Mental health interpreting is an important subset of study, since the issues encountered intersect with both medical and legal interpreting theory, practice and Code of Ethics. It involves complex and intimate interpersonal communication with individuals who may act, speak or think in unusual ways, and there are laws that may require the interpreter to break confidentiality or intervene. Surprisingly enough, mental health interpreting issues have not been properly studied or researched, and are scarcely mentioned in even the most prominent medical publications, such as the Diagnostic and Statistical Manual of Mental Disorders, used by clinicians to diagnose mental disorders. Even within the interpreting community, as of this publication, there are no official positions in the United States on the role of the interpreter or code of ethics in Mental Health.The objective of this book is to help interpreters build upon their expertise and prepare themselves to better interpret in the mental health field.
Buy it here:
Contact author here: https://www.facebook.com/ariannamaguilar?__tn__=%2CdC-R-R&eid=ARC-YDGYC2jVs75X1E_lJU8A5_uov9pGDs0eO3vyWAV6rn-rDOFEy212Kk7_SU3vxu6ASCCNKcSA74ip&hc_ref=ARStxx74wUmHrBrnp3pTGrDem0GgHxYpbCOshCICFcfwrc03zjJXXFLUseQ3qPSETdo&fref=nf
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InterActive Terminology for Europe (IATE) has released an updated version of its terminology database. From http://termcoord.eu/ :
IATE, the EU’s interinstitutional terminology database was developed in the early 2000s. Despite its continuous development and maintenance ever since, due to technological evolution and changes in institutional terminology work, the need for a new, upgraded IATE has become clear. The development of IATE 2 started in 2016 and a brand-new version of IATE has been launched on Monday 12 November 2018.
->PRESS RELEASE AVAILABLE HERE<-
With 50 million queries per year, it is a highly popular tool for anyone looking for accurate terminology. It contains over 8 million terms in the 24 official EU languages and in a wide variety of subject areas.
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Microsoft Translator has been upgraded to offer artificial intelligence (AI) powered offline language packs across all Android, iOS, and Amazon Fire devices. The AI-powered offline language packs were previously limited to devices with a dedicated AI chip. However, Microsoft engineers have now leveraged edge computing to bring AI-backed neural machine translation (NMT) to the masses. The Translator team is also in plans to bring the new experience to Windows devices in the coming future.
The Redmond company in 2016 built its NMT model for online use only as it required high-quality translation models. But in 2017, the experience debuted on select Android devices that are equipped with a dedicated AI chip. It brought offline translation quality in line with the quality offered by the original online neural translation model. And now, the Translator team has optimised the initial offline-specific algorithms to bring language packs irrespective of any particular hardware.
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Amazon announced this month it was releasing “Amazon Translate”. From the announcement:
Today we’re excited to make Amazon Translate generally available. Late last year at AWS re:Invent my colleague Tara Walker wrote about a preview of a new AI service, Amazon Translate. Starting today you can access Amazon Translate in US East (N. Virginia), US East (Ohio), US West (Oregon), and EU (Ireland) with a 2 million character monthly free tier for the first 12 months and $15 per million characters after that. There are a number of new features available in GA: automatic source language inference, Amazon CloudWatch support, and up to 5000 characters in a single TranslateText call.
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In case you had not noticed, you can now log in to your ProZ.com account using your Google, Facebook, or LinkedIn credentials.
These log in options are provided to make it easier to jump to your ProZ.com account, or to access your account in the event of a forgotten password.
If you run a website or application and would like to allow your users the option of signing in with their ProZ.com accounts, you can find out how here.
A recent survey of freelancers centered around their marketing efforts showed some interesting finds:
- 55% of freelancers spend 3 hours a week on their online marketing efforts
- 51% of respondents considered marketing too time-consuming, and 41% felt marketing was too costly
- 83% are investing financially in online marketing of some sort
- 72% say they are spending less than but up to 100 USD a month in marketing (those who spend more than that report earning more)
- The average survey respondent had reached their income goal within two years of starting out
The survey sample were some 2,000 US freelancers of all types, so it is reasonable to expect those numbers to be somewhat different if we narrow it down to translators and interpreters, expand the sample to other countries, or both.
One number in particular that caught my eye was the monthly investment in marketing. 100 USD a month sounded pretty steep to me, but maybe I’m wrong. 1,200 USD in freelancer marketing a year. Do you spend that much on your marketing? If so, drop me a line, I’d be very interested in hearing about it and if you find it to be a good investment.
If you are already a paying ProZ.com member, you are spending between 12 and 18 dollars a month on marketing through your membership, though you get all the rest of the tools and opportunities available along with it. It’s a kind of marketing that is easy to do, what we’d call passive marketing.
Be an ant, not a grasshopper
For some kinds of work, sending CVs, applications, emails, calling or meeting potential clients, printing business cards or flyers, posting ads, and all of that active marketing, can be effective. Many freelance translators and interpreters find that kind of marketing tiring, frustrating, and also expensive, both in terms of money and time. You’d rather be translating or interpreting, right?
You may have to rush to do active marketing if you suddenly find yourself short on clients or workload. This tends to happen when a freelancer has no kind of marketing in place while they are fully-booked, a bit like the grasshopper who watched the ant stock up for winter, unworried during the summer because food was plentiful, and then sorely unprepared for the winter.
Passive marketing is your ant stocking up for winter. It can help save you from the unexpected, even though work might be plentiful now. And sometimes it’s a gateway to new opportunities that can pop up and replace what you’ve got going on with something even better.
Where is your shop window?
As I said, passive marketing is easier to do, if you do it right, and the time/monetary investment is quite different too. It basically consists of opening up a brightly-lit shop window (your online presence) on a bustling street. Many people walk by, window shopping, but if your shop has the right goods (your services, expertise, samples, things that make you stand out), shoppers will pop in to look and talk to you. Some will be interested in buying now, some will simply make a note of your shop for when they do need what you have to offer.
Where is that bustling street, though? Well, ProZ.com is one of them. You should have a professional online presence in any serious work-related venue for language professionals (a profile on LinkedIn, for example). But since ProZ.com is the busiest street when it comes to searching for and finding language professionals, if you are not figuring there as prominently as possible, you are definitely missing out on client contact. So that ProZ.com membership, roughly the cost of a new pair of shoes per year, is all you need to keep your shop window on the busiest street in the industry.
Check your directory ranking in your top language pair and area of expertise. What page of the results are you on? How many pages of results will your ideal client browse through to get to you? They say, “The best place to hide a dead body is on page 2 of Google search results.” Directory results work in a similar way. Chances are, by the time a client has gone a few pages in, they’ve already found the people they are looking for.
Don’t waste my time
Now, when I say “online presence” I don’t mean having a profile registered on a place and having the bare minimum of information filled out there. Nowadays, if I’m looking for a service/service provider online, I don’t even look twice at people who have not put some time investment into presenting their services. No picture? No thanks. No real name? See ya. No details about the services you offer or why I should choose you? Don’t waste my time! This is where the time investment comes in. It’s mostly an up-front investment. Put in the time to craft that presentation, then go do whatever else you want, and let it go to work for you in the background.
Now think about your two biggest clients…
I’ve got all the clients I can handle right now, no need, you might say. OK! But how many times would you try to go back to a shop that was closed every time you went there?
Now, think about your two biggest clients. Would you be in trouble if tomorrow, through no fault of your own, you lost those two clients? If so, why not put your shop window out there, and occasionally field an inquiry from an interested potential client? The worst that can happen is that you’ll make some new contacts while you’re working, and heaven forbid your fully-booked status should change, you’ll have some good leads to work with.
What’s in your shop window?
Now go over to your ProZ.com profile. At the top of your profile you will see a link to “Force visitor view”. Click on that. What you see is what any visitor to your profile will see when they are evaluating working with you. Put yourself in the shoes, or eyes, of your ideal client. Does what you see there look professional, attractive, keep your interest, “sell” you on the idea of contacting this person with a work offer? Does it speak to that person’s strengths, what makes them different from the competition?
By the way, if you are looking for ways to build, update, or fine tune your online presentation, many of the same principles of decorating a real shop window apply! Thinking about it this way may also help get your creative juices going. If you need some inspiration, you can find some pointers here:
Croissant is an app that allows its members to try out nearby coworking spaces at a reduced cost. In exchange for a monthly subscription, users can book participating coworking spaces depending on the number of hours they plan to use, with the option to rollover unused hours.
If you are into coworking or have been thinking of trying it out and you live in or near one of the 40 cities that are currently covered, this might be worth checking out. They offer a free seven-day trial to test drive the service.
Read more on Business Insider at http://www.businessinsider.com/croissant-coworking-space-app-review-2018-3
Or on the Croissant website at https://www.getcroissant.com/
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Wordfast has released version 5.4 of its platform independent desktop tool, Wordfast Pro. Notable features and improvements include Adaptive Transcheck, a new segment changes report format, a new feedback proxy tool, and the ability to connect to Wordfast Anywhere TMs and glossaries. This latest feature puts the power of server-based TMs and glossaries into the hands of desktop users for free. Please see the release notes page for more details.
Wordfast also recently released Wordfast Anywhere 5.0 which includes a localized user interface in French and Spanish. The UI is ready to be translated to other languages with a collaborative translation page accessible through a user’s profile.
Wordfast will be showcasing the interconnectivity of Wordfast Pro and Wordfast Anywhere during its 4th annual user conference – Wordfast Forward – to take place on June 1-2, 2018 in Cascais, Portugal.
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ELRA is happy to release a new version of its Catalogue of Language Resources publicly.
Completely redesigned, with a new interface and an improved navigation, the new Catalogue allows visitors an easier access to the 1075 Language Resources (LRs) and their corresponding description. Among the new features, the Catalogue now offers an extended metadata to describe the LRs, a refined search on the Catalogue data for finding more specific information using criteria such as language, resource or media type, license, etc.
Currently, LRs can be selected, and placed in a cart from where the user can send a request for quotation to initiate the order. When logging in, the user selects LRs and obtains distribution details (licensing information, prices) depending on his/her user status: ELRA member/Non-member, Research vs Commercial organization. The full e-commerce integration will be completed at a later stage.
More functionalities pertaining to the ELRA Catalogue, including the ISLRN automatic submission and the e-licensing module (automatic filling in and electronic signature), will be developed and integrated.
Please visit this new version of the Catalogue here: http://catalogue.elra.info
*** About ELRA ***
The European Language Resources Association (ELRA) is a non-profit making organisation founded by the European Commission in 1995, with the mission of providing a clearing house for language resources and promoting Human Language Technologies (HLT).
To find out more about ELRA, please visit: http://www.elra.info.
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One Sunday, at one of our weekly salsa sessions, my friend Frank brought along a Danish guest. I knew Frank spoke Danish well, since his mother was Danish, and he, as a child, had lived in Denmark. As for his friend, her English was fluent, as is standard for Scandinavians. However, to my surprise, during the evening’s chitchat it emerged that the two friends habitually exchanged emails using Google Translate. Frank would write a message in English, then run it through Google Translate to produce a new text in Danish; conversely, she would write a message in Danish, then let Google Translate anglicize it. How odd! Why would two intelligent people, each of whom spoke the other’s language well, do this? My own experiences with machine-translation software had always led me to be highly skeptical about it. But my skepticism was clearly not shared by these two. Indeed, many thoughtful people are quite enamored of translation programs, finding little to criticize in them. This baffles me.
As a language lover and an impassioned translator, as a cognitive scientist and a lifelong admirer of the human mind’s subtlety, I have followed the attempts to mechanize translation for decades. When I first got interested in the subject, in the mid-1970s, I ran across a letter written in 1947 by the mathematician Warren Weaver, an early machine-translation advocate, to Norbert Wiener, a key figure in cybernetics, in which Weaver made this curious claim, today quite famous:
When I look at an article in Russian, I say, “This is really written in English, but it has been coded in some strange symbols. I will now proceed to decode.”
Some years later he offered a different viewpoint: “No reasonable person thinks that a machine translation can ever achieve elegance and style. Pushkin need not shudder.” Whew! Having devoted one unforgettably intense year of my life to translating Alexander Pushkin’s sparkling novel in verse Eugene Onegin into my native tongue (that is, having radically reworked that great Russian work into an English-language novel in verse), I find this remark of Weaver’s far more congenial than his earlier remark, which reveals a strangely simplistic view of language. Nonetheless, his 1947 view of translation-as-decoding became a credo that has long driven the field of machine translation.
Since those days, “translation engines” have gradually improved, and recently the use of so-called “deep neural nets” has even suggested to some observers (see “The Great AI Awakening” by Gideon Lewis-Kraus in The New York Times Magazine, and “Machine Translation: Beyond Babel” by Lane Greene in The Economist) that human translators may be an endangered species. In this scenario, human translators would become, within a few years, mere quality controllers and glitch fixers, rather than producers of fresh new text.
Such a development would cause a soul-shattering upheaval in my mental life. Although I fully understand the fascination of trying to get machines to translate well, I am not in the least eager to see human translators replaced by inanimate machines. Indeed, the idea frightens and revolts me. To my mind, translation is an incredibly subtle art that draws constantly on one’s many years of experience in life, and on one’s creative imagination. If, some “fine” day, human translators were to become relics of the past, my respect for the human mind would be profoundly shaken, and the shock would leave me reeling with terrible confusion and immense, permanent sadness.
Each time I read an article claiming that the guild of human translators will soon be forced to bow down before the terrible swift sword of some new technology, I feel the need to check the claims out myself, partly out of a sense of terror that this nightmare just might be around the corner, more hopefully out of a desire to reassure myself that it’s not just around the corner, and finally, out of my longstanding belief that it’s important to combat exaggerated claims about artificial intelligence. And so, after reading about how the old idea of artificial neural networks, recently adopted by a branch of Google called Google Brain, and now enhanced by “deep learning,” has resulted in a new kind of software that has allegedly revolutionized machine translation, I decided I had to check out the latest incarnation of Google Translate. Was it a game changer, as Deep Blue and AlphaGo were for the venerable games of chess and Go?
I learned that although the older version of Google Translate can handle a very large repertoire of languages, its new deep-learning incarnation at the time worked for just nine languages. (It’s now expanded to 96.)* Accordingly, I limited my explorations to English, French, German, and Chinese.
Before showing my findings, though, I should point out that an ambiguity in the adjective “deep” is being exploited here. When one hears that Google bought a company called DeepMind whose products have “deep neural networks” enhanced by “deep learning,” one cannot help taking the word “deep” to mean “profound,” and thus “powerful,” “insightful,” “wise.” And yet, the meaning of “deep” in this context comes simply from the fact that these neural networks have more layers (12, say) than do older networks, which might have only two or three. But does that sort of depth imply that whatever such a network does must be profound? Hardly. This is verbal spinmeistery.
I am very wary of Google Translate, especially given all the hype surrounding it. But despite my distaste, I recognize some astonishing facts about this bête noire of mine. It is accessible for free to anyone on earth, and will convert text in any of roughly 100 languages into text in any of the others. That is humbling. If I am proud to call myself “pi-lingual” (meaning the sum of all my fractional languages is a bit over 3, which is my lighthearted way of answering the question “How many languages do you speak?”), then how much prouder should Google Translate be, since it could call itself “bai-lingual” (“bai” being Mandarin for 100). To a mere pilingual, bailingualism is most impressive. Moreover, if I copy and paste a page of text in Language A into Google Translate, only moments will elapse before I get back a page filled with words in Language B. And this is happening all the time on screens all over the planet, in dozens of languages.
The practical utility of Google Translate and similar technologies is undeniable, and probably it’s a good thing overall, but there is still something deeply lacking in the approach, which is conveyed by a single word: understanding. Machine translation has never focused on understanding language. Instead, the field has always tried to “decode”—to get away without worrying about what understanding and meaning are. Could it in fact be that understanding isn’t needed in order to translate well? Could an entity, human or machine, do high-quality translation without paying attention to what language is all about? To shed some light on this question, I turn now to the experiments I made.
I began my explorations very humbly, using the following short remark, which, in a human mind, evokes a clear scenario:
In their house, everything comes in pairs. There’s his car and her car, his towels and her towels, and his library and hers.
The translation challenge seems straightforward, but in French (and other Romance languages), the words for “his” and “her” don’t agree in gender with the possessor, but with the item possessed. So here’s what Google Translate gave me:
Dans leur maison, tout vient en paires. Il y a sa voiture et sa voiture, ses serviettes et ses serviettes, sa bibliothèque et les siennes.
The program fell into my trap, not realizing, as any human reader would, that I was describing a couple, stressing that for each item he had, she had a similar one. For example, the deep-learning engine used the word “sa” for both “his car” and “her car,” so you can’t tell anything about either car-owner’s gender. Likewise, it used the genderless plural “ses” both for “his towels” and “her towels,” and in the last case of the two libraries, his and hers, it got thrown by the final “s” in “hers” and somehow decided that that “s” represented a plural (“les siennes”). Google Translate’s French sentence missed the whole point.
Next I translated the challenge phrase into French myself, in a way that did preserve the intended meaning. Here’s my French version:
Chez eux, ils ont tout en double. Il y a sa voiture à elle et sa voiture à lui, ses serviettes à elle et ses serviettes à lui, sa bibliothèque à elle et sa bibliothèque à lui.
The phrase “sa voiture à elle” spells out the idea “her car,” and similarly, “sa voiture à lui” can only be heard as meaning “his car.” At this point, I figured it would be trivial for Google Translate to carry my French translation back into English and get the English right on the money, but I was dead wrong. Here’s what it gave me:
At home, they have everything in double. There is his own car and his own car, his own towels and his own towels, his own library and his own library.
What?! Even with the input sentence screaming out the owners’ genders as loudly as possible, the translating machine ignored the screams and made everything masculine. Why did it throw the sentence’s most crucial information away?
We humans know all sorts of things about couples, houses, personal possessions, pride, rivalry, jealousy, privacy, and many other intangibles that lead to such quirks as a married couple having towels embroidered “his” and “hers.” Google Translate isn’t familiar with such situations. Google Translate isn’t familiar with situations, period. It’s familiar solely with strings composed of words composed of letters. It’s all about ultrarapid processing of pieces of text, not about thinking or imagining or remembering or understanding. It doesn’t even know that words stand for things. Let me hasten to say that a computer program certainly could, in principle, know what language is for, and could have ideas and memories and experiences, and could put them to use, but that’s not what Google Translate was designed to do. Such an ambition wasn’t even on its designers’ radar screens.
Well, I chuckled at these poor shows, relieved to see that we aren’t, after all, so close to replacing human translators by automata. But I still felt I should check the engine out more closely. After all, one swallow does not thirst quench.
Indeed, what about this freshly coined phrase “One swallow does not thirst quench” (alluding, of course, to “One swallow does not a summer make”)? I couldn’t resist trying it out; here’s what Google Translate flipped back at me: “Une hirondelle n’aspire pas la soif.” This is a grammatical French sentence, but it’s pretty hard to fathom. First it names a certain bird (“une hirondelle”—a swallow), then it says this bird is not inhaling or not sucking (“n’aspire pas”), and finally reveals that the neither-inhaled-nor-sucked item is thirst (“la soif”). Clearly Google Translate didn’t catch my meaning; it merely came out with a heap of bull. “Il sortait simplement avec un tas de taureau.” “He just went out with a pile of bulls.” “Il vient de sortir avec un tas de taureaux.” Please pardon my French—or rather, Google Translate’s pseudo-French.
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Facebook announced this morning that it had completed its move to neural machine translation — a complicated way of saying that Facebook is now using convolutional neural networks (CNNs) and recurrent neural networks (RNNs) to automatically translate content across Facebook.
Google, Microsoft and Facebook have been making the move to neural machine translation for some time now, rapidly leaving old-school phrase-based statistical machine translation behind. There are a lot of reasons why neural approaches show more promise than phrase-based approaches, but the bottom line is that they produce more accurate translations.
Traditional machine translation is a fairly explicit process. Relying on key phrases, phrase-based systems translate sentences then probabilistically determine a final translation. You can think of this in a similar light as using the Rosetta Stone (identical phrases in multiple languages) to translate text.
In contrast, neural models deal in a higher level of abstraction. The interpretation of a sentence becomes part of a multi-dimensional vector representation, which really just means we’re trying to translate based on some semblance of “context” rather than phrases.
It’s not a perfect process, and researchers are still tinkering with how to deal with long-term dependencies (i.e. retaining understanding and accuracy throughout a long text), but the approach is incredibly promising and has produced great results, thus far, for those implementing it.
Google announced the first stage of its move to neural machine translation in September 2016 and Microsoft made a similar announcement two months later. Facebook has been working on its conversion efforts for about a year and it’s now at full deployment. Facebook AI Research (FAIR) published its own research on the topic back in May and open sourced its CNN models on GitHub.
“Our problem is different than that of most of the standard places, mostly because of the type of language we see at Facebook,” Necip Fazil Ayan, engineering manager in Facebook’s language technologies group, explained to me in an interview. “We see a lot of informal language and slang acronyms. The style of language is very different.”
Facebook has seen about a 10 percent jump in translation quality. You can read more into the improvement in FAIR’s research. The results are particularly striking for languages that lack a lot of data in the form of comparative translation pairs.
Source: TechCrunch, article by John Mannes posted on 3 August 2017 – Read the original at:
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Acclaro is excited to announce the general availability of the My Acclaro translation management platform. This SaaS based platform provides clients instant access to their translation work as well as options to directly connect content to Acclaro’s translation environment via API, cloud and CMS(Content Management System) integrations.
With instant, at-a-glance access to a translation management dashboard, users will be able to create orders and request quotes, get up-to-the-minute translation statuses, pick up files when translations are completed, communicate with their dedicated project team, and track their translation budgets.
“We’ve received very positive feedback over the last several months from users who have been working within a fully functional, pre-launch version of My Acclaro. I am confident that new users will be impressed with My Acclaro’s capabilities including its ease of use and integration to content management tools,” said Michael Kriz, Acclaro’s founder and CEO.
A key feature of My Acclaro is the ability to connect and share content via popular web publishing and cloud storage tools such as Dropbox, Box, Zendesk, Hubspot, WordPress, Drupal, Craft CMS and Adobe Experience Manager eliminating the cost and errors typically associated with manual exports or copy and paste.
“We’ve made sure companies can establish seamless content integrations between their environments and Acclaro’s translation management platform and teams of professional linguists,” Kriz said. ”The transparency, productivity and connectivity available through the My Acclaro translation management platform results in faster turnaround times and lower costs with the same high quality translation services – all benefits that are increasingly vital in a competitive global economy.”
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Trailers for season 7 of Game of Thrones have supporters of the various in-universe character factions on tenterhooks. Meanwhile, more dedicated and geeky fans of Game of Thrones might be able to appreciate a new option being offered by language translating app Duolingo: learning Valyrian.
Unlike English, High Valyrian uses an aorist tense, similar to Ancient Greek and Sanskrit. David J. Peterson, the linguist who created the Dothraki and Valyrian languages for the TV series, worked on the Duolingo course, so you can be assured any dragon-training commands you learn will be effective.
Peterson created the language mostly from scratch, constructing the grammar around the two key phrases used in George R.R. Martin’s A Song of Ice and Fire books: “Valar Morghulis” (“All men must die”) and “Valar Dohaeris” (“All men must serve”).
The language, which has been in Duolingo’s “Incubator” for the last several months, has now been released in beta.
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Today’s post is about the improvements in the field of terminology support for interpreters through computer-assisted interpreting (CAI) tools. InterpretBank is an example of such tools, it was developed as part of a PhD project and it uses IATE as one of its terminology sources. Our guest writer Claudio Fantinuoli (Johannes Gutenberg University Mainz in Germersheim) tells us all about it.
InterpretBank is a computer-assisted interpreting (CAI) tool originally developed at the Johannes Gutenberg Universität Mainz in Germersheim as part of a PhD research project. The objective of this project was to create a computer program to support professional interpreters during all phases of the interpreting workflow, from preparation to the act of interpreting. With the aim of improving interpreting quality especially in the context of specialised events, InterpretBank focuses on the creation and management of specialised glossaries as well as on facilitating terminology memorization and retrieval during interpretation.
InterpretBank implements the results of several years of research and the feedbacks of a growing number of users. The tool integrates automatic translation and high-quality terminology databases, such as IATE, to reduce the effort and the time involved in writing glossaries. During preparation, a memorization utility helps interpreters learning the event-related terms. While interpreting, intelligent algorithms allow the user to access relevant terminology quickly and without distracting the interpreter from his or her primary activity – translating between languages. Several independent studies have confirmed that the tool can contribute to increasing the overall interpreting quality. We have now taken a further step forward integrating Speech Recognition.
The interest for the emerging field of CAI tools is growing: InterpretBank is taught in a large number of universities and in dedicated seminars held by professional associations around the world. InterpretBank is the tool of choice not only of many professionals but also when it comes to empirical research in the field of translation technology. In Germersheim, for example, an ongoing PhD project is investigating cognitive load in simultaneous interpreting with the support of terminology management tools.
More information about the tool at www.interpretbank.com
Looking for a good book to read this summer? Something both insightful and entertaining?
We have an exclusive discount for readers of Proz.com news on The Ultimate Guide to Becoming a Successful Freelance Translator! Act now to get it at the price of a one-day sunbed rental – the deal is valid till the end of July. Just go to http://translatorsbook.com and apply the code “SummerDiscount” during checkout to get a 50% discount.
Topics within the book include:
• Skills and qualifications
• Finding and winning new clients
• Marketing tips for freelance translators
• How to handle some of the trickiest translation problems
There’s also a wealth of information beyond these subjects, including a comprehensive list of resources for translators.
If you’re interested in learning more, visit www.translatorsbook.com or our Amazon product page to see what The Ultimate Guide To Becoming A Successful Freelance Translator can do for you.
Once you’ve read the book, please do let us know your feedback.
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CSOFT (#22 on our global list of the 100 largest LSPs) has banked on mobile being a driving force behind language needs. In December 2015, the company released Stepes (pronounced /’steps/), a human-powered mobile translation app designed to mobilize professional translators and Uberize the world’s bilingual population in the process. Last year, the company broadened the offering to support on-demand social media and image translation, again harnessing the power of the crowd. However, 2017 will be the year of interpreting for the company. EVP Carl Yao briefed us on CSOFT’s latest offering: on-demand interpreting from mobile devices.
This new capability is significant for several reasons:
- Stepes combines multiple desirable attributes into one package. Yao said that the service lets you access interpreters on demand, but still have the ability to schedule calls. It taps into local interpreters who are knowledgeable about the area in which customers need service. The platform is designed for both consecutive and simultaneous interpreting, enabling simple one-to-one conversations where the customer often puts the interpreter on speakerphone.
- The service leverages the power of the crowd. The company relies on a pool of 100,000 professional linguists today, but CSOFT plans to tap into the much larger population of bilingual people. Many of them essentially provide language services for extra revenue in their spare time. Linguists can indicate when they are online and able to accept jobs. The Uber-style app shows you a map with the location of nearby interpreters on standby. Upon completion of each interpreting session, customers have the opportunity to rate the performance of each interpreter.
- The service will evolve the role of Interpreters into that of a multilingual concierge. You can ask a bilingual crowd member for a restaurant recommendation or tips on how to use the local public transport system. Interpreters step out of the role of linguistic mediator between two parties exchanging information to become an information source themselves.
- CSOFT is going after travelers frustrated with MT results. It sees tremendous potential when looking at the numbers of downloads of apps such as iTranslate and Google Translate. The company wants to provide a more personable service with a local helper, yet at a modest cost because its fees range from US$0.60 to 0.75 per minute.
Of course, this disruptive offering brings up a lot of questions. What about the ethical boundary for interpreters not to add to or change the message being delivered by another? How do you ensure the privacy of interpreters? How can the system’s ratings distinguish between linguists’ language skills and their knowledge of gluten-free restaurants in the area?
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Australian start-up, Lingmo International, has brought us one step closer to the science-fiction dream of a universal translator earpiece. The Translate One2One, powered by IBM Watson artificial intelligence technology, is set to be the first commercially available translation earpiece that doesn’t rely on Bluetooth or Wi-Fi connectivity.
Translation technology has been rapidly progressing over the past few years. Both Google and Skype have been developing, and constantly improving, both text-to-text and speech-to-speech systems, and the current Google Translate app offers fantastic translation functionality through your smartphone, but we haven’t seen that transferred into something like an earpiece until very recently.
Last year, Waverly Labs launched its Pilot earpiece, which communicates with an app via Bluetooth to offer near real-time speech-to-speech translation. Waverly Labs made US$5 million from its initial Indiegogo campaign, and is set to ship the first round of pre-orders later this year. The handheld ili translator also promises Wi-Fi-free language translation when it launches in October.
With the imminent launch of the Translate One2One, Lingmo is poised to jump to the head of the class with a system that incorporates proprietary translation algorithms and IBM’s Watson Natural Language Understanding and Language Translator APIs to deal with difficult aspects of language, such as local slang and dialects, without the need for Bluetooth or Wi-Fi connectivity.
“As the first device on the market for language translation using AI that does not rely on connectivity to operate, it offers significant potential for its unique application across airlines, foreign government relations and even not-for-profits working in remote areas,” says Danny May, Lingmo’s Founder and Director.
The system currently supports eight languages: Mandarin Chinese, Japanese, French, Italian, German, Brazilian Portuguese, English and Spanish. The in-built microphone picks up spoken phrases, which are translated to a second language within three to five seconds. An app version for iOS is also available that includes speech-to-text and text-to-speech capabilities for a greater number of languages.
The Translate One2One earpiece is available now to preorder for $179 with delivery expected in July. A two-piece travel pack is also available for $229, meaning two people, each with their own earpiece, could hold a real-time conversation in different languages.
Just a few years ago the idea of a universal translator device that slipped into your ear and translated speech into your desired language in real-time seemed like science fiction, but between Lingmo, Waverly Labs, Google and a host of other clever start-ups, that fantastic fiction looks to be very close to becoming a reality.
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With ever-increasing demand for local content, the pressure is growing on translation teams to do more with their available resources.
AdaptiveMT, introduced in SDL Trados Studio 2017, is a game-changer for machine translation (MT) technology. By learning from post-edits it provides translators with a self-learning, personalized MT service that improves the quality of suggestions and boosts productivity.
Learn about how AdaptiveMT is transforming the role of MT in this white paper.
Download this white paper here >>
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More than a year has passed since our first edition of startups to watch. So it was time to check in again with language industry founders and see what new business models are emerging. The companies we cover in this edition have all been founded after 2013 and are starting to get traction.
Interprefy: BYOD Remote Simultaneous Interpreting
The Pitch: Get rid of those clunky translation headsets and listen to a live interpreter at conferences using your smartphone, tablet or laptop. Variations in streaming speed and audio quality considered, it should be easy, more convenient, fast, via an app or a web browser.
Cadence Translate: Real-time Interpretation for Business Meetings
The Pitch: Hire an interpreter from anywhere in the world for your business meetings, conference calls, and livestreams. As you talk, remote interpreters are translating your message on the fly in another language or multiple languages. A proprietary matchmaking platform called “SmartMatch” can connect buyers with the right interpreter.
VoiceBoxer: Voice Interpretation for Multilingual Webinars
The Pitch: Live voice interpretation for international webinars, virtual meetings, and web presentations is made easy with VoiceBoxer’s multilingual web presentation and communication platform. Established in 2013, the startup is run by a team of five in its headquarters in Copenhagen.
MiniTPMS: Management System for Small LSPs
The Pitch: If you still use spreadsheets and post-its to track your translation business projects, then you’re living in the wrong century, says MiniTPMS Founder and CEO Nenad Andricsek. These tools may get a job or two done, but in terms of technology, it’s prehistoric, he continues. Andricsek’s startup offers a tool which helps organize the business of very small, boutique LSPs.
Translation Exchange: Website and App Localization
The Pitch: More and more companies are discovering the value of localization, but the traditional process of localizing websites and mobile apps is outdated, cumbersome, and error- prone.
“Translation Exchange automates the entire localization process,” says Co-founder and CEO Michael Berkovich. “My co-founder Ian McDaniel and I led the localization efforts at a company called Yammer and that was the genesis of Translation Exchange.”
For several years, the field of quality checking tools has been largely stagnant, with incremental updates to established tools. Recently, TAUS’ Dynamic Quality Framework (DQF) and the EU’s Multidimensional Quality Metrics (MQM) have set the stage for new developments in quality assessment methods thanks to their new methods and push for standardization. In this blog, we’ll review three new market entrants that are hoping to shake up this area. But let’s start with an overview of the types of tools out there:
- Automatic quality checkers. These tools use pattern recognition and other language technology approaches to identify potential problems, such as broken or missing links, inconsistent terminology, and missing content. These applications help linguists identify and fix problems during production to ensure quality.
- Quality assessment scorecards. Many LSPs use spreadsheet-based tools or simple software applications to count errors in translations to generate quality scores. They use the figures these produce to decide whether target text meet thresholds for acceptance. The classic example of such a system is the now-defunct LISA QA Model, but most CAT tools have some basic functions in this area.
Both of these approaches serve their purpose and help both LSPs and their clients, but three companies are bringing energy to an area that has been something of a language technology backwater. In CSA Research’s briefings with the developers of these tools, we saw encouraging signs that quality assessment is taking off again.
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