MT and MAT
Will translators be replaced by computers? If so, when? If not, why not? And in
between these two extremes resides a world of possibilities that exist under the
rubric MAT (Machine Assisted Translation), all of which are impacting on translators
right now and will in all probability rise in significance very rapidly over the
coming years. Will translators want to work with the new technologies? Will the
new technologies work at all? And most important, can someone entering this field
now at the start of a career path expect it to remain even remotely recognizable
in the coming quarter century?
Perfect Translation
The perfect translation system, be it a human or machine, does not exist. However,
the dream of something like the Babblefish from the Hitchhiker’s series or
the universal translator on Star Trek haunts us and might go something like this.
Your personal computer will have a translation module, maintained from some central
database created by the publisher of the system. When email comes in, it will automatically
and almost instantly be translated into whatever language you desire (presumably
your native tongue). When you send email, it will be translated into whatever language
you choose. You will be able to configure it so that when email goes out to Japan,
it is translated into Japanese, when it goes to France, it is translated into French,
and so on (or you can configure on a person by person basis, giving consideration
to the linguistic skills of individuals).
Similar systems will exist for businesses, but they will be faster and more comprehensive.
A book will be scanned into a computer and rendered into another language in a matter
of minutes. The computer might even attend to the graphics and desktop publishing
tasks, assuming you want it to. The finished translation will need the same amount
of editing and proofreading that any piece of writing does, that is to say a lot.
Interpretation will work the same way. Your phone company will provide for virtually
nothing a system which lets you talk to anyone in any language. You call Japan and
speak to Mr. Tashima. You say what you want in English and he hears it in Japanese.
He says what he wants in Japanese and you hear it in English. Court, medical, and
conference interpreting will work in basically the same way. People will have small
devices like hearing aids which will pick up the incoming language and convert it
into your native tongue. These devices will also use noise cancellation technology
to take care of any interfering sounds so that you hear only the interpretation.
A box on your television, or perhaps inside it, will provide instant interpretation
or subtitles of foreign films and television broadcasts. You will flip to one of
the more than 500 channels you have and see a program which looks interesting, and
the system will provide instant interpretation of the dialog.
Furthermore, small devices the size of a pocket calculator will read things for
you. You point them at a menu, a street sign, or a newspaper and they scan the page
and they translate it and then give you either a printed version on a small screen
or read it to you.
Such technology would make communication with anyone anywhere possible. You could
travel in remote parts of Tibet and speak and read with the locals. You will walk
into a conference and listen to an interpretation of the speaker given by a machine
which never tires or loses interest in the task. You can go to a doctor or hotel
or restaurant anywhere and communicate everything you need to, be it verbally or
in writing.
Can It Be Done?
This is really two questions. One: Is machine translation possible in theory? Two:
What will machine translation be like in practice within the next ten to twenty
years? The former question seems not to be asked much, if at all, except in certain
research laboratories. The latter question seems very much on the minds of translators
and others in the translation industry, if only because of the profound financial
impact the answer to the question will have.
The first question, whether or not machine translation is possible in principle,
might seem impossible to answer. Or perhaps you think that the answer has to be
assumed as negative until proven otherwise, in other words, it ain't possible until
someone does it. But given that machine translation, unlike breaking the four-minute
mile, will involve hundreds or thousands of people working for years or perhaps
even decades and spending billions, possibly trillions of dollars in their effort,
a little theory seems like a good idea.
The arguments against machine translation being possible seem to run something like
this. Language is too subtle and complex for a computer to understand and translate.
There are just too many variables to consider in any given sentence. Linguistic
communication relies too heavily on context and intonation, on body language and
cultural underpinnings, to be handled by a computer. Computers will never be fast
enough or powerful enough to deal with the immense requirements of language translation.
Computers will have to understand what they read in order to translate, and therefore
will have to be sentient themselves, in some fashion similar to what we humans experience
as self-awareness. And perhaps the most fundamental argument against machine translation
lies in the question of whether or not the human brain is capable of actions and
behaviors that cannot be reduced to algorithms.
Fair enough, all good arguments. But the argument for machine translation being
possible in theory is sufficiently powerful and compelling to obviate all the above
arguments against it. In simple terms, the argument for machine translation goes
like this: "If that three-pound piece of meat in your head can do it, why not
a hunk of technology?" In essence, the proof for machine translation being
possible in principle is sitting in every translator's head. That three-pound pulpy
grayish mass that we call the brain allows a translator to translate. A brain is
an organic machine consisting of roughly one-hundred billion cells, neurons and
glial cells, each with a multitude of connections to other neurons, communicating
chemically with each other through synapses whose activities are modulated by neurotransmitters.
Regardless of how little is actually understood about the brain, and regardless
of the obvious deficiencies of my description of it above, the brain remains a finite
object capable of only a finite number of actions. As such, the brain can be considered
a machine, or if you prefer a less mechanistic metaphor, a piece of organic technology,
which can in principle be understood and reproduced. And so a computer that translates
as well as a human translator is in principle possible.
But So What?
What does the argument above really imply for the future? In other words, just because
something is possible in principle doesn't mean we'll be able to do it in practice,
at least not in the near term. Or maybe we will.
First I want to dispense with a few preconceptions and protests that are probably
percolating in your mind. One, computers are plenty fast nowadays. I don't mean
the little box sitting on your desk or lap, which is in and of itself powerful in
many ways but equally limited. I mean the chips that are currently on the drawing
boards for the next generation of supercomputers. If Moore's Law holds for even
fifteen more years (note: Moore's Law refers to the trend of doubling the computational
capacity of chips every eighteen months), and as a technical translator who does
a lot of work in computer science and electrical engineering I can say with some
confidence that the research community believes it will, then we will have a computer
chip whose speed and capacity is functionally equivalent to the human brain by 2025
at the latest. Similarly, the cost and performance of various types of memory are
expanding far faster than most home users can find uses for, though web servers
rapidly eat up even terabytes of data. Finally, the kind of parallel processing
that gives supercomputers much of their power is becoming more and more common at
the consumer level, so even if Moore's Law places an upper limit on the performance
of an individual chip, a group of chips tied together, making full use of terabytes
of RAM and other high-speed memory arrays, should easily equal the raw power of
the human brain within fifteen years.
Enough of the technical stuff. That's not, you might say, where the problems really
lie. They reside instead in the nature of language, in the intricacies and subtleties
of written and oral communication, in the nuances of a person's voice or the subtext
in a well-written paragraph. Accurate enough, to varying degrees, but rarely relevant
to the vast majority of what is being translated in the world these days.
Most of what is translated in our industry is not high literature destined to be
awarded Nobel or Pulitzer prizes. Rather the majority of material that translators
work on is information, ideas, or beliefs on a particular subject, and most often
the material is nothing more than instructions, directions, or explanations, with
a minimum of style of literary content. The material is generally bland and dry,
for instance software or hardware manuals, engineering specifications, scientific
or other technical research material, financial or corporate reports, fiscal analyses,
clinical trial reports, patents, and so forth. Accurately rendering the subtle style
of a source text is rarely an issue that translators struggle with, or even discuss
much amongst themselves. So if the current human translators don't have to deal
with the subtleties and nuances of well-written literary prose, then neither will
the machine systems.
As an aside, let us keep in mind that literary translation is an area of endless
debate among literary translators; the sheer number of versions of literary classics
amply demonstrates this. That machines may not in the foreseeable future tackle
such material is not relevant to this discussion; instead it should be remembered
that even humans have difficulty ferreting out the intended meaning in a sentence
written by a literary master. What's more, that meaning will change with both the
reader and the times. Literary theory and literary analysis are dedicated to such
issues; the fact that these are fertile fields for endless explorations suggests
that people aren't quite sure what to make of fiction like James Joyce's Ulysses,
to pick a particularly intractable text. I am certain that computers will eventually
try their electronic hand at rendering the Mahabarat or the writings of Chuang-zu
into English, and I look forward to studying the results.
Back to the topic at hand though. What MT systems will work on represents a fairly
particular subset of the world's written output. Not only does written language
spare the MT system from having to deal with intonation or body language, but the
kind of writing commonly translated in the translation industry at present is generally
more carefully structured and reasoned, freer from grammatical and syntactic errors,
less liable to contain slang, neologisms, or spur-of-the-moment coinages, and more
precise in terminology usage than spoken language, even on the same subject, would
be.
Finally, the MT system may not even have to understand what it is translating. I
say this for two reasons. First, translators occasionally, and almost exclusively
amongst themselves, talk about how little they understand of some of the material
they work on. They of course can follow the gist and usually much more, but they
also know, at least deep down, that they probably do not have the same in-depth
understanding that the specialist or expert who wrote the material has. This can
occur with material as simple as a business letter, in which the topic of the letter
is understood between both parties but not known to the translator, or material
as abstruse as an ethical commentary on organ transplantation and brain death.
Second, and most important, computers are more and more often nowadays performing
on par with humans in complex tasks. The canonical example is chess. You are doubtlessly
aware that Deep Blue defeated the Russian Chess Master Kasparov in a recent match.
Kasparov felt it would never happen, until it did that is. He even commented after
the match that at times there seemed to be an intelligence behind Deep Blue's decisions,
that the computer became more cautious at one point in one game. Of course he, and
all observers, know that no such thing happened. And despite the considerable accomplishment
that Deep Blue represents in combining dedicated hardware with expert system-style
programming, Deep Blue is neither conscious nor intelligent in the human sense of
those words. To put it another way, after the match, Kasparov made many insightful
and thoughtful remarks when asked about his experience. In contrast, if anyone bothered
to ask Deep Blue a question, I'm certain the remark was silence. And it is more
than doubtful that Deep Blue has any particular plans for its prize money, or any
desire one way or another to play chess again.
The point is that tasks which require considerable intellectual achievement for
humans can be performed using different methods by computers. Whether or not translation
is one such task remains to be seen. In other words, do we need to create a sentient,
intelligent computer, then teach it to translate and hope after its training it
wants to translate, or can we build a sophisticated expert system, a Blue Linguist
if you will, that translates as well as a human does, despite using completely different
internal methods? This question will be answered in part in the various R&D
labs around the world working on MT. And it will be answered in part by the market.
In other words, if the translation is good enough, translation consumers will not
care who or what translated it using which method. So the real question for MT in
essence becomes: what is good enough?
Good Enough?
Good enough means acceptable to those who want the translation. Consider this: a
company wants all the specifications for an automobile translated from English into
French, Spanish, German, Italian, Dutch, Portuguese, Chinese, and Japanese. The
specifications total over 5,000 pages, approximately 1 million words. Assume that
a translator can do 5,000 words per day (I realize this is high, but assume it anyway).
It will therefore take 200 days of work to produce the translation. A team of ten
translators will still take 20 days, plus the time to unify the text after the translators
are finished. At $0.25 per word (what the agency might charge the automobile company),
the total cost per language would be $250,000. And these numbers are for each language
involved. Therefore, if a machine system can translate the information at 20,000
words per hour, we see that the job might be done in a little over two days, plus
clean-up time. And the computer plus software will cost considerably less, maybe
$3,000 for the computer and $4,000 for the software for each language pair.
But, you say, the translation won’t be as good. I agree, at least based on
current software and technology. However, let us recall that quality is only one
of many factors in a market economy, and the most important factor is embodied that
old epigram: time is money. Recall that this statement really means that speed is
money. The faster the better. The sooner the product hits the market, the sooner
the company recoups its investment. The lower the investment, the better.
So we have a case of the classic cost-benefit ratio. Therefore, the real question
is: at what point does the quality of a translation become more important than the
cost or time involved? If the machines are 200 times faster, 1000 times cheaper,
and produce reasonably accurate and intelligible translations, they will get most
of the work. And although they have not reached this state yet, it seems clear,
given current technology and progress, that the time is not too far off when they
may just well be there, at least for certain categories of translation.
For an excellent study of the cost/benefit ratio of current MT and MAT systems,
I strongly recommend Lynn Webb's Master's Thesis on the subject, available at www.webbsnet.com.
I hope Lynn will be able to keep her research current as the technologies she evaluated
develop.
Machine Interpretation
Some people claim rather strangely that machine translation is possible, but machine
interpretation is not. I disagree. Interpretation deals with the spoken language,
a fundamentally simpler form of language than the written language. There are three
issues that will tax MI systems: non-verbal communication that accompanies speech,
voice processing and synthesis, and the general sloppiness of spoken language.
(Please note that although speech-to-speech MT is a common way to refer to machine-driven
interpretation systems, I prefer MI not only because it is a more compact term,
but also because it serves to remind us of the important linguistic distinctions
between translation and interpretation.)
The first issue will not be as important as many people might think. A speaker at
a large conference, for instance, does not rely much on body language to communicate,
simply because most viewers are not close enough to benefit from it. In fact, many
speakers at conferences are really just reading prepared speeches, changing the
issue from machine interpretation to machine translation (of course, the machine
has to be aware of deviations from the prepared text, just as a human interpreter
does). Witnesses in court are trained by lawyers to avoid body language, so that
the jurors will pay attention to the words only. And when body language is important,
humans have a great deal of trouble, given how varied and complex each person’s
use of such non-verbal communication is. So the computers will have the same problems
the humans do.
The second challenge is being met as I write this. We've all seen and heard about
voice input software such as Dragon Systems' Naturally Speaking or IBM's Via Voice.
Both work reasonably well without taxing a mainstream home or business system. It
is not difficult to imagine such software becoming virtually 100% accurate (or at
least as accurate as a human listener, perhaps more so) within a few more generations
of the software. The same holds for speech synthesis. I've been listening to my
Macintosh for years now, having it read material I have written to me so that I
can edit by listening to a disinterested reader (and trust me, the computer is completely
neutral). The available voices are admittedly obviously synthetic and frequently
tinny or disturbingly neutral, but they are improving. An acceptable synthesized
voice seems likely within a few years. If you want a sample of the improvements
in this area, listen to the Web newscaster Ananova (www.ananova.com). This virtual
woman reads the day's news headlines in a generally acceptable voice, though at
times pronunciation does sound decidedly computer-like.
The third problem, the general sloppiness and imprecision of human speech, will
be a challenge only insofar as the computers are not as accurate as people are.
When queried about the meaning of an ambiguous or obscure statement, most people
will admit that they hadn't thought much about it, but now that they do, they realize
they can't be certain as to the intended meaning. How exactly MI systems will address
such challenges, perhaps by reproducing the ambiguities, querying the speaker (if
possible, and note that when querying is possible, that is what human interpreters
do), or simply paraphrasing the statement based on a best-effort guess, remains
to be seen. I suspect though that MI systems will in time become sufficiently accurate
to be practical.
There is a final problem, one not often discussed when MT, particularly MI, is mentioned.
This is the psychological element. Even if we have a lab-tested, government-approved,
U.N.-certified MI system, it may still not be adopted for quite some time. People
may simply not accept it. I've seen Japanese people struggle with the idea that
I can speak the language fluently, and some I knew during my years in Japan never
quite accepted it. Given that kind of attitude, and it is prevalent among many languages
and cultures in the world, machine interpretation systems may not be warmly greeted,
at least not initially. So their first appearances may be in situations in which
we the users will not realize machines are doing the work instead of humans, such
as in telephone communications when making airline or hotel reservations or getting
technical support for software, or perhaps for international operator assistance.
Eventually such systems will be accepted, I think, if only because people ultimately
accept anything that makes life easier.
The State of the Art
So, you say, this is all well and good, but none of it is going to happen for a
long time. Perhaps not even for centuries. We'll all be long dead, or at least retired,
before a computer can do anything useful with language or in translation. Maybe,
but a review of where the MT/MAT industry is now seems in order.
The pace of change in computing is enough to give a seasoned funambulist vertigo.
The original PCs, including the TRS-80 (with 4K of memory, no hard drive, floppy
drive, and no operating system per se), the Commodore 64, the Apple II, etc. were
less powerful than the current average Casio BOSS or Sharp Wizard, to say nothing
of the current 3M PalmPilots, which effectively represent more computing power than
Apollo 11 had at its disposal. The first PCs, the 8086 and then the 286, introduced
in the early 1980s were brain-dead machines even back then. For the past eight years,
we’ve seen CPU processing speed double every 18 months as per Moore's Law,
hard disk storage space double every two years, and the arrival of peripherals such
as CD-ROM drives, DVD drives, scanners, and laser printers which only ten years
ago or so were either dreams or ghastly expensive technologies.
The processing power and storage capacity to handle incredibly large and complex
tasks is available, or will be soon. This means that the brute-force approach becomes
more and more viable as an approach to problems that at present resist elegant computational
solutions. Brute force more than anything else let Deep Blue defeat Kasparov, and
though chess is hardly as complex as language, it suggests that what seemed for
centuries to represent a pinnacle of human intellectual achievement can be performed
without an iota of thought as we know it, just virtually inconceivable amounts of
raw processing power.
In addition, I think we forget the extent to which human-like computing has already
started to enter our lives. We now have voice-driven phone systems in which you
state your preferred selection aloud and the system processes it. Admittedly these
systems are crude and nowhere remotely near providing real-time online translation,
but they indicate that what once seemed to be an insurmountable problem, that of
voice recognition and synthesis, is falling to the wayside.
Similarly, optical character recognition, the solution to getting texts into computers,
is now extremely fast and accurate. What's more, you can buy a little pen dictionary
that has a built-in scanning head at its tip. Run it over a word you need to look
up, and the dictionary will then display the definition on a small LCD screen built
into the shaft of the pen. Again, very limited compared to the demands of true MT,
but suggestive nonetheless.
Current MT products, including PowerTranslator, Transcend, Logos, and others, have
a limited capacity to provide useful translations. Although some translators disparage
these products' output as nothing more than word salad, in many cases the results
are useable, if inelegant. For informational purposes, however, the results may
be satisfactory to some people. Moreover, if the text to be translated is limited
in terms of style, usage, and terminology, and is put through a preparatory editing
process, then the results may be sufficiently good that with some, or arguably considerable,
post-editing, the final translation could be printed and distributed with no fear
of rejection.
Regardless of the limited scope of application for current MT software, such technology
is slowly improving and will eventually, I think, be capable of providing usable
translations for general consumption. Long before that happens though Machine-Assisted
Translation (MAT) technology will revolutionize the translation industry.
MAT
Currently MAT is in its early childhood. The most sophisticated systems are still
little more than elaborate databases with version control features for preparing
and monitoring document translation, terminology and glossary management functions,
and some fuzzy logic for finding good matches for text that has not actually been
translated yet.
Future systems, as described in recent magazines such as Language International
and Multiling will offer far more. Not only will they come with vast pools of sample
translations mined from the terabytes of such material already available and extensive
terminology and glossary listings, but they will also offer intelligent matching
of untranslated text that far outperforms today's best "fuzzy" guesses,
real-time collaboration between non-local sites via the Internet, constant and automatic
updating of sample translations and word lists via bot searches of the Web, and
so forth.
The future translator will not sit at a desk with a printed copy of a text to one
side of the keyboard and some dictionaries or other resources to the other. In fact
many translators already work primarily if not exclusively with electronic source
material and use at least some Web-based resources for terminology research. Instead
future translators will likely have a live link to their client's web site, working
directly in real time with the other translators and project manager involved in
the project. They will prepare the source material for "translation" by
the MAT system, then monitor the output and work on the parts that the system cannot
handle. They will also perform considerable editing, proof-reading, and QA work,
along with developing and maintaining glossaries, sample translation databases,
and other necessary resources for the MAT system.
This paradigm shift is already underway, with products like Trados' Workbench, IBM's
Translation Manager II, Corel Catalyst, and Atril Software's Déjà
Vu leading the way. Other products are more focused on localization, while still
others, such as Logos, offer a hybrid system that exists somewhere between true
MT and MAT, depending, perhaps, on who you ask and what you want to do with it.
The point is that this paradigm shift to MAT is not in the hazy future but is happening
now. Languages that use the Roman alphabet and routinely use source material in
electronic format are the most amenable to use with this software; languages such
as Japanese and Chinese are still largely not available in electronic format, and
even when they are, the systems do not handle such two-byte languages particularly
reliably, at least not yet.
In other words, if you are a Spanish-English or German-English translator, you are
probably already using MAT software, or you will be soon enough. If you are a translator
working from Japanese to English, you have a couple of years yet before you have
to make the move, though doing so earlier would be wise.
There is, however, a problem. Actually, there are a few problems. The first and
most obvious is the cost associated with MAT. Not only is the software itself quite
expensive for freelance translators to add to their office arsenal, but also it
requires more RAM, more hard disc space, and a large monitor to be used efficiently.
In addition, a scanner with good OCR software would also be extremely useful. This
whole bundle could run as much as $4000, depending on which combination of hardware
and software one opts for. Obviously $4000 is a lot for a freelance translator to
invest, particularly since many translation vendors prefer to pay translators who
use MAT or MT software less than they otherwise would. In fact, some translators
who use MAT go as far as not telling their clients about it so as to avoid the issue
of reduced rates when using MAT. In sum, there are considerable costs for a freelancer
who uses MAT, and how the market will treat such freelancers remains undecided in
places.
Second, and perhaps less obvious, is the question of ownership of material. Translators
are independent contractors who translate on a work-for-hire basis. They do not
own what they produce. If a translator creates a glossary or terminology list in
an MAT package while doing a translation for a client, who owns that list? If the
translator cannot recycle or reuse such lists, much of the value of MAT will be
lost. The same can be said for the organizations that want the translations done,
too. Moreover, how would a translation vendor know if I were reusing a terminology
list that I created while working for them? And should they care? Such problems
are common with Internet and computer technologies. Just consider the issues surrounding
MP3 if you are uncertain as to the arguments on both sides. I would like to see
a cooperative arrangement exist, one in which translators can continue to build
and extend their libraries of terminology and translation samples, and perhaps even,
when not legally inappropriate, share material with each other. The same, I believe,
should hold for translation vendors. The more good resources we all have, the better
our translations will become, and the more quickly we can do them. That is after
all the point of MAT.
The third and final problem is translators themselves. Many translators seem resistant
to MAT because of the paradigm outlined above. They see translation as a highly
intellectual process, one which involves careful analysis of the source text, meticulous
research in "quaint and curious volumes of forgotten lore", and then creative
writing to formulate a target text that balances form and function. MAT takes much
of this away, they believe. It is too automated, too computerized, too…, well,
you get the idea. I don't consider these translators to be Luddites, resisting to
the last a change that is inevitable and beneficial. What I think they are resisting,
and I share in their resistance, is a tendency in the translation industry, and
in localization in particular, to put speed above everything else. Translators thrive
on the challenge of creating a high-quality translation; MAT is perceived by many
as a way to crank out in very short times a translation of at best marginal quality.
"Good enough so that we don't get sued" is how one localization manager
put it to me one day. Whether or not these attitudes are justified or reasonable
is a matter of endless debate; but the fact remains that many translators are not
rushing to embrace these technologies, use them only grudgingly, and in some cases
are leaving the translation profession. I hope that translators will give the technology
a chance to mature, to be better understood and appreciated, and to be more widely
used in the industry before they reject it. MAT is here to stay; it has its place;
it has the potential to let translators do what they do best. Conversely though,
employers of translators, localization firms in particular, should take the time
to train translators to these systems, to transition not overnight but a bit more
gradually to this new paradigm, and to let translators actually translate. Unhappy
translators rapidly become ex-translators, and the supply of good translators is
small enough that no one should do anything to reduce it.
Final Thoughts
In 1992 I bet a friend that within 15 years, computer translation systems would
take over the industry, leaving very little work for humans, who will maintain and
operate the systems and edit their translations. As of this writing (spring, 2000),
I am prepared to say that I have lost this bet. My earlier estimations about when
and how machine translation would evolve are clearly incorrect, so I concede.
But let’s take a look at what has happened in the past five years, the time
from when I first wrote about that bet until now. The first desktop supercomputer,
the Apple Macintosh G4, has arrived, with Intel’s chip line only slightly
behind. Voice synthesis is now available as a part of the Mac OS, and though the
voices are lackluster, they are usable. Voice-input systems, such as IBM’s
ViaVoice and Dragon Systems Naturally Speaking series, are now available for a couple
of hundred dollars or less and offer accuracy rates approaching 98%. And machine-assisted
translation software (MAT) and terminology management software are becoming more
prevalent and useful.
Ultimately I believe true MT is inevitable, though how or when it will arise I no
longer care to predict. As Neils Bohr said: prediction is difficult, especially
about the future.
For me the real question is how will a machine translation system be created. There
are two major avenues of research: One, create a conscious computer which can understand
and manipulate language essentially as a human would, but do so much more quickly
and accurately. This seems extremely difficult for the near term, as there is as
yet no good definition of consciousness itself, and what relationship language and
consciousness have remains to be clarified. There are also obvious logistical and
ethical issues involved, such as what to do if the sentient computer isn’t
in the mood to translate (can you threaten to pull its plug?), or how to educate
such a computer to be a good translator (how to accomplish that with humans is still
a subject of some debate).
The other major avenue is to create a system which produces a good translation using
different methods from how the human brain does it (however that may be). This is
the approach used by all current machine translation systems. Progress thus far
is better measured not by how far the systems have come, but by how far they still
have to go. Perhaps IBM is working on a successor to Deep Blue. IBM might name it
the Blue Linguist and have teams of researchers creating specially-designed language
chips, circuit boards, databases, and so forth. And perhaps there will be a contest
every year in which the Blue Linguist and five expert human translators all work
on the same documents, with a panel of judges trying to identify the Blue Linguist’s
work from among the group of six translations.
The point is that the results of the MT, or for that matter the MAT, system matter,
not the method used to produce them. The translation industry is always ready to
adopt any technology or methodology that improves translation quality and speed
while reducing costs. So translators, whether or not they like it, will have to
use MAT software. And true MT is coming, and translators should keep track of the
progress in this area.