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might have asked yourself: Shouldn't this Tool Box Journal have
been in my inbox about a week ago? And, yes, that would an adequate
question to which there is a very deepL answer.
have been waiting for almost two weeks to talk to someone at DeepL/Linguee
about, well DeepL (which, by the
way, is pronounced deep-l). They've been a little overwhelmed by the
immediate attention they received when, with great fanfare, they
released their neural machine translation engine between English,
German, French, Spanish, Italian, Polish, and Dutch. This new engine is
producing results that in many cases seem to be better -- and sometimes
significantly better -- than both Google Translate and Microsoft
I was finally able to talk with Jaroslaw Kutylowski, DeepL's CTO,
today, which allowed me to complete this Tool Box Journal.
was a lot Jaroslaw could legally and strategically not say, but what he
could say was still very interesting. For instance: When I asked him
how long they've been planning to have a machine translation engine, he
told me that it really only started to occur to them about a year ago,
when neural machine translation first popped up and became everyone's
favorite topic to talk about. It does make sense, though, that the
company known for Linguee (the whole company is actually called
DeepL now with Linguee being one of its products) is using its
somewhat curated corpus in the EU languages plus Russian, Chinese, and
Japanese to train a neural machine translation engine. How much the
curation/editing/dictionary building plays a role in the relatively
high quality according to the company's own estimation was among the
questions for which I received no answer.
there was no time commitment to the immediate roadmap, there is
particularly much emphasis on adding new languages, which likely will
be the ones that are already covered by Linguee but could
include others as well. What also is on the roadmap is the development
of an API (there is an unofficial and unwelcome one right here, but you
would be not well-advised to integrate that since that will be blocked
soon by DeepL). The API, or application programming interface,
is particularly important if DeepL is to be used by
professional translators who would want to use it not on a webpage but
integrated into a translation environment tool. And at that point of
the discussion I knelt down (actually I didn't, but I would have if
that would have made a difference) and asked that in exchange for
payment for this use of such API, DeepL would commit itself to not
using the data that is being translated for training purposes (which it
does right now) -- along the lines of what Google Translate
does. And while Jaroslaw did not completely commit himself, he said
there would be a very great likelihood that this indeed would happen.
This is really, really good news because this is what makes this
essentially usable for translators. Microsoft has unfortunately never
embraced that idea, using the data you upload through its API for
training purposes (which some of your clients might not mind, but many
others will, no matter how much Microsoft assures us that it will only
happen in a high level and temporary manner). Google has understood why
it should not do that, and now seemingly DeepL has as well.
also asked whether there were champagne corks popping at DeepL's
headquarters in Cologne when just a few days after DeepL was
launched Google announced new developments for their machine
translation system. It seemed to me the timing was just too close
to be coincidental -- but who knows. (DeepL doesn't know either, so the
champagne question also went unanswered, but I'm pretty sure if this
had been a video call, there would have been a knowing smile on
thing I had been very surprised about was the "voice" of the announcement
-- which was decidedly un-German and very American in the sense that it
was rather uncompromising (the actual term that came to my mind was
"hyperbolic"). Well, Jaroslaw said, you have to be self-confident when
you have good reason for it, even if it might not match the prevailing
culture of where you are. And I guess that's a pretty healthy way of
looking at things.
to come back to the quality, you can see some numbers in the press
release that show impressive quality gains, which partly matched my own
-- very subjective and limited -- testing. Particularly when it came to
advanced technical and semi-technical texts, the quality of the
English> German direction was decidedly better and more natural than
Google's and Microsoft's neural output. When it came to relatively
high-brow press material, however, the pendulum seemed to swing the
other way. But again, the sample I had was obviously very small.
is something I found interesting and reassuring. When we first looked
at the neural outputs that Microsoft and Google have been producing in
comparison to their earlier statistical engines, they seemed oh-so
elegant and fluid. Suddenly, though, when compared to this, they looked
terrible again. This reminded me how easily impressed we are with
advancements but then often forget to look at the results in their own
right to realize that they all have a very, very long way to go,
is one interesting little experiment some of you have probably also
already tried: If you retrieve data from Linguee to have it
translated by DeepL, you do not get the existing translation
that already can be found in Linguee and that DeepL was trained
on but a completely new one, all done with the neural computer "brain."
Chances are this would have been different if they had built a
statistical machine translation program where the original fragments
might in fact have been reassembled -- but that's just not how neural
MT works (see also the article about Google's statistical MT in this Tool
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the most important utility you need to be able to receive and send
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compressed format, or receiving one file instead of many (including a
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The Tech-Savvy Interpreter: The Four-Legged Stool of Remote
Interpreting (Column by Barry Slaughter Olsen)
difficult to identify a tipping point before the tip occurs. I don't
claim to have a crystal ball or clairvoyance, but 2017 sure feels like
a tipping point for remote interpreting from where I stand. I've been
following this market closely since 2011 when InterpretAmerica
identified remote interpreting as a force that had huge potential to
both expand and disrupt the interpreting profession.
me explain what I see. To do so, I'll make use of a familiar analogy --
the four-legged stool. When the stool has all four legs, it is firm,
solid, and works well. But remove one or more of the legs, and it
becomes unstable, tips over easily, and really isn't good for much.
the legs of the stool, four elements must be firmly in place for remote
interpreting to be sturdy, dependable, and work well. They are: the end
users, the technology, the interpreters, and language service companies
service provider can exist without customers, and at its core,
interpreting is a service. That remains true no matter where or how the
service is provided, for whom, or by whom. Without this leg, the
other three have no reason to exist, which is why I list this leg first.
i.e., end users, have been asking -- many clamoring -- for new remote
interpreting delivery models for years. In some cases, they want remote
interpreting because of shrinking budgets and growing mandates. In
others, the current face-to-face interpreting delivery models simply
don't provide the service fast or conveniently enough. Finally, end
users are asking for interpreting services for virtual meetings that
take place online. Traditional delivery models simply are not set up to
provide interpreting in these circumstances.
all this means is that the demand is there for both remote consecutive
and simultaneous interpreting. End users both need it and want it.
decades, the technology leg of the remote interpreting stool was either
totally absent, unstable, or prohibitively expensive. It was easy to
dismiss the idea because we could simply say "the technology just isn't
there yet." That is clearly no longer the case. The technology is here
and it works. The cost has also come down dramatically. These facts are
now forcing interpreters in all settings and specializations to take a
long, hard look at how we work and why.
number of technology offerings for remote interpreting delivery has
increased quickly in the last two or three years. This has happened
because foundational telecommunications technologies are making
high-quality audio and video transmission over the Internet available
to anyone with a broadband internet connection and a video and audio
enabled endpoint (from a telepresence studio to a smartphone and
everything in between). It is as though we now have a blank digital
canvas that innovators are designing and creating on with few things
constraining what is possible.
the early stages of this digital wave, it was the outsiders who were
trying to disrupt the interpreting space. As the technologies have
gotten better and the use cases more specific, remote interpreting
startups have sharpened their focus and are gaining traction. But
perhaps the most telling development in this area in recent months is
the announcement by leading simultaneous interpreting equipment
providers that they have launched or are close to launching their own
remote interpreting platforms. (See
KUDO, launched by MediaVision CEO Fardad Zabetian
announcement made by Congress Rental Network President Gilles
Goudreault earlier this year.)
When the incumbents tip their hand, it's a safe bet that the tipping
point has arrived or is fast approaching.
third -- and most important -- leg of the remote interpreting stool is
the interpreters. Remote interpreting has received a cool to outright
hostile reception among many practicing interpreters whether in
clinics, courtrooms or conference centers. And with good reason in some
pressure on interpreters comes from several quarters. Interpreting
remotely, audio only or with video, requires additional skills and
cognitive tasks that take training and practice to obtain. Typically,
employers and end users are ignorant of the stress this puts on
interpreters, who have to play catchup at a time when very little
training is available.
healthcare interpreting, we are seeing a trend in some hospitals to
replace their entire on-site interpreting staff with remote
interpreting. Either on-site interpreters are asked to sit in a room in
front of a video screen all day or they are summarily fired and a
remote interpreting service is brought in. The lack of best practices
and guidelines for employers and end users has a real, negative impact
on interpreter working conditions in these circumstances.
remote interpreting requires interpreters to make a
sometimes-significant investment in equipment to set up a remote
workspace that meets the often-stringent privacy requirements of
years past, I had observed a generally dismissive attitude regarding
remote interpreting. With its increasing penetration into the market,
however, that attitude is beginning to evolve into one of concern,
curiosity and increasingly, tentative acceptance. Examples of changing
attitudes include the last two congresses of the International
Federation of Translators (2014 - Man vs.
Machine? The Future of Translators, Interpreters and Terminologists,
2017 - Disruption
the recent meeting of the AIIC
Private Market Sector Committee (PriMS) in Cartagena,
Colombia, that revolved around the topic of remote interpreting; and a
recent podcast from the Troublesome Terps on remote interpreting.
I now see a growing, honest, more even-handed debate of the topic and a
possible rapprochement. Standards bodies like ISO/TC37/SC5
Committee F43 on Language Services and Products
are continuing their deliberations to set standards for different types
of remote interpreting.
all these developments point toward a reluctant warming up to remote
interpreting on the part of interpreters, one thing remains abundantly
clear -- no remote interpreting service will be successful without
qualified interpreters who are able to do their job in a professional,
dignified manner. With few exceptions, the field is currently failing
in providing the tools interpreters need to justify the effort and
investment needed to make the adjustment to remote interpreting. That
is why the interpreter leg of the stool is currently the most unstable.
I originally developed this analogy, the stool had only three legs.
I've come to the realization that the stool was missing a vital fourth
leg -- language service companies (LSCs). They most often function as
intermediaries between end clients and service providers. offering a
valuable service to both end user and interpreter. This is a role that
is only growing in importance as the frequency and number of
interpreted interactions increases, the duration of these interactions
becomes shorter, and the number of language combinations multiplies. It
is unrealistic to expect individual interpreters to handle the
logistics of such complex interactions or to expect large end users to
maintain individual contact with interpreters in every language
combination they require.
LSCs are developing the workflows and delivery platforms needed to
provide the organizational and administrative services and the
technological platforms that match the right interpreter with the right
end user quickly and efficiently. Because of this, the role of LSCs in
the remote interpreting space is likely only to grow.
said, enterprising interpreters have and will continue to develop
direct relationships with key end users of their services. This will
likely be the case in the remote interpreting space as well, but the
cadre of interpreters with direct clients will be small.
6: Bringing the Stakeholders Together
interpreting is moving forward and being adopted by more and more end
users. The pace of this change is astonishing. There are examples of
implementations of remote interpreting that have gone terribly wrong
and others that are going right and are expanding access to this vital
the rollout of remote interpreting services in many instances is taking
place with few to any guidelines at all, and interpreters are feeling
the pressure. This is why InterpretAmerica has issued a call to action
to all parties that have a stake in remote interpreting. On October 30,
2017, InterpretAmerica will convene its sixth Summit (You can
learn more about InterpretAmerica 6 here).
This one-day working meeting will convene (on-site and online),
interpreters, professional association leaders, industry leaders,
technology developers and end users in pursuit of an ambitious goal: to
put into motion a credible process for the creation and implementation
of best practices for remote interpreting - across all interpreting
specializations and stakeholder groups.
opens on September 18, 2017. Given the working format of the Summit,
registration will be limited to 50 participants on-site and 80
participants online. However, a livestream of the two Summit plenary
sessions will be available to a larger audience. On-site and online
participation will be very similar with the exception that online
participants won't be able to join on-site participants for lunch in
DC. That aside, all participants will be engaged in the same tasks and
working together, all remote interpreting stakeholders can contribute
to making that four-legged stool sturdy, dependable and work well for
everyone involved. I hope you'll join us!
you have a question about a specific technology? Or would you like to
learn more about a specific interpreting platform, interpreter console
or supporting technology? Send us an email at email@example.com.
at the Cutting Edge of Interpretation
interpreting market is constantly evolving, with new customers, new
technologies, and new modes of delivery. Join Cadence to keep your
finger on the pulse of the industry and discover exciting new
opportunities to work, learn, and grow.
to register and learn more.
Google Translate's Dual System (Premium Edition)
post by Emma Goldsmith
about the GT4T
tool mentioned something that sent me on a little bit of a wild-goose
talking about wild geese, and Canada Geese, in particular: I live at
the Oregon coast, very close to the path of the total eclipse a couple
of weeks ago. For the great event we went to a large pasture along the
majestic Umpqua River where a herd of about 100 wild elk graze. About
60 or so were there on that day, and it was beautiful to see how they
responded to the increasing twilight and dropping temperatures -- they
were clearly confused, first laid down, then got up again, and
eventually hid in the forest. Everyone and everything else was
similarly impressed. Even the birds and insects quieted down. Except,
you guessed it, for the silly Canada Geese who apparently had not
gotten the memo and squawked through the whole event -- very eerie.)
was I? Oh, yes, the wild-goose chase.
mentioned that GT4T, a tool that selectively offers access to a
number of machine translation engines, offers access to two different
kinds of Google Translate data -- from the neural machine
translation engine and from the statistical (phrase-based) machine
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really have not written much about Smartling in this
publication. There were a number of reasons for that, chiefly that it's
a product primarily marketed to translation buyers. While there are
many language service providers and individual translators who work
with and on it, that decision is made if they work for a client who
been following Smartling very closely, though, and have had
many conversations with its CEO Jack Welde over the years.
recent announcement about the "Quality Confidence Score" seemed very
relevant for all of us (Common Sense Advisory's blog
post gives a good overview).
here is why: On and off I've mentioned Memsource's reports on
translation practices derived from across its cloud-based network (here
is a recent example). Rather than looking at individual clients
using the tool, they look at anonymized data from all clients and are
able to draw conclusions from that data how the average translators
interact with the environment and the features provided by Memsource.
used that kind of network-wide approach in its cloud (in which about 7
billion words have been translated so far) to build up a set of
parameters that all contribute to an assessment of the quality of any
and every translation project. Sound crazy? Not really, if you think
about it. While Smartling is not releasing any final list of those
parameters, they have shared that some include the expected ability to
produce quality work by translator and editor (based on past projects
done in the Smartling cloud), duration of edits, comparison of
length of strings, handling of codes, typos, and machine-detectable
grammar issues, and so on and so forth.
resulting quality score for each project signals to either the language
service provider or the translation buyer what to do with the
translation. (Assuming the score can be trusted,) a high score might
mean that the translated data can be immediately published or that the
edits really were unnecessary; a low score might suggest that another
proofreading step has to be introduced or linguists have to be
switched, or . . ..
client never becomes identifiable in the data being looked at, although
the translator, editor, and any other linguist working on the material
does, at least internally. While this data is not being made explicit
or public, it forms part of the score being given to any translation.
called this process "something between a statistical regression model
and computer learning," and while it gives me pause and makes me wonder
whether this is desirable from the viewpoint of the individual linguist
(who of course always has the option not to work within that network),
I don't think it's a problem per se and just continues something that
has long been in the making.
fact, I recently was interviewed by a journalist about how computer
learning and artificial intelligence affect translation. She was
thinking of machine translation -- which I happily talked about with
her -- but only after I told her that there are many other areas within
the translation process and workflow that have also been affected by
advanced computer processes. Starting from dynamic spell-, grammar- and
style-checking to job assignments to deeper data-driven insights about
how translators actually work.
of stuff to think about, huh? I'm glad I was able to provide you with
something to do this weekend!
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