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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.
See full review >>
San Francisco, California: Boostlingo, a next-generation interpreting delivery platform, has partnered with ProZ.com to bring the world’s largest community of freelance interpreters to users of its platform.
Boostlingo has made interpretation widely accessible, offering language service providers (LSPs) on-demand access to Video Remote Interpreting (VRI) and Over the Phone Interpreting (OPI), advanced scheduling and administration, 24-7 customer support and usability across all devices. Leveraging pre-screened freelance interpreters from ProZ.com, Boostlingo will provide LSPs direct, efficient access to the ProZ.com database and its worldwide interpreter network.
“ProZ.com has built an amazing reputation and brand in the language industry. Combining the power of Boostlingo technology with the marketplace benefits of the ProZ.com ecosystem will create an unmatched interpretation network,” Boostlingo CEO Bryan Forrester said of the partnership. “We are excited to join forces with ProZ.com.”
ProZ.com has provided tools and opportunities to members in the language industry since 1999. Boostlingo will integrate with ProZ.com’s pre-screened freelance interpreter pool, syncing with ProZ.com interpreters to efficiently fulfil immediate VRI, OPI and in-person interpreting opportunities.
“We’re excited to help interpreters expand their businesses through this partnership with Boostlingo,” said ProZ.com President Henry Dotterer. “Boostlingo has built an impressive platform for businesses that offer interpreting services, and we’re glad to help connect interpreters to the resulting business opportunities.”
To generate greater use of the ProZ.com interpreter network and the Boostlingo platform, both parties have agreed to cross-promote, maintain high industry standards, and work closely together to improve the speed and ease at which a third party can access a qualified interpreter. More information can be found at www.ProZ.com and www.boostlingo.com.
ABOUT BOOSTLINGO: Boostlingo, LLC is a language software and technology company based in San Francisco, California. Boostlingo is focused on defining the next generation of interpretation technology solutions. The software is device agnostic, infinitely scalable and compliant across all common regulatory and security requirements. By providing access to On-Demand VRI, OPI and On-Site scheduling services, Boostlingo intends to advance global visibility and support the interpreter community.
ABOUT PROZ.COM: Founded in 1999, ProZ.com is home to the world’s largest community of freelance translators. Companies that require translations can use the site’s directory to find translators or translation companies at no charge. In addition, translators working on jobs have a structured means (called “KudoZ”) of obtaining assistance from colleagues on challenging terms. Many other services are provided for translators, including discussion forums, in-person and virtual meetings, the “Blue Board” database of translation outsourcers with reviews, and more. The ProZ.com interpreting pool launched on June 1, 2017.
Qualified interpreters may apply at http://www.proz.com/pools/interpreters.
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They look a bit more stylish than your average babel fish, but it remains to be seen if they work as well. From the article:
The earbuds run on a new version of Bragi’s operating system, which will come to the original Dash as well. It enables the simpler pairing process, helps the buds auto-detect a workout, and refines the on-bud touch controls, which until now were about as easy to learn as Morse Code. The new OS also introduces two of the more futuristic features Bragi’s been talking about for years: real-time translation, through a partnership with the iTranslate app, and a gesture interface that lets you control your music just by moving your head.
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Kató is the improved and expanded translation platform, formerly known as the Translators without Borders (TWB) Workspace, and it is where much of the magic happens. Kató connects over 500 non-profit partners with a diverse community of volunteer translators and many other language services. First established as the TWB Workspace in 2011 following a collaboration between TWB and ProZ.com, the online platform has since helped non-profit partners such as Doctors without Borders, Refugee Aid and Save the Children to share essential information in local languages and to translate over 40 million words. Today, the revamped Kató is more robust than ever with computer-assisted translation tools, functionality for storing common words and taxonomies and even bigger incentives for the community. Translators can now use Kató to interpret for all media, including providing subtitles and voiceovers for videos. The platform is even being used to train fluent speakers of languages that desperately need more translators and interpreters.
KATO – BRIDGING THE LANGUAGE GAP
40+ million words translated so far
4,000 professional translators
500+ non-profit partners
190 language pairs
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Translation Management Software (TMS) provider, smartCAT and Lilt, an interactive and adaptive Machine Translation (MT) tool, have partnered to combine the latest in MT technology with a robust collaborative translation environment.
Lilt’s adaptive MT is now available within the smartCAT editor, giving smartCAT’s customers access to this technology in a single activation click.
“Lilt fit just right into the smartCAT ecosystem. We loved the futuristic approach to machine translation it promotes, so delivering the technology to our users instantly became our priority. What makes this integration so special is that it takes smartCAT’s unique real-time multi-user collaboration to the next level. Each time a translator confirms a segment, the engine instantly trains and provides the correct suggestions to all the participants in the project, helping them to maintain consistency across the text. Despite the technology behind the new feature being complex and unfamiliar, we made sure it’s intuitive and easy to use.” said Ivan Smolnikov, CEO at smartCAT.
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With its Creators Update for Windows 10, Microsoft promised that users would have the option to postpone future updates for a limited period of time and many rejoiced. But now that the update has started rolling out, it’s become apparent that there are still some stability issues and performing a manual installation isn’t recommended right now.
In a blog post, Microsoft’s director of program management explained that the latest update has been rolling out slowly because there are known issues that could be a problem for anyone who isn’t an advanced user. The post doesn’t go in depth on what those issues are but it appears that all the bugs haven’t been ironed out for certain devices. For instance, PCs that use a certain type of Broadcom radio were having connectivity problems with Bluetooth devices.
If you aren’t the type to manually install updates, this probably isn’t your problem. Windows 10 has automatically pushed updates to users since it debuted. The Creators Update has a lot of cool little features, but the most useful one is that it offers a simple way to pause installing updates for up to seven days. Updates are good for security but Windows has had an insidious way of suddenly deciding it’s time to install that latest patch and restart right when you’re in the middle of something important.
Microsoft is still automatically updating users this time around and if you encounter problems, you can find instructions for rolling back the update here. If you’re the cavalier type who doesn’t care about warnings and just wants to start making 3D dogs in MS Paint, you can manually download the update here.
A new voice-transcription software, named Trint, can listen to an audio recording or a video of two or more speakers engaged in a natural conversation, then provide a written transcript of what each person said.
Trint’s technology is still nascent, but it could eventually give new life to vast swaths of non-text-based media on the internet, like videos and podcasts, by making them readable to both humans and search engines. People could read podcasts they lack the time or ability to listen to. YouTube videos could be indexed with a time-coded transcript, then searched for text terms. There are other applications too: Filmmakers could index their footage for better organization, and journalists, researchers, and lawyers could save the many hours it takes to transcribe long interviews.
As machine learning and automation technologies continue to transform the 21st century, voice recognition remains a pesky speed bump. Transcription in particular is a technology that some have spent decades pursuing and others deemed outright impossible in our lifetimes. While news organizations and social media outlets alike have invested heavily in video content, the ability to optimize those clips for search engines remains elusive. And with younger readers still preferring print to video anyway, the value of transcribed text remains high.
Based in London and launched in autumn 2016, Trint is a web app built on two separate but entwined elements. The company’s transcription algorithm feeds text into a browser interface for editing, which links the words in a transcript directly to the corresponding points in the recording. While the accuracy is hardly perfect (as Trint’s founders are the first to admit), the system almost always produces a transcript that’s clean enough for searching and editing. At roughly 25 cents per minute (or $15 per hour), Trint’s software-as-service costs a quarter of the $1 per minute rate offered by competitors. There’s a reason Trint is so cheap: Those other services, like Casting Words and 3Play, use humans to clean up automated transcripts or to do the actual transcribing. Trint is all machines.
Microsoft has released voice recognition toolkits for programmers to experiment with, and Google just last week added multi-voice recognition to its Google Home smart speaker. But Trint’s software was the first public-facing commercial product to serve this space.
According to lead engineer Simon Turvey, Trint users report an error rate of between five and 10 percent for cleanly recorded audio. Though this is close to the eight percent industry standard estimated last year by veteran Microsoft scientist Xuedong Huang, the Trint founders consider their product’s editing function the thing that gives them a stronger competitive edge. Trint’s time-coded transcript and the web-based editor allows users to quickly find and work on the quotes they need.
Trint can currently understand 13 languages, including several varieties of English accents. Since it’s a cloud-based application, Trint’s voice transcription algorithm can be updated frequently to add new languages, new accents (Cuban-accented English is tough), and fresh batches of proper nouns.
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Last year, Google Translate introduced neural machine translation, which uses deep neural networks to translate entire sentences, rather than just phrases, to figure out the most relevant translation. Since then we’ve been gradually making these improvements available for Chrome’s built-in translation for select language pairs. The result is higher-quality, full-page translations that are more accurate and easier to read.
Today, neural machine translation improvement is coming to Translate in Chrome for nine more language pairs. Neural machine translation will be used for most pages to and from English for Indonesian and eight Indian languages: Bengali, Gujarati, Kannada, Malayalam, Marathi, Punjabi, Tamil and Telugu. This means higher quality translations on pages containing everything from song lyrics to news articles to cricket discussions.
From left: A webpage in Indonesian; the page translated into English without neural machine translation; the page translated into English with neural machine translation. As you can see, the translations after neural machine translation are more fluid and natural.
The addition of these nine languages brings the total number of languages enabled with neural machine translations in Chrome to more than 20. You can already translate to and from English for Chinese, French, German, Hebrew, Hindi, Japanese, Korean, Portuguese, Thai, Turkish, Vietnamese, and one-way from Spanish to English.
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Language I/O has released LinguistNow Chat, enabling companies to provide real-time, multilingual chat support inside several major platforms, including Salesforce.com and Oracle Service Cloud.
The LinguistNow product suite works within the Oracle and Salesforce customer relationship management (CRM) systems. It enables companies to provide customer support in any language over any support channel. Using a hybrid of machine and human translation services, LinguistNow let’s [sic] monolingual agents provide support in any language simply by clicking a button.
“With LinguistNow, companies can receive outstanding translations for self-help articles, ticket/email responses, and chat,” said Kaarina Kvaavik, co-founder of Language I/O, in a statement. “Our customers are already seeing tremendous cost reductions by using our existing products. Some of them have seen a more than 40 percent reduction on customer support costs.
“We use a unique combination of human and machine translation, which is why our translations are both fast and accurate,” Kvaavik continued. “We help companies improve their quality of customer support while also reducing their costs. First, we allow customers to answer their own questions by providing outstanding article translations. Second, we allow agents to accurately and quickly respond to emails and chat in the customer’s native language.”
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