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1.
What Exactly Is a Technical Freelance Translator?
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I
was asked some time back to write a book chapter about freelance
translators and translation technology. Not surprisingly, I started by
defining a "freelance translator" in this context. Here's what I came
up with:
"According
to Wikipedia,
a 'freelancer'
is 'a person who is self-employed and is not necessarily committed to a
particular employer long-term. (...) The term freelancing is most
common in culture and creative industries [such as] music, writing,
acting, computer programming, web design, translating and illustrating,
film and video production, and other forms of piece work which some
cultural theorists consider as central to the cognitive-cultural
economy.'
"With
translators listed directly in the middle of groups identified as
typical freelancers, we need to further narrow the distinction between
literary and technical translators. 'Technical translation' is defined
according to Sofer (The Global
Translator's Handbook. Lanham:
Taylor Trade Publishing, 2012, 20) 'by asking, does the subject being
translated require a specialized vocabulary, or is the language
non-specialized?' A sampling of areas in which technical translators
are active includes aerospace, automotive, business/finance, chemistry,
civil engineering, computers, electrical/electronic engineering,
environment, law, medicine, military, nautical, patents, social
sciences, and telecommunications (ibid., 67f.).
"The
diversity of fields for technical freelance translators is reflected in
other areas of diversity as well.
"First,
there is a wide array of commitment to the task of technical
translation, ranging from voluntary, occasional (paid), and full-time
translators. In the context of this contribution, we will consider only
technical translators who make a substantial part or all of their
livelihood by performing translation for one or -- more typically --
many clients. These clients could be translation agencies that
subcontract to individual freelance translators or direct clients who
hire freelance translators without a mediating actor. End clients may
range from large international organizations to individuals who need to
have personal documents translated.
"Second,
the most natural area of diversity originates in the many different
language combinations. Both source and target languages differ greatly
in how they are supported by technologies. This includes
- access to dictionaries and/or corpora
- spell- and grammar-checking
- input methods (including voice
recognition)
- morphology recognition
- machine translation
- the applicability of technologies that
rely on parameters such as space-based word delimiters or fuzzy term
recognition in languages with no traditional word boundaries or no
inflection
"Third,
there tends to be a correlation between the translated languages and
the location of the translator. In turn, the location has an impact on
the access to various kinds of technologies, from limitations to online
resources applied by service providers or political control or simply
prohibitive costs.
"And
finally, the nature of each translator's specialization also results in
differing technology requirements, including potential limitations of
using certain technologies that may not match security protocols or
regulations or a particular high (or low) appreciation of very specific
terminology with its corresponding technology requirements.
"Given
all this, the following observations are by necessity generalizations
about the members of this diverse community."
Is
that how you would define (professional, technical) freelance
translator? I'd be eager to hear some feedback.
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2.
Equipping Yourself
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I
had an interesting talk with Deepinder ("Deep") Singh last week. Deep,
a veteran localization product and localization manager at Dell,
partnered with Prasoon Rana, who has had a long career at SAP, to form Prudle Labs.
After reading their website, you -- like me -- might still have a hard
time actually understanding what Prudle Lab is all about, but I think
it's worthwhile to look at. Especially if you are a small or mid-size
language service provider who knows that there is a very significant
gap between what you can offer on the high-end technology spectrum in
comparison to your much larger competitors and/or what your client
might expect. You might have your translation processes nailed down,
have adequate linguistic quality assurance processes, and even use a
good supporting tool set. But when it comes to the internationalization
of some applications, localization of an app, or the implementation of
technical quality assurance, you might not feel as confident. This is
where a company like Prudle Labs might just be helpful.
The
Prudle founders looked at their combined experience at Dell and SAP
(two of the largest translation and localization buyers and
practitioners), identified the typical weaknesses (corporate speech:
"pain points") in their processes, and built a chain of products and
processes that specifically address those and everything else in the
process. Now, Prudle Labs would be happy to work directly with end
clients to do the whole shebang (i.e., lifecycle from
internationalization to localization/translation, quality assurance,
and product launch). After talking with Deep, however, I realized that
they -- and comparable companies -- might fit much more seamlessly into
an existing infrastructure by supplementing the technical capabilities
of the myriad translation agencies out there that quite frankly sit out
a lot of opportunities because they don't think they have the expertise.
Last
week I sent out a tweet
that garnered remarkable Twitter-tention:
"Just
spoke with a translation agency that's successfully been in business
for 20 years and that has never really used translation technology of
any kind. Not as an example to follow but to show that the world of
translation is very diverse, more than we sometimes think."
I
was not talking about an agency that translates only high-end marketing
or other highly creative, non-repetitive materials; this agency
actually offers technical translation. They'd never gotten around to
implementing translation technology and had been doing fine. Could they
have been more successful with a robust set of technology? Probably,
maybe even likely so. The point is this: They've been doing okay (and
probably had very happy contracting translators who kept the benefits
of using technology all to themselves), and they're not the only ones.
In fact, there's a broad range of translation companies with every
possible combination of technical readiness, but only very, very few
are technically equipped (either in experience or equipment) with
everything that's out there. A company like Prudle Labs offers the
possibility to focus on the areas in which you are strong and find
experienced partners for the rest.
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Across
Quick Tutorials
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Translator Edition?
If so, take a look at our new YouTube channel. The channel features
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3.
The Tech-Savvy Interpreter: It Finally Happened...AI Tried to
Replace Conference Interpreters (Column by Barry Slaughter Olsen)
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Tencent's
AI Interpreting Fail
At
this year's Boao Forum
in China's Hainan Province, Chinese internet giant Tencent rolled the
AI dice and unveiled its speech-to-speech translation system with great
fanfare. According to Harry Dai, Vice-Dean at the Graduate Institute of
Interpretation and Translation at Shanghai Foreign Studies University,
the announcement sent shockwaves through the professional interpreting
community in China in the lead up to the Forum. In the press, there
were claims that the system achieved 97% accuracy. Interpreters were
worried. (Watch Harry's presentation at the 22nd
SCIC Universities Conference here,
starting at 5:43:46)
Then,
as reported by
the
South China Morning Post,
the speech-to-speech translation system "made an error-filled debut"
and "spouted gibberish" displayed on a screen in the conference venue
and on a special WeChat app. Upon hearing the news, nervous
interpreters were quick to express their relief and even gloat about
the tech disaster on social media. (Check out the Slator story
on
the topic
to see some examples of the gibberish produced and interpreters' snarky
responses to the debacle.)
I've
been monitoring the mainstream and tech press for articles about
technology and interpreting for years, but I'd never seen a bomb drop
like this one before -- the use of speech-to-speech translation at a
high-level international forum to replace highly-trained conference
interpreters. That took some serious bravado or serious ignorance about
what simultaneous interpretation requires to be done successfully, or
perhaps a bit of both. But I can't help but wonder where Tencent
acquired so much faith in their system to accept such a high-stakes
debut on the international stage. All other demos I have seen from the
likes of Google
and Microsoft,
were carefully orchestrated and short. They were crafted in an effort
to create that "wow factor" that makes everyone think that the
technology can do more than it really can when the conversation goes
beyond simple greetings and pleasantries.
Interpreters
everywhere were quick to point out that this massive failure of AI
applied to language interpretation is evidence that we are not going to
be replaced anytime soon. And they are right. However, this story is
noteworthy for another reason -- end user expectations. Tencent surely expected
its technology to work. Many tech analysts and investors expect
AI to replace interpreters. And more and more audiences expect
to be able to have easy access to interpreting services, human or
otherwise in an increasing number of settings. Although the story of a
major AI-powered speech-to-speech translation fail should calm our
fears of being replaced, it should also motivate us to find new, better
and more convenient ways to provide our service. Stubbornly putting all
our professional eggs in a basket that hasn't changed much in 70 years
is a bad idea. We need to diversify.
Microsoft,
Google, IBM, Facebook, Amazon, Alibaba, Tencent, Baidu, and many other
technology companies are actively developing and marketing neural
machine translation platforms that can be connected to speech
recognition and speech synthesis programs. To be sure, there will be
more attempts -- and more failures -- when it comes to speech-to-speech
translation. My hope is that they will be matched by more attempts --
some successful, others not to make high-quality human interpretation
available in new ways and in new settings. In an ever-changing
multilingual communications landscape, our relevance as a profession
depends on it.
Do
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 inquiry@interpretamerica.com.
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4.
Quo Vadis, Content?
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Two
years ago, in edition 261 of the Tool
Box Journal, I wrote this about ContentQuo:
"MultiQA,
the quality assurance and terminology tool that I highly praised in June of 2013,
had a rather rocky road but one that might still eventually lead to
greener pastures.
"Briefly,
it really was never widely used except internally by ITI, the Russian
company that developed it. To put it mildly, that's something I simply
don't understand. While it was not a super-easy tool to use, it
excelled at areas where it virtually had no competition, especially in
morphology-based terminology recognition.
"Anyway,
the result is that it's not available anymore to the general public.
That's a bummer. But it has been taken over by ContentQuo, a startup
company led by Kirill Soloviev. Kirill's vision is to offer a kind of
quality assurance that is more holistic than just looking at individual
linguistic parameters, instead taking into account key performance
indicators and user response through web analytics as well. You can see
some more on that in this presentation."
Just
this week I touched base with Kirill, who is finally about to launch a
product by the end of this month. Why did it take so long? Well, partly
because he really had to rethink his concept. Did it make sense to look
at localization from a results-oriented perspective (Kirill called it
"outcome-based localization)? Absolutely! After all, if there are fewer
complaints about a translated support database, better response rates
with localized email campaigns, or higher response rates in apps, this
could and should be seen as a result of successful localization. (Of
course, the opposite is true as well.) Why did this concept not work
(at least yet)? Because data tends to be siloed in organizations. With
no data to access, there are no insights about success.
So,
Kirill went back to the drawing board and came up with a different
product with more "mundane" features (his words, not mine).
ContentQuo
is still a product with great ambitions, but rather than having to rely
on data that is difficult or impossible to access, it uses data
generated through its own interface.
It's
a TQM (Translation Quality Management) platform that offers an
interface for the review of translated bilingual files (XLIFF in all
kinds of flavors, a number of current and legacy
translation-environment-tool-specific formats, but also CSV files and
TMX and TBX files). The reviewer can apply the quality metrics of the DQF-MQM error
typology
that can be tailored down to the needs of the customer and applied to
any part of the segment. (The latter fixes a shortcoming in some other
implementations I've seen that allows the reviewer to assign an error
only to the complete segment.)
If
the file is not only assessed (by applying error categories, severity
grades, and comments) but (optionally) also edited, the changes are
displayed in an MS Word-like tracked changes format; if so desired, it
can also be saved and pushed back to the originating location or
system. (This last step presupposes that the entire file is being
reviewed in the ContentQuo
environment rather than a sample, which is more likely to be the
default option.)
And
why push back to a "system"? The idea is that ContentQuo
will (eventually) connect with and to third-party translation
management systems (Plunet, XTRF, WorldServer,
etc.), which will then allow for a direct connection between the
quality management interface that ContentQuo
provides and the location where the translation management stores the
translated files. This will be done via API connectors that will be
developed by client demand and then made available to everyone (the
goal is to have two to four of those by the end of this year).
Another
area where connectors are to be developed is with automated quality
assurance tools, such as Xbench
and Verifika,
so they can be brought into the process as well. The morphological
abilities of the features that were part of MultiQA
will be brought into ContentQuo
in that manner, as well.
Applying
the quality metrics -- whether automated or manual -- results in an
overall quality rate expressed as a percentage (every error equates
with a deduction from the ideal 100% grade). It's up to the client's
quality manager (or perhaps more likely, the localization manager) to
determine what is considered a non-acceptable failure rate. The results
of the QA processes are displayed first by language and then by
translator/project in a portal that also allows for quick access to the
actual review interface. Translators (and reviewers and arbiters -- who
are called into situations of possible disputes between translator and
reviewer) have their own access to the system where they can see what
evaluations they have received (and why) and also have the possibility
to respond. The interaction between the different parties can either be
anonymized or attached to actual identities.
ll
of this is hosted in the cloud -- or can be hosted onsite if so
desired. ContentQuo
is a Estonian company, its development team is located in Russia, and
the cloud is hosted in Germany. The pricing for this obviously
SaaS-based offering starts at 99 euro a month (with educational and
nonprofit discounts) and relates to the number of words processed
through the system.
Since
clients might need some outside expertise to determine certain details,
like what part of the DQF-MQM metrics to apply or what fail rate to
execute, Kirill from ContentQuo
is happy to refer to a group of third-party quality experts who can aid
that process.
And
who are the clients? While Kirill is presently focusing mostly on
larger enterprise clients, it could (and, he hopes, will) eventually
include LSPs and translation buyers of all sizes.
I
failed to discuss with Kirill when I talked to him what a fantastic
tool this would be as part of a platform for translators where they
could openly show their expertise and quality ratings for specific
kinds of projects or industries. Anyone willing to tackle
that?
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5.
This 'n' That
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The
2018 meeting of the
European Association for Machine Translation (EAMT)
will be held in Alacant/Alicante, Spain, on 28-30 May 2018, and will
have a translators' track for the first time. I've been told that
Alicante is not a bad place to be at the end of May....
xl8.link,
the link shortener just for translators and interpreters, is open again
on a new platform. We previously had problems with phishing attacks, so
we hope this change will deter any evil phishers. Either way, any URL
you've already shortened or will shorten will continue to work.
Microsoft
has released a shiny and highly accessible new Writing Style Guide
for English. This is obviously a highly important resource for anyone
translating Windows-
or otherwise MS-based software and related data into English. There are
also style guides for other languages,
but unfortunately they're not nearly as comprehensive and easily
navigatable. (And the English Apple
counterpart can be found here
-- thanks, Andrea Bernard,
for the tip.)
Amazon Translate has now officially opened up access to its
neural machine translation engine in English <> Arabic,
Chinese (Simplified), French, German, Portuguese, and Spanish. At this
point it appears that it's primarily geared toward larger service
providers rather than individual translators, and that might be a good
thing because it looks like Amazon has not yet caught up with the
latest developments in data privacy. If you look under "Privacy" right here,
you can see that data submitted through Amazon
Translate is used for purposes
other than your own, which is so "last month" (when Microsoft finally
joined Google and DeepL in no longer doing this as long as the data
comes through an API connection).
One
more thing about MT. GT4T,
the little tool that gives you access to a large number of MT engines,
has added access to DeepL Pro.
It had already provided access to DeepL
for some time, but that was through the non-API access and was
therefore not confidential. Another reason why this relevant is
because, as I reported in the last Tool
Box Journal,
DeepL is asking for a 20-euro-a-month minimum payment for the use of
the translation engine that otherwise uses a character-based payment
schedule. Since this might not be very cost-effective for some, access
through GT4T
might make more sense. This works because GT4T essentially acts as a
reseller for DeepL and can afford to include the DeepL data that its
users are using through its licensing fee. (Note that DeepL
Pro only works if you have a
character-based license with GT4T
rather than a time-based license -- because this might "bankrupt" GT4T,
as Dallas Cao, the developer, remarked to me.)
The
only drawback I see to using MT through GT4T's
interface is its lack of adequately deep integration into the
translation tool environment. Once again, my
greatest benefit from MT is not the MT'ed suggestion of the whole
segment but the more granular data that I can choose to use or not,
ideally by the system suggesting these fragments based on what I have
already entered. If GT4T
could only provide that!
Dallas?
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The
Last Word on the Tool Box Journal
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2018 International Writers'
Group
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