translation tools.
Facebook has artificial intelligence experts on the job at one of its
research labs in Paris.
Up to 200 languages are currently used on Facebook, said Antoine Bordes,
European co-director of fundamental AI research for the social network.
Automatic translation is currently based on having large databases of
identical texts in both languages to work from. But for many language pairs
there just aren’t enough such parallel texts.
That’s why researchers have been looking for another method, like the
system developed by Facebook which creates a mathematical representation for
words.
Each word becomes a “vector” in a space of several hundred dimensions.
Words that have close associations in the spoken language also find
themselves close to each other in this vector space.
– From Basque to Amazonian? –
“For example, if you take the words ‘cat’ and ‘dog’, semantically, they are
words that describe a similar thing, so they will be extremely close together
physically” in the vector space, said Guillaume Lample, one of the system’s
designers.
“If you take words like Madrid, London, Paris, which are European capital
cities, it’s the same idea.”
These language maps can then be linked to one another using algorithms —
at first roughly, but eventually becoming more refined, until entire phrases
can be matched without too many errors.
Lample said results are already promising.
For the language pair of English-Romanian, Facebook’s current machine
translation system is “equal or maybe a bit worse” than the word vector
system, said Lample.
But for the rarer language pair of English-Urdu, where Facebook’s
traditional system doesn’t have many bilingual texts to reference, the word
vector system is already superior, he said.
But could the method allow translation from, say, Basque into the language
of an Amazonian tribe?
In theory, yes, said Lample, but in practice a large body of written texts
are needed to map the language, something lacking in Amazonian tribal
languages.
“If you have just tens of thousands of phrases, it won’t work. You need
several hundreds of thousands,” he said.
– ‘Holy Grail’ –
Experts at France’s CNRS national scientific centre said the approach
Lample has taken for Facebook could produce useful results, even if it
doesn’t result in perfect translations.
Thierry Poibeau of CNRS’s Lattice laboratory, which also does research into
machine translation, called the word vector approach “a conceptual
revolution”.
He said “translating without parallel data” — dictionaries or versions of
the same documents in both languages — “is something of the Holy Grail” of
machine translation.
“But the question is what level of performance can be expected” from the
word vector method, said Poibeau.
The method “can give an idea of the original text” but the capability for a
good translation every time remains unproven.
Francois Yvon, a researcher at CNRS’s Computer Science Laboratory for
Mechanics and Engineering Sciences, said “the linking of languages is much
more difficult” when they are far removed from one another.
“The manner of denoting concepts in Chinese is completely different from
French,” he added.
However even imperfect translations can be useful, said Yvon, and could
prove sufficient to track hate speech, a major priority for Facebook.