Category: Translation Business

memoQ 8.6 is Here!

memoQ 8.6 is Here!

memoQ 8.6 is our third release in 2018, and we are very excited about the new functionality it brings. The highlight of 8.6 is definitely the aim to pave the way to a more CMS-friendly translation environment, but like previous versions, it includes enhancements in many areas, including integration, terminology, productivity features, file filters, and user experience. Learn more about the most recent version and see how it will help you be even more productive.

 

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More than 1 trillion words a day

More than 1 trillion words a day

The world is reaching a new milestone in MT – more than 1 trillion words a day. Machines translate in a single day more than all professional translators on the planet combined can do in a year.

 

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Lessons in Building a Language Industry Startup

Lessons in Building a Language Industry Startup

Bryan Forrester, CEO of Boostlingo, Matt Conger, CEO of Cadence Translate, and Jeffrey Sandford, Co-Founder and CTO of Wovn Technologies joined Smart on stage to share their experiences and insights with the 120 senior executives in attendance.

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Translators and Technology: Friends or Foes?

Translators and Technology: Friends or Foes?

It is a fact that different kinds of technology creep into the translation industry on all levels. As a result, some participants in this magical process of transforming a text to fit a different language, cultural, and sociological community, can feel quite uneasy, or even anxious. Will machine translation (MT) reach parity with human translation (HT)? Will there be a need for translators?

 

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How Augmented Translation Will Redefine the Value of Translators

How Augmented Translation Will Redefine the Value of Translators

Norbert Oroszi, CEO of translation software company memoQ, joined the speaker line up at a sold-out SlatorCon San Francisco 2018 to reflect on the role of humans and machines in shaping the future of translation technology.

To lay the groundwork, Oroszi began by drawing a comparison between the role of technology in the automotive industry and localization. More than 100 years ago when technology hit the car industry to enable mass production of vehicles, many were fearful that machines would replace humans, but technology did not take jobs away from workers in the car industry. Instead, automation augmented human capabilities, redefined the value of workers, and facilitated what became an automotive revolution.

Read full article from here.

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SYSTRAN presents its latest translation engines: huge quality & speed improvement!

SYSTRAN presents its latest translation engines: huge quality & speed improvement!

The latest version of our AI-powered Translation Software designed for Businesses

SYSTRAN Pure Neural® Server is our new generation of enterprise translation software based on Artificial Intelligence and Neural networks. It provides outstanding professional quality with the highest standards in data safety.

Our R&D team, extremely active to provide corporate users with state-of-the-art translation technology tailored for business, just released a new generation of Neural MT engines. SYSTRAN new engines are developed with OpenNMT-tf, our AI framework using latest TensorFlow features, and backed by a proprietary new training process: Infinite Training.

These innovations bring two major impacts on businesses:

  • Better Translation Quality & fluidity: the new engines exploit SAT (Self Attentional Transformers) neural networks that improve a contextual translation for better quality & fluency.
  • Better Performances: translation speed (char./sec.) is improved by 10 to 30 times on CPU hardware compared to previous generation engines.

For more info, please visit: https://bit.ly/2QgAMMq

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The Augmented Translator

The Augmented Translator

The idea that robots are taking over human jobs is by no means a new one. Over the last century, the automation of tasks has done everything from making a farmer’s job easier with tractors to replacing the need for cashiers with self-serve kiosks. More recently, as machines are getting smarter, discussion has shifted to the topic of robots taking over more skilled positions, namely that of a translator.

A simple search on the question-and-answer site Quora reveals dozens of inquiries on this very issue. While a recent survey shows that AI experts predict that robots will take over the task of translating languages by 2024. Everyone wants to know if they’ll be replaced by a machine and more importantly, when will that happen?

“I’m not worried about it happening in my lifetime” translator, Lizajoy Morales, told me when I asked if she was afraid of losing her job to a machine. This same sentiment echoes with most of Lilt’s users. Of course, this demographic is already using artificial intelligence to their advantage and tend to see the benefits over than the drawbacks.

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Many translators, however, are quick to argue that certain types of content are impossible to be translated accurately by a machine, such as literature, which relies on a human’s understanding of nuance to capture the author’s intention. Or in fields like legal or medicine, that rely on the accuracy of a human translator.

But even in these highly-specialized fields, machines can find their place in the translation workflow. Not as a replacement, but rather as an assistant. As translators, we can use machines to our advantage, to work better and faster.

But I’m not talking about post-editing of machine translation. In a recent article from a colleague, Greg Rosner talks of the comparison of post-editing to the job of a janitor — just cleaning up a mess. True machine assistance augments the translator’s existing abilities and knowledge, letting them have the freedom to do what they do best — translate — and keeping interference to a minimum.

So how do machines help translators exactly? With an interactive, adaptive machine translation, such as that found in Lilt, the system learns in real-time from human feedback and/or existing translation memory data. This means that as a translator is working, the machine is getting to know their content, style and preferences and thus adapting to this unique translator/content combination. This adaptation allows the system to progressively provide better suggestions to human translators, and higher quality for fully automatic translation. In basic terms, it’s making translators faster and better.

Morales also pointed out another little-known benefit from machine translation suggestions: an increase in creativity. “This is an unexpected and much-appreciated benefit. I do all kinds of translations, from tourism, wine, gastronomy, history, social sciences, financial, legal, technical, marketing, gray literature, even poetry on occasion. And Lilt gives me fantastic and creative suggestions. They don’t always work, of course, but every so often the suggestion is absolutely better than anything I could have come up with on my own without spending precious minutes searching through the thesaurus…once again, saving me time and effort.”

Many are also finding that with increased productivity, comes increased free time. Ever wish there were more hours in the day? If you’re a translator, machine assistance may be the solution.

David Creuze, a freelance translator, told us how he spends his extra time, “I have two young children, and to be able to compress my work time from 6 or 7 hours (a normal day before their birth) to 4 hours a day, without sacrificing quality, is awesome.”

With these types of benefits at our fingertips, we should stop worrying about machines taking the jobs of translators and focus on using the machine to our advantage, to work better and ultimately focus on what we do best: being human.

 

Reference: https://bit.ly/2MgDaAj

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Is This The Beginning of UNMT?

Is This The Beginning of UNMT?

Research at Facebook just made it easier to translate between languages without many translation examples. For example, from Urdu to English.

Neural Machine Translation

Neural Machine Translation (NMT) is the field concerned with using AI to translate between any language such as English and French. In 2015 researchers at the Montreal Institute of Learning Algorithms, developed new AI techniques [1] which allowed machine-generated translations to finally work. Almost overnight, systems like Google Translate became orders of magnitude better.

While that leap was significant, it still required having sentence pairs in both languages, for example, “I like to eat” (English) and “me gusta comer” (Spanish).  For translations between languages like Urdu and English without many of these pairs, translation systems failed miserably. Since then, researchers have been building systems that can translate without sentence pairings, ie: Unsupervised Neural Machine Translation (UNMT).

In the past year, researchers at Facebook, NYU, University of the Basque Country and Sorbonne Universites, made dramatic advancements which are finally enabling systems to translate without knowing that “house” means “casa” in Spanish.

Just a few days ago, Facebook AI Research (FAIR), published a paper [2] showing a dramatic improvement which allowed translations from languages like Urdu to English. “To give some idea of the level of advancement, an improvement of 1 BLEU point (a common metric for judging the accuracy of MT) is considered a remarkable achievement in this field; our methods showed an improvement of more than 10 BLEU points.”

Check out more info at Forbes.

Let us know what do you think about this new leap!

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Here’s Why Neural Machine Translation is a Huge Leap Forward

Here’s Why Neural Machine Translation is a Huge Leap Forward

Though machine translation has been around for decades, the most you’ll read about it is the perceived proximity to the mythical “Babel Fish” –an instantaneous personal translation device– itself ready to replace each and every human translator. The part that gets left out is machine translation’s relationship with human translators. For a long time, this relationship was no more complex than post-editing badly translated text, a process most translators find to be a tiresome chore. With the advent of neural machine translation, however, machine translation is not just something that creates more tedious work for translators. It is now a partner to them, making them faster and their output more accurate.

So What’s the Big Deal?

Before we jump into the brave new translating world of tomorrow, let’s put the technology in context. Prior to neural machine translation, there have been two main paradigms in the history of the field. The first was rules-based machine translation (RBMT) and the second, dominant until very recently, was phrase-based statistical machine translation (SMT).

When building rules-based machine translation systems, linguists and computer scientists joined forces to write thousands of rules for translating text from one language to another. This was good enough for monolingual reviewers to be able to get the general idea of important documents in an otherwise unmanageable body of content in a language they couldn’t read. But for the purposes of actually creating good translations, this approach has obvious flaws: it’s time consuming and, naturally, results in low quality translations.

Phrase-based SMT, on the other hand, looks at a large body of bilingual text and creates a statistical model of probable translations. The trouble with SMT is its reliance on systems. For instance, it is unable to associate synonyms or derivatives of a single word, requiring the use of a supplemental system responsible for morphology. It also requires a language model to ensure fluency, but this is limited to a given word’s immediate surroundings. SMT is therefore prone to grammatical errors, and relatively inflexible when it encounters phrases that are different from those included in its training data.

Finally, here we are at the advent of neural machine translation. Virtually all NMT systems use what is known as “attentional encoder-decoder” architecture. The system has two main neural networks, one that receives a sentence (the encoder) and transforms it into a series of coordinates, or “vectors”. A decoder neural network then gets to work transforming those vectors back into text in another language, with an attention mechanism sitting in between, helping the decoder network focus on the important parts of the encoder output.

The effect of this encoding is that an NMT system learns the similarity between words and phrases, grouping them together in space, whereas an SMT system just sees a bunch of unrelated words that are more or less likely to be present in a translation.

Interestingly, this architecture is what makes Google’s “zero-shot translation” possible. A well-trained multilingual NMT can decode the same encoded vector into different languages it knows, regardless of whether that particular source/target language combination was used in training.

As the decoder makes its way through the translation, it predicts words based on the entire sentence up to that point, which means it produces entire coherent sentences, unlike SMT. Unfortunately, this also means that any flaws appearing early in the sentence tend to snowball, dragging down the quality of the result. Some NMT models also struggle with words it doesn’t know, which tend to be rare words or proper nouns.

Despite its flaws, NMT represents a huge improvement in MT quality, and the flaws it does have happen to present opportunities.

Translators and Machine Translation: Together at Last

While improvements to MT typically mean increases in its usual applications (i.e. post-editing, automatic translation), the real winner with NMT is translators. This is particularly true when a translator is able to use it in real time as they translate, as opposed to post-editing MT output. When the translator actively works with an NMT engine to create a translation, they are able to build and learn from each other, the engine offering up a translation the human may not have considered, and the human serving as a moderator, and in so doing, a teacher of the engine.

For example, during the translation process, when the translator corrects the beginning of a sentence, it improves the system’s chances getting the rest of the translation right. Often all it takes is a nudge at the beginning of a sentence to fix the rest, and the snowball of mistakes unravels.

Meanwhile, NMT’s characteristic improvements in grammar and coherence mean that when it reaches a correct translation, the translator spends less time fixing grammar, beating MT output and skipping post-editing all together. When they have the opportunity to work together, translators and their NMT engines quite literally finish each other’s sentences. Besides speeding up the process, and here I’m speaking as a translator, it’s honestly a rewarding experience.

Where Do We Go Now?

Predicting the future is always a risky business, but provided the quality and accessibility of NMT continues to improve, it will gradually come to be an indispensable part of a translator’s toolbox, just as CAT tools and translation memory already have.

A lot of current research has to do with getting better data, and with building systems that need less data. Both of these areas will continue to improve MT quality and accelerate its usefulness to translators. Hopefully this usefulness will also reach more languages, especially ones with less data available for training. Once that happens, translators in those languages could get through more and more text, gradually improving the availability of quality text both for the public and for further MT training, in turn allowing those translators, having already built the groundwork, to move on to bigger challenges.

When done right, NMT has the potential to not just improve translators’ jobs, but to move the entire translation industry closer to its goal of being humanity’s Babel Fish. Not found in an app, or in an earbud, but in networks of people.

 

Reference: https://bit.ly/2CewZNs

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TRANSLATION TECH GOES MEDIA

TRANSLATION TECH GOES MEDIA

Four out of the five fastest-growing language services companies in 2018 are media localization specialists. The media business has seen a boom over the last two years, and traditional translation companies are taking notice. Media localization won’t stay an uncontested insular niche for long. In fact, conventional LSPs and technology providers are moving into this sector and expanding their technical capabilities this year.

HERE ARE A FEW EXAMPLES, WITH MORE TO FOLLOW…

Omniscien launched an automated subtitling tool

Omniscien, previously Asia Online, is best known for its trainable machine translation software, but now they are going into a new area – video subtitling. Omniscien has just started selling Media Studio, which was built based on product requirements from iflix, a Malaysian competitor to Netflix.

Under the hood Media Studio has machine learning components: audio transcription, dialog extraction, and neural MT engines pre-trained for subtitles in more than 40 language combinations. The technology is able to create a subtitle draft out of a raw video already in the target language. It can even adjust timings and split long sentences into multiple subtitles where necessary. And it’s learning all the time.

For the human part of the work, Media Studio includes a web-based subtitle editor and a management system, both including a significant range of features right from the start. Translators can edit time codes in a drag-and-drop fashion, skip parts of the video without speech, customize keyboard shortcuts, and more. Project managers can assign jobs and automatically send job notifications, track productivity, and MT leverage.

The video is hosted remotely and is streamed to linguists instead of sending complete films and episodes. This adds a security layer for the intellectual property. No one in the biz wants the next episode of the Game of Thrones to end up on thepiratebay.org faster than it would on a streaming service. Linguists in low-bandwidth countries can download videos in low quality and with a watermark.

On the downside, this new tool does not integrate with existing CAT and business management systems for LSPs out of the box, doesn’t have translation memory support or anything else that would make it fit as one of the blades in the Swiss army knife of LSP technology.

According to Omniscien’s CEO Dion Wiggins, iflix has processed hundreds of thousands of video hours through the system since its inception in late 2016. By now, three more large OTT providers have started with Media Studio. Content distribution companies are the main target for the tool, but it will be available for LSPs as well once the pricing is finalized.

GlobalLink deployed subtitle and home dubbing software

At a user conference in Amsterdam this June, TransPerfect unveiled a new media localization platform called Media.Next. The platform has three components:

The subtitle editor is a CAT-tool with an embedded video player. Translators using this platform can watch videos and transcribe them with timings, possibly with integrated speech recognition to automatically create the first pass. As they translate using translation memory and termbase, they are able to see the subtitles appear on the screen.

The home dubbing is all about the setup on the voice-actor side. TransPerfect sends them mics and soundproofing so that recording can happen at home rather than at a local audio studio.

A media asset management platform stores videos at a single location and proxies them to the translator applications instead of sending complete files over the Internet, similar to Omniscien’s approach.

The official launch of TransPerfect’s Media.NEXT is scheduled for mid-August.

Proprietary tech launched earlier this year

TransPerfect’s tech is proprietary, meant to create a competitive advantage. Media localization companies such as Zoo Digital and Lylo took a similar approach. They have launched cloud subtitling and dubbing platforms, but continue to keep technology under the radar of other LSPs.

The idea of “dubbing in the cloud” is that it gives the client visibility into the actual stage of the process, and flexibility with early-stage review and collaboration with the vendor. The same idea permeates Deluxe Media’s platform Deluxe One unveiled in April this year. It’s a customer portal that provides clients with access to multiple services and APIs.

Deluxe One user interface

MemoQ and Wordbee add view video preview for subtitling

At the same time, subtitling capabilities are beginning to make their way into tools that are available to hundreds of LSPs around the world.

Popular translation editor memoQ has added a video player with a preview in their July release. The editor now opens the video file at the point that is being translated and displays the translated text so that translators can check it live. It can also show the number of words per minute, characters per second, or characters per line.

A similar preview appears in Wordbee. The embedded video player can open videos from an URL, or play clips that are uploaded to the editor directly. The initial release includes a character limitation feature to keep subtitles concise, and anchoring: clicking on the segment with the text rewinds the video to that text.

This is a step showing memoQ’s and Wordbee’s move deeper into media, and differentiating them from other TMS.

So far, few TMS had video previews, one of them was Smartcat. Subtitling functionality in Smartcat has been developed in 2013 for a special project, crowdsourced localization of e-learning platform Courserra. Today, users need to enable subtitling functionality on request. The feature set available includes a video player, timecode parsing, and anchoring. Subtitling user numbers in Smartcat are rising, according to product manager Pavel Doronin.

Back to memoQ and Wordbee, their development teams probably will need to expand the list subtitling features over time: first of all, timecode editing. Moreover, memoQ and Wordbee support .SRT extension, whereas Omniscien’s tool supports TTML as well: a more advanced format that allows manipulating subtitle colors, position on screen and formatting. TTML might become more important for video on demand work and streaming platforms, for instance, it is the format that Netflix uses.

Future “luxury” features could include character tracking with descriptions explaining their voice and preferred vocabulary, support for the speech-to-text technology, audio recording, etc.

Subtitling commoditization looms

Subtitling is not new to the translation industry, and almost every mature CAT/TMS supports .srt SubRip text files. However, linguists have to run a third-party video player in a separate window to see their work. They also have to reload and rewind every time to see changes in the subtitles.

That’s why in professional scenarios, subtitlers often use Amara, Subtitle Workshop, Oona captions manager, CaptionHub or similar specialized tools. These tools came from outside the language industry and didn’t support translation memories, term bases, and embedded MT.

Previous attempts to create tools that combine the best of two worlds didn’t quite meet with commercial success. Years following the launch, user numbers for dotsub.com, hakromedia SoundCloud, and videolocalize.com stayed limited. So far, most language industry professionals viewed media localization as a niche service rather than as a growth area. As a result, they didn’t invest in specialized departments and software. But with video content increasing in share, and with media companies demonstrating record revenues, this might eventually change.

However, by the time it does change, translation tools may achieve a “good enough” capability. Fast-forward 1-2 years – most LSPs might be able to subtitle without extra investment or training. It will become even easier to enter into subtitling and compete, leading to price pressure. Subtitling may turn into an even more crowded and low-margin space before you can say “commoditization”.

Dubbing: Home studio vs studio M&A strategy

Dubbing, on the other hand, is a different kind of deal.

So far, the dubbing market has been dominated by larger companies such as Deluxe and SDI Media that provide voice talent in physical sound studios located in multiple countries. Perhaps one of the best examples of this would be Disney’s Let It Go which has been translated into at least 74 languages.

Infrastructure for such projects is costly to build and maintain. Brick-and-mortar studios have high bills and need a constant flow of work to stay profitable. Projects might be hard to find for second-tier language outlets. To have a French studio overloaded and a Croatian studio suffering losses year after year is a realistic scenario for a network company.

The virtual/home studio approach being used by newer players in this field such as TransPerfect, Zoo Digital and Lylo Media, is more scalable and provides acceptable quality for most clients. But will it be enough for high-profile content owners that award the largest contracts?

If the home studio approach produces sustainable growth, commercial software vendors will jump in and replicate the technology, leading to lower entry to dubbing. However, if it fails over 2018-2019, instead M&A will become the go-to-market strategy in the media localization space. Watch out for smaller studio acquisition frenzy!

Reference: http://bit.ly/2LVhf6C

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