Category: Translation Business

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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Translating in-house? Here’s what you need to know

Translating in-house? Here’s what you need to know

How does your law firm get translations done? If you do some or all of it in-house, you may run your own translation team; rely on the language skills of lawyers, knowledge managers and other colleagues; or use a combination of the two.

Given the ever-present need to work as efficiently as possible in order to meet delivery deadlines, we’ve seen a number of ways in which law firms try to speed up their in-house translation process. Here are a few of the things they’ve learned along the way:

Be wary of manually reusing content

Translators may look to reuse content from previous translations by copying and pasting chunks of text. It’s an exceedingly common practice — but one that’s fraught with danger. Just as with drafting precedents, starting with existing documents risks missing subtle differences between cases — and horror stories abound of the consequences of these errors (including houses being sold to the wrong person). The end result: most of the time saved reusing content could well be spent fixing errors (either those caught internally or spotted by your client).

Many hands make light work?

Another option is to split a document into sections for several colleagues to work on concurrently. This may be faster and may reduce the chance of errors when compared to reusing old documents, but it raises concerns around consistency between different translators. It also means great care must be taken when the translated document is stitched back together at the end of the process.

Free tools come with a cost

It may be tempting to turn to free online translation tools like Google Translate to make the work go faster, especially for small jobs like birth certificates or even tweets. But law firms are generally against the idea — and understandably so. For starters, free tools aren’t designed to translate complex legal terminology, or to render legal concepts from one jurisdiction to another — so there’s no guarantee of quality or accuracy. Reviewing and amending the translated output could therefore take more time than doing the work from scratch. Besides that, using such tools could put confidential or valuable information at risk.

The right technology is a lifesaver

One reason many law firms struggle to get translations done quickly, accurately and consistently is that they’re doing the work using standard office productivity apps. But as these apps aren’t designed with translators’ needs in mind, they don’t include the features needed to make translation an efficient process.

That’s why you’ll find that firms who are translating in-house successfully are often using computer-assisted translation (CAT) tools.

CAT tools are designed to help translators work faster and smarter. Using technology developed specifically to support translation work, it can:

  • Raise quality and consistency, and accelerate handling of repetitive content, with translation memories and terminology databases that simplify reuse of previously approved content
  • Increase translators’ productivity with features to increase the speed of translation while safeguarding against mistakes
  • Turn lengthy documents round faster by making it easy for several people to collaborate on the same translation

Balance speed, quality and cost

At the end of it all, the translation challenge comes down to three variables: cost, quality, and speed. Just as with the outsourcing model, translating in-house brings unique challenges to maintaining the balance between these three variables.

CAT tools can help law firms tip the scales in their favor, by giving them a way to improve speed without compromising on quality.

Reference: http://bit.ly/2OgpXcg

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Why People Leave the Language Industry

Why People Leave the Language Industry

Working in a language service provider (LSP) can be an exciting and challenging place to be: managing complex projects, figuring out best workflows, delivering to demanding deadlines, working with teams of people from diverse cultural and linguistic backgrounds… But what motivates people who have studied languages and translation to leave behind a career in the language industry? And where are they now?

Slator conducted a number of interviews and spoke to individuals who had all worked for 3+ years in a sales or operational capacity within a language service provider in either France or the UK. All had studied languages and / or translation to degree or post-grad level.

Respondents revealed that they were originally attracted to the language industry for a variety of reasons, including the appeal of a “multicultural environment” coupled with a “passion for languages”, “the ability to be able to communicate without borders”, having the opportunity to use languages on a daily basis and “learning more about…the art of translation and culture surrounding the language.”

The aspects of their roles that they enjoyed most ranged from “the camaraderie and internationalism” to “client side, travel and coaching people” and the fact that “it was always busy and fast-paced which kept me on my toes.”

International Environment

Asked what they liked about the companies (LSPs) they worked in, one respondent identified “the feel you were doing something important for large clients” and another said that she had worked in “a small company and became friends with colleagues”. Again, “international environment” and “international focus with different office locations” featured prominently.

On the benefits of the industry as a whole, one respondent said she “learned a lot about finance and legal, practicing at least two languages daily”. Innovation and technology also contributed to the appeal: “it is forever evolving and has an exciting future ahead,” one said, while another thought that “interesting technology advances” were a distinct positive.

So, despite the lure of an international and evolving market, where you can meet people and travel, what factors are leading individuals to leave the industry in favor of alternative careers? And what opportunities are they seeking out?

Why People Leave

We asked respondents to describe their reasons for leaving the language industry in their own words. One person, who worked for six years in project management, said that it was “impossible to progress, [there was] no reward for going the extra mile, [and] no human contact”.

Another, who spent three years in the industry in a project management role echoed this sentiment, saying that “I found I’d reached a plateau, both in terms of pay and also in terms of what my job entailed (it became quite samey)”. She went on to say “I figured my skills could be transferred into another sector which could offer more variety and options for the future”.

60% of respondents cited lack of progression for the primary reason for leaving.

One individual saw few differentiating factors between LSPs saying “I felt that most LSPs were much of the same and there was little difference between being a project manager at any of them. I decided to leave to travel for a few months and didn’t feel like I wanted to return to the same career.” Yet another thought that “the progression was too slow. Individuals were being overloaded and therefore project standards were slipping.” One person identified a culture of blame in the company he left behind to pursue a teaching career.

Only one respondent gave reasons that were uniquely “pull factors” and said that she had left “to pursue other avenues in a different field and focus on the transferable skills gained during my time at the various LSPs, notably, leadership and coaching” after 13 years in the industry.

When pressed, 60% of respondents cited lack of progression for the primary reason for leaving, with comments including “impossible to progress”, no “options for career progression” and “being overlooked for promotion”.

Stepping Stone to Another Career

A few interview questions centered on the respondents’ experience of pursuing a new career – how easy did they find it to secure a new role and how had working the language industry prepared them for a new line of work.

Most (80%) felt that the skills they had acquired were relevant to other industries. And the respondents had moved into a variety of roles and industries, from HR and Recruitment to Wine Buying, Events Coordination for a cloud networking company, Supply / Logistics, Marketing and Teaching.

Most found it “quick”, “easy” or “relatively easy” to find a new role, suggesting that language industry leavers are in high demand across a wide range of industries.

Most (80%) do not regret the decision to leave, while 20% are unsure (perhaps because it is still early days), but none are openly dissatisfied with their choice to pursue a different career. Similarly, none could foresee returning to the industry out of choice, e.g. “I hope it does not happen, but if it does, it will be for a short time as I am not willing to stay in this industry.”

The exception to this seemed to be the possibility of returning in the capacity of a freelance linguist: “The flexibility of being a freelance translator might tempt me at some point in the future. (I still do some freelance translating on the side of my current job)” and “If there was a feasible option of getting into interpreting I’d also be tempted.”

What Can We Do Better?

In a tick box question, respondents selected the factors that would have led them to stay in the language industry. A more interesting role and better long-term career opportunities topped the list, with more money, and better progression, management and work-life balance also emerging as possible difference-makers.

Participants were also asked what advice and recommendations they would have for employers in the language industry through questions such as “If you could change one thing about the language industry what would it be?” and “What, if anything, could your company could have done differently that would have encouraged you to stay in the language industry?”

Of the respondents who said that there was something their company could have done to retain them (60%), all said it would have involved better career developments or pay.

Things that people hoped to change about the industry at large included:

  • “Better salaries compared to Project Managers in other industries”;
  • “Make its employees feel more valued. The turnover was very high as extra duties were heaped on with no reward”;
  • “Make people more aware of how much work is behind a simple translated document, have respect for others’ job/work”;
  • “More honesty around quality assurance”

As the industry matures with a wider breadth of job roles and shows healthy levels of employment globally, companies must pay attention not to lose key talent to competing industries. Some might argue that LSPs are necessarily bottom heavy and that attrition among the more junior levels of project managers and sales employees is to be expected as it is not possible for everyone to progress. But turnover of staff carries a huge cost – not least for training, recruitment and extra overtime – one that can be driven down by heeding advice and implementing good people management strategies.

Reference: http://bit.ly/2LPiFhS

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Court Rules That Free MT Isn’t Enough for Legal Scenarios

Court Rules That Free MT Isn’t Enough for Legal Scenarios

In recent months, we have increasingly heard from enterprise localization groups that their executives are pushing for the adoption of neural machine translation (NMT), driven largely by a very successful public relations campaign from Google that has touted the very real improvements in NMT over the past two years. Unfortunately, some business leaders have seen media coverage and concluded that they no longer need language professionals and can simply replace translators with the “magic” of AI.

Given the way many people have come to treat Google Translate and its competitors as authorities on all matters linguistic, it was really only a matter of time before free, online MT played a role in a court case. Recently, an English-speaking police officer in Kansas City used Google Translate to converse with a Spanish-speaking individual and obtain consent to search his car. In the course of the officer’s search he discovered a large quantity of illegal narcotics. It seemed an open-and-shut case: he had permission to search the vehicle and found the drugs.

But a judge threw out the case: Google Translate rendered the officer’s “Can I search the car?” in Spanish as “¿Puedo buscar el auto?,” which is more along the lines of “Can I look for the car?” The defendant successfully argued that he gave permission only for the officer to look for the car, not look in it. The court ruled that the Google Translate output was not sufficient for consent and tossed the case.

Although legal experts argue that this particular case is unlikely to change things much – police can take additional steps to clarify consent – it points to the danger that comes from relying on MT uncritically and should serve as a caution against uncritical MT boosterism. It won’t slow down the adoption of MT – the economic requirements it fulfills are too compelling – but cases like these should provide a wake-up call for naïve adoption in cases where accuracy matters. NMT may be great when you are willing to ask questions and clarify responses, but you cannot rely upon it for cases where the results can affect life, liberty, or liability… or your bottom line.

The lesson here is not that MT is bad. After all, humans can make similar mistakes. Consider the case of Willie Ramirez, which resulted in a US$71 million judgment against a hospital, centered around a misunderstanding of the Spanish word “intoxicado” – which means “poisoned” rather than “intoxicated” – that left a young baseball star with permanent disability.

The difference is that humans respond to context and can take steps to clarify, while MT by itself does not. It provides a best machine guess at a translation, but takes no responsibility when things go wrong. Google specifically states that it does not provide any sort of warranty that its services will be accurate or usable, and indeed the company could not do so given the way its technology functions. By contrast, a human interpreter who would be liable for getting something wrong will have a strong incentive to make sure that the details are correct. An expert linguist will know what matters in a given context and ensure that the communication reflects it. MT doesn’t care.

Contrary to fears that MT will replace human translators, CSA Research’s examination of the issue shows that MT can augment human translators, making them more efficient and better able to focus on the important details.

Our research shows LSPs that MT accelerates the growth of LSPs that adopt it. LSPs and enterprises alike need to understand the technology, how to work with it, where it applies, and how best to deploy it. Translation buyers need a realistic assessment of what it can and cannot do for them and should work closely with providers to achieve their goals. Like any technology, MT is a tool, and tools used incorrectly can harm their users and those around them, but when applied properly, technology tools deliver real benefits. Just don’t expect NMT to provide you with legal or medical advice and always involve professional linguists when accuracy and message matter.

Reference: http://bit.ly/2mK8ywQ

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The Translation Industry’s Top-Earning Career Paths

The Translation Industry’s Top-Earning Career Paths

Adaptive’s recruiters are often asked by candidates how they can build their careers to raise their market value and earnings. Here we share our map of the paths which lead to some of the top-paying roles in the global language services industry.

First things first – you don’t have to be a salesperson to make big bucks.

Often when Adaptive is approached by candidates looking to up their earnings in the language services industry, there’s an expectation that only the high-flying BDMs and C-suite management are making top money.

After all, BDMs are on commission plans and signing big customer deals can be very lucrative. And it’s true – top BDMs and sales managers can be making as much as anyone on this list.

But salespeople are not the only ones with strong pay packages in the language industry.

In fact, we’ve left sales out of our list below to offer alternate options to translation and localization industry professionals looking to build their careers.

So here we go – four routes to top-paying roles if cold calling isn’t your thing:

1. Business Unit Leadership

e.g. VP Life Sciences, VP Engineering

Broad-ranging VP titles usually signify a role that is a mix of client relations, operations and specific expertise in a particular area.

Professionals in these positions are in charge of ‘business units’ which operate like mini-companies within the larger organization, focused on one specific area – such as services to the Life Sciences market or engineering services.

This means the VP’s responsibility is wide, often covering a separate profit and loss account for their unit. VPs leading these areas can come from a variety of backgrounds, but have often worked their way up an internal hierarchy where their increasing experience makes them more and more valuable.

They head up hiring, account management and ensure that their company’s service offering continues to be competitive and evolve with the market.

Career Entry Point: Project Manager, Account Manager, BDM

Key Skill: ability to combine rounded business skills with deep subject-matter expertise

Average Salary Range: $100,000 – $160,000 + bonus

2. Internal Technology Management

e.g. CTO, VP of Technology

At the highest level, technology managers need to be more than just experts in localization workflows, and lead areas such as networking, security, compliance, training, technology change management, data recovery and more.

Their focus is on the role technology plays in helping the company reach strategic goals and impacting overall P&L.

Localization career paths typically go from specialist to generalist with candidates building a base in CAT tools, internal and client workflows and then rounding out generalist IT competencies to continue progressing.

Career Entry Point: CAT Tools Specialist, Localization Technology Manager, Localization Engineer

Average Salary Range: $120,000 – $180,000 + bonus

Key skill: ability to visualize and implement technology changes which make high-value improvements to the global organization

3. Operations & General Management

e.g. VP Operations, General Manager

A great goal for Project Managers!

Many of the industry’s top-paid professionals in operations (production) leadership started ‘in the trenches’ as PMs. Growth in this career channel comes from deep first-hand knowledge of internal workflows, aptitude for working directly with key customers and versatile operational skills – organization, planning, financial management and personnel leadership.

As operations candidates move up the career ladder, they broaden their generalist business skills and combine them with their expert knowledge of localization processes to eventually step up and take overall responsibility.

Career Entry Point: Project Manager, QA Manager

Salary range: $120,000 – $150,000 + bonus

Key skill: ability to design and maintain efficient teams and workflows to deliver reliably and profitably for customers

4. Client Solutions Development

e.g. VP Client Solutions, Global Solutions Manager

A specialist team within most LSPs, solutions professionals focus on bridging the gap between sales, production and IT.

Their focus is building creative solutions for prospective and existing customers, which involves customizing, integrating and potentially selecting new tools to bring together clients’ existing technology systems and those used by the LSP.

Many client solutions experts get their start in engineering and are well versed in CAT tools, but also work to develop strong client relationship skills throughout their careers. Often professionals in this space work on the client side for at least a few years, building inside knowledge from the buyer perspective.

At the top of the tree, global managers for solutions teams build some of the most advanced workflows in commercial localization.

Career Entry Point: Localization Engineer, CAT Tools Specialist, Project Manager, Account Manager

Salary range: $130,000 – $150,000 + bonus

Key skill: ability to think creatively to create unique technology-based workflow solutions

* * *

Adaptive Globalization recruits within the translation, localization and language technology sectors from entry-level to VP+.

We love to chat with translation industry professionals about how they can make the right career moves to achieve their goals. Drop me a line at ray.green@adaptiveglobalization.com

You can check out Adaptive Globalization’s full list of vacancies for PMs, Account Managers, Loc Engineers, BDMs and more in our job listings here

Reference: http://bit.ly/2LAenLc

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Creative Destruction in the Localization Industry

Creative Destruction in the Localization Industry

Excerpts from an article with the same title, written by Ameesh Randeri in Multilingual Magazine.  Ameesh Randeri is part of the localization solutions department at Autodesk and manages the vendor and linguistic quality management functions. He has over 12 years of experience in the localization industry, having worked on both the buyer and seller sides.

Te concept of creative destruction was derived from the works of Karl Marx by economist Joseph Schumpeter. Schumpeter elaborated on the concept in his 1942 book Capitalism, Socialism, and Democracy, where he described creative destruction as the “process of industrial mutation that incessantly revolutionizes the economic structure from within, incessantly destroying the old one, incessantly creating a new one.

What began as a concept of economics started being used broadly across the spectrum to describe breakthrough innovation that requires invention and ingenuity — as well as breaking apart or destroying the previous order. To look for examples of creative destruction, just look around you. Artificial intelligence, machine learning and automation are creating massive efficiency gains and productivity increases, but they are also causing millions to lose jobs. Uber and other ride hailing apps worldwide are revolutionizing transport, but many traditional taxi companies are suffering.

Te process of creative destruction and innovation is accelerating over time. To understand this, we can look at the Schumpeterian (Kondratieff) waves of technological innovation. We are currently in the fifth wave of innovation ushered in by digital networks, the software industry and new media.

Te effects of the digital revolution can be felt across the spectrum. Te localization industry is no exception and is undergoing fast-paced digital disruption. There is a confluence of technology in localization tools and processes that are ushering in major changes.

The localization industry: Drawing parallels from the Industrial Revolution

All of us are familiar with the Industrial Revolution. It commenced in the second half of the 18th century and went on until the mid-19th century. As a result of the Industrial Revolution, we witnessed a transition from hand production methods to machine-based methods and factories that facilitated mass production. It ushered in innovation and urbanization. It was creative destruction at its best. Looking back at the Industrial Revolution, we see that there were inflection points, following which there were massive surges and changes in the industry.

Translation has historically been a human and manual task. A translator looks at the source text and translates it while keeping in mind grammar, style, terminology and several other factors. Te translation throughput is limited by a human’s productivity, which severely
limits the volume of translation and time required. In 1764, James Hargreaves invented the spinning jenny, a machine that enabled an individual to produce multiple spools of
threads simultaneously. Inventor Samuel Compton innovated further and came up with the spinning mule, further improving the process. Next was the mechanization of cloth weaving through the power loom, invented by Edmund Cartwright. These innovators and their inventions completely transformed the textile industry.

For the localization industry, a similar innovation is machine translation (MT). Tough research into MT had been going on for many years, it went mainstream post-2005. Rule-based and statistical MT engines were created, which resulted in drastic productivity increases. However, the quality was nowhere near what a human could produce and hence the MT engines became a supplemental technology, aiding humans and helping them increase productivity.

There was a 30%-60% productivity gain based on the language and engine that was used. There was fear that translators’ roles would diminish. But rather than diminish, their role evolved into post-editing.

The real breakthrough came in 2016 when Google and Microsoft went public with their neural machine translation (NMT) engines. Te quality produced by NMT is not yet flawless, but it seems to be very close to human translation. It can also reproduce some of the finer
nuances of writing style and creativity that were lacking in the rule-based and statistical machine translation engines. NMT is a big step forward in reducing the human footprint in the translation process. It is without a doubt an inflection point and while not perfect yet, it
has the same disruptive potential as the spinning jenny and the power loom. Sharp productivity increases, lower prices and since a machine is behind it, the volumes that can be managed are endless. And hence it renews concerns about whether translators will be needed. It is to the translation industry what the spinning jenny was to textiles, where several manual workers were
replaced by machines.

What history teaches us though is that although there is a loss of jobs based on the existing task or technology, there are newer ones created to support the newer task or technology.

In the steel industry, two inventors charted a new course: Abraham Darby, who created a cheaper, easier method to produce cast iron, using a coke-fueled furnace and Henry Bessemer, who invented the Bessemer process, the first inexpensive process for mass-producing steel. The Bessemer process revolutionized steel manufacturing by decreasing its cost, from £40 per long ton to £6–7 per long ton. Besides the
reduction in cost, there were major increases in speed and the need for labor decreased sharply.

The localization industry is seeing the creation of its own Bessemer process, called continuous localization. Simply explained, it is a fully-connected and automated process where the content creators and developers create source material that is passed for translation in continuous, small chunks. The translated content is continually merged back, facilitating continuous deployment and release. It is an extension of the agile approach and it can be demonstrated with the example of mobile applications where latest updates are continually pushed through to our phones in multiple languages. To facilitate continuous localization, vendor platforms or computer-assisted translation (CAT) tools need to be able to connect to client systems or clients need to provide CAT tool-like interfaces for vendors and their resources to use. The process would flow seamlessly from the developer or content creator creating content to the post-editor doing edits to the machine translated content. The Bessemer process in the steel industry paved the way for large-scale continuous and efficient steel production. Similarly, continuous localization has the potential to pave the way for large-scale continuous and efficient localization enabling companies to localize more, into more languages at lower prices.

There were many other disruptive technologies and processes that led to the Industrial Revolution. For the localization industry as well, there are several other tools and process improvements in play.

Audiovisual localization and interpretation: This is a theme that began evolving in recent years. Players like Microsoft-Skype and Google have made improvements in the text-to-speech, speech-to-text arena. The text to speech has become more human-like though it isn’t there yet. Speech-to-text has improved significantly as well, with the output quality going up and errors reducing. Interpretation is the other area where we see automated solutions springing up. Google’s new headphones are one example of automated interpretation solutions.

Automated terminology extraction: This is one that hasn’t garnered as much attention and focus. While there is consensus that terminology is an important aspect of localization quality, it always seems to be relegated to a lower tier from a technological advancement standpoint. There are several interesting commercial as well as open source solutions that have greatly improved terminology extraction and reduced the false positives. This area could potentially be served by artificial intelligence and machine learning solutions in the future.

Automated quality assurance (QA) checks: QA checks can be categorized into two main areas – functional and linguistic. In terms of functional QA, automations have been around for several years and have vastly improved over time. There is already exploration on applying machine learning and artificial intelligence to functional automations to predict bugs, to create scripts that are self-healing and so on. Linguistic QA on the other hand has seen some automation primarily in the areas of spelling and terminology checks. However, the automation is limited in what it can achieve and does not replace the need for human checks or audits. This is an area that could benefit hugely from artificial intelligence and machine learning.

Local language support using chatbots: Chatbots are fast becoming the first level of customer support for most companies. Most chatbots are still in English. However, we are starting to see chatbots in local languages powered by machine translation engines in the background thus enabling local language support for international customers.

Data (big or small): While data is not a tool, technology or process by itself, it is important to call it out. Data is central to a lot of the technologies and processes mentioned above. Without a good corpus, there is no machine translation. For automated terminology extraction and automated QA checks, the challenge is to have a big enough corpus of data making it possible to train the machine. In addition, metadata becomes critical. Today metadata is important to provide translators with additional contextual information, to ensure higher quality output. In future, metadata will provide the same information to machines – to a machine translation system, to an automated QA check and so on. This highlights the importance of data!

The evolution in localization is nothing but the forces of creative destruction. Each new process/technology is destructing an old way of operating and creating a new way forward. It also means that old jobs are being made redundant while new ones are being created.

How far is this future? Well, the entire process is extremely resource and technology intensive. Many companies will require a lot of time to adopt these practices. This provides the perfect opportunity for sellers to spruce up their offering and provide an automated digital localization solution. Companies with access to abundant resources or funding should be able to achieve this sooner. This is also why a pan-industry open source platform may accelerate this transformation.

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