ight years and two Olympic Games ago, I wrote an article
titled " Tapping
Into the Olympic Spirit: The Pillars of Translation
." Inspired by
the classical columns of the Greek Olympic hosts, I tried to narrow
down the four pillars of translation that give us the required balance
in our everyday lives as translators.
Here's what I identified:
- Profound knowledge of the grammatical rules that
govern the source and target language(s)
- Lexical knowledge of source and target language(s)
and the complicated relationships between the two lexicons
- Ongoing, practical interaction with the source and
target language(s) and the ability to understand contextually (I called
that "living knowledge of language" back then)
- Knowledge and hands-on experience with the tools of
the trade, which included the many computerized resources such as
computer-based reference materials and computer-assisted translation
Examining these four pillars, I concluded that what
distinguishes us from machine translation systems is the third
criterion—machine translation systems might know language rules,
lexicons, and technology, but since they don't truly understand
language in context, they often fail us.
(And before you get too upset, I also mentioned "other
supporting pillars, such as marketing, client education, and the
ability to work on a team.")
Now that the world's best Olympic athletes are
assembling again, this time in London, has anything changed?
The short answer is "yes, to some degree." The
"pillars" that I listed remain, but there is a different emphasis on
the final one. And I also would draw a slightly different conclusion
when it comes to machine translation.
The Changing Fourth Pillar: Our Knowledge of Tools
of the Trade
First of all, I think that we can give ourselves a
little pat on the back for having come a long way with translation
technology in these last eight years. While technology for the
professional translator did not develop as fast as it could have (too
much effort had to be spent to convince us of its usefulness), our
appreciation and application of it has. Without any hard numbers to
prove it, I know that among professional technical, medical, legal, and
other functional translators the actual employment of translation
environment or computer-aided translation tools has increased
significantly. Many of us might have owned a copy of Trados or
one of its many competitors in 2004, but as far as really understanding
how to use it efficiently for every job that came through our office
doors or email inboxes—that was a different matter altogether.
Today, while we are still far from full and perfect implementation,
we've come a lot closer.
The same is true with online resources. Many of us not
only use simple online dictionaries, we also access much more complex
corpora. Some of us are even building our own corpora (or translation
memories) to support our translation process.
One area that unfortunately has not changed when it
comes to technology is the sparse use of our translation environment
tool's terminology feature. Many of us still aren't willing to invest
the time to learn how to use these tools adequately or build up our own
terminology resources. Change here might come
indirectly through the increased use of subsegment leveraging (I've
written about this often, including right
Machine Translation on the Move
And why would I draw different conclusions in regard to
machine translation today?
Just the week before this article was written, Yahoo!
Babelfish was officially put out to pasture. That's the same Babelfish
that put machine translation—for better or worse—at
everyone's fingertips and paved the way for all other online machine
translation engines. It's not that you can't
access the same kind of results anymore. These were always provided
courtesy of Systran, and you can find the same engine on their
website. But aside from a certain historical relevance surrounding
the retirement, what's most interesting is that it was replaced with
Microsoft Bing Translator. This means that all large search
engines now have machine translation features that are based on
statistical machine translation (SMT) engines rather than rules-based
systems (you can find definitions and
links on the different approaches here).
Google uses Google Translate, Bing and Yahoo!
use Bing Translator, and the leading Chinese search engine Baidu
and the Russian leader Yandex use their own proprietary SMT
engines (Yandex only for its most important languages of
Russian, Ukrainian, and English).
So, my 2004 conclusion that machine translation lacked
a "living knowledge of language" could easily be disputed today:
statistical machine translation is based on actual translated texts
rather than mere rules like its rules-based sibling. Does that actually
make it better? It depends. The large online systems might perform
better "out of the box" than their rules-based cousins for some texts;
on the other hand, the rules-based systems are generally
superior when they're trained for a specific subject matter (see this article
in the Translation Journal).
What's probably more relevant to us when it comes to
machine translation is that more and more translators are actually
using it for a first "dirty" translation pass if no translation memory
hit is found. How many? Certainly more than in 2004! Look at the fact
that machine translation has become a staple in virtually all
translation environment tools, a state of affairs that was almost
unheard of back then.
To switch our metaphor now to one of the Olympic
sports, it's safe to say (and is becoming increasingly politically
correct) that alongside the arrows of translation memory leveraging,
terminology management, quality assurance, and file management, machine
translation is a weapon that is increasingly finding its place in the
professional translator's quiver.
It's also good to know, though, that some things never
change: Just like eight years ago, none of those arrows in our quiver
will hit the target without our guiding and steady hands.