Meet us at MILIPOL PARIS 2017 and discover how Artificial Intelligence enhances Multi-language OSINT & COMINT

Today’s defense and security organizations are up against all sorts of growing threats and they need the most efficient intelligence tools possible. As real-time information for quick decision making is crucial, they face huge challenges in terms of data collection and analysis:

  • Exponential amounts of information to be collected and processed (social media, rise of User Generated Content)
  • Variety of sources and formats (text, audio, video, image)
  • Multiple languages and lack of linguistic skills and expertise, especially in Middle Eastern languages.

Entities in charge of territories security more than ever need to have efficient multilingual intelligence capabilities of OSINT and COMINT.

On November 21-24th, SYSTRAN will be participating in the MILIPOL event in Paris in partnership with VOCAPIA Research. MILIPOL Paris is the leading event for homeland security and is organized under the patronage of the French Ministry of Interior.

As the leader in language processing technology, SYSTRAN launched in 2016 the first Neural Machine Translation technology able to provide intelligence professionals with a secure automated translation solution available in more than 140 languages pairs with an outstanding quality for languages as Arabic or Chinese for example.

Our partner, Vocapia Research, develops leading-edge multilingual speech processing technologies to enable speech recognition, automatic audio segmentation and much more. We will be showcasing our Real Time Text & Speech Neural Translation solution and hold DEMO sessions to show you how we integrate into your internal processes to seamlessly manage multilingual projects.

We therefore invite you to stop by our stand (Hall 6 n°E 155) to check out our solutions and discuss about how we can bring true value to your organization.

Emmanuel TONNELIER, Director of Defence & Intelligence Solutions at SYSTRAN will be one of your main contact there. Please feel free to get in touch with him before or during the event to arrange a meeting onsite.

We look forward to see you on our booth,

SYSTRAN’s team

Useful Information:

MILIPOL’s Website

Venue Adress:

Parc des Expositions de Paris-Nord Villepinte
ZAC de Paris Nord 2 – CD 40
93420 Villepinte
France

 

Return on expertise: Alvarez & Marsal reveal the backstage of eDiscovery success stories with SYSTRAN Pure Neural™ Machine Translation

SYSTRAN’s solution are used every day by various types of companies across many industries to get the most accurate and secure automatic translations on any type of content – from sensitive documents to websites to mobile apps and much more.  We’d like to focus today on how one of our clients – Alvarez & Marsal, a consultancy firm- uses SYSTRAN’s platform to manage eDiscovery projects with the highest efficiency and accuracy.

The processes and tools used in eDiscovery scenarios are, most of the time, quite complex given the large volumes of electronic data produced. Unlike hard-copy evidence, e-documents are a lot more dynamic and contain various metadata that demand the highest translation quality in order to eliminate any claims of spoliation at any time in a litigation case.

Phil Beckett, the firm’s Managing Director, who has recently been named ‘Investigation Digital Forensic Expert of the year’ by Who’s Who Legal is talking to us about how SYSTRAN’s solutions plug into their internal processes to manage their projects end to end.

Phil Beckett – Managing Director at Alvarez & Marsal

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The use of machine translation in eDiscovery

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This article originally appeared on Kirti Vashee’s Blog.

There are some kinds of translation applications where MT just makes sense, and it would be foolish to even attempt these kinds of projects without decent MT technology as a foundation. Usually, this is because these applications have some combination of the following factors:

  • Very large volume of source content that simply could NOT be translated without MT in any useful time frame
  • Rapid turnaround requirement (days, hours or minutes) for the content to have any value to the translation consumers
  • A user tolerance for lower quality translations at least in early stages of information review
  • To enable information and document triage when dealing with large document collections and help to identify highest priority content from a large mass of undifferentiated content. This process also helps to identify the most important and relevant documents to send to higher quality human translation.
  • Translation Cost prohibitions (usually related to volume)

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SYSTRAN at the Digital Forensics and Analysis Summit

Digital SummitOn October 16-17th, SYSTRAN and its partner Relativity will be participating in the Digital Forensics & Analysis Summit as sponsors and exhibitors. The Digital Forensics & Analysis Summit is a two-day forum that will gather international experts from around the world in Abu Dhabi to share best practices on how technology is used in their forensics department to extract evidence that is able to stand up in trial.

Since information governance, forensics and eDiscovery procedures face mounting pressure from the growth of Electronic stored Information, legal standards and rules governing digital investigation requirements have also contributed to the rise in litigation and associated legal costs.

Within this environment, documents written in languages other than English, including data collection, processing and reviewing can pose major challenges, especially when ensuring the mandatory confidentiality of those procedures, as these typically forbid online translation. Organizations need to search by keyword and find relevant documents and emails in the appropriate languages while controlling costs and maximizing productivity. Therefore time-intensive human translation is usually not an option and the need for viable machine translation solutions becomes all the more apparent.

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SYSTRAN Launches Podcast Exploring How Language Impacts Business & Government

If your job depends on understanding what’s being said in more than one language, SYSTRAN’s new podcast holds the answers.

Whether you’re trying to read an e-mail from a colleague, give an answer to a customer, or deliver accurate documentation across the globe, language plays a key role in determining the success of your actions. If you cannot communicate, then the action is useless. The same goes for finding evidence in a huge pile of multilingual data, increasing the value of your user-generate-content, running compliance programs internationally, or protecting a nation as a government organization. Language governs everything we do.

SYSTRAN aims to share the unique stories, stats, and perspective on how language impacts business and our everyday lives in a brand new podcast series, released today.

In the last 50 years, SYSTRAN has been delivering machine translation capabilities to Fortune 500, billion-dollar start-ups, education institutions, government communities and LSPs all over the globe. This has given the company a unique perspective across industries such as banking, finance, manufacturing, legal, internet, security, software, wearable devices and IoT.

Its newest technology, Neural Machine Translation (NMT) is disrupting the language translation industry by giving organizations access to higher quality machine translation than ever before. Unraveling where NMT and language intersect with the speed and security of a business is the aim of their new podcast.

“We’ve had the incredible opportunity to uncover how NMT intersects with big data, global teams, new user experiences and security, and that’s what we want to share with you every month through different Podcast Series,” says Craig Stern, Marketing Manager of the Americas at SYSTRAN.

It’s not always clear where NMT technology can fit into your workflow. In this podcast, SYSTRAN will uncover how companies are using SYSTRAN’s Pure Neural Machine Translation (PNMT) to gain market share and unexpected places where language translation is vital.

The first Podcast series, “The language of bribery, and the challenge of anti-corruption communications: a real-world story” will cover FCPA compliance and one man’s true recount of going to prison because of violating those laws.

The series is available now to listen on the following channels:

For more information, email BeyondLanguage@systrangroup.com.

How Neural Machine Translation (NMT) is Creating a Global Post-Language Economy

This article was originally published in The Next Web. How Neural Machine Translation (NMT) is Creating a Global Post-Language Economy.

Businesses are same all over the world. People work hard to make their products sell, their companies grow and broaden their futures. But while business may be universally understood, the languages we conduct it in are not. One of the exciting recent developments in technology by Google has the potential to change the face of business as we know it. It is known as Neural Machine Translation (NMT), and it promises to break down language barriers to a degree we have never seen before.

“The Internet created a global economy, but there are still numerous friction points, chief among them a substantial language barrier,” says Denis Gachot, CEO of SYSTRAN Software Inc., a language technology company. “NMT is a scalable solution to the language barrier problem that can achieve numerous outcomes – allow businesses to rapidly transmit large volumes of documents in different languages, connect small businesses to the global economy that could not operate without professional translation, and even empower consumers to find products and services they couldn’t have before.”

How is it different from Rule-based and Phrase-based translation models?

Neural Machine Translation is the shift from rule-based and phrase-based translation models, which translates word-by-word or in groups of words between languages. Instead, NMT translates entire sentences at a time, looking to discern cultural, colloquial, and technical contexts to create more accurate translations. The technology mirrors human intuition in its ability to pick up on subtleties, but because it is a machine, it can process these faster than we can.

Let’s take an example of an official note that says: パリに出張の時に私はCEOに会いました.

With a phrase-based machine translation (PBMT), you would receive the translated output as: ‘I met Paris in the CEO trip doing business.’ With Neural Machine Translation (NMT), you will get: ‘I met the CEO when I was in Paris on a business trip.’

In Japanese, main verbs are always used at the end, so to make sense of the used phrases within it, you need to reference the end of a sentence.

NMT is also an end-to-end learning system, which means that it gets better the longer it is in use. This deep learning function powers its neural network, which computes translations with such a degree of complexity that often times even its developers are unsure how it arrived at its conclusion. It is, in a sense, very much like the human mind.

The beauty in all this is the ease to conduct international business now. Caring for clients, clarifying concerns with business partners, or trying to reach new markets is possible with the click of a button. All those written communications that would once have required a linguist can be translated with a comparable degree of accuracy using NMT, making business as usual, unusually uncomplicated.

So how should we anticipate seeing the effects of NMT?

Firstly, NMT allows small businesses to bring their product to the global market, and with them, their increasingly high standards for innovative, quality products. If a businesswoman in Poland wants to sell to clients in Japan, she can do so without having to spend weeks laboring over miscommunications and misunderstandings in emails written and read in second languages. This will increase the diversity of products and services available, while also speeding the pace of innovation.

Large corporations are already benefiting from this technology. Google’s launch of NMT technology operates in eight of the world’s major languages, covering 30 percent of the world’s population. SYSTRAN’s PNMT facilitates communications in 70 different language pairs.

An influx of both quality and quantity in products and producers will inevitably boost the current of globalization and its roaring marketplace. Language, which was originally a tool for organizing mankind, has in recent times become a barrier. This new wave of advancements in NMT may be just the crossing over point the global market has been looking for but it is more than ready to bridge that language barrier very soon.

SYSTRAN’s team is setting private meetings for an exclusive view of the PNMT concept. For more information, contact Craig Stern at craig.stern@systrangroup.com and to set up a meeting, click here.

Related Links

SYSTRAN PNMT DEMO

Positive Feedback from PNMT Beta Tester

This article was originally published in The Next Web. How Neural Machine Translation (NMT) is Creating a Global Post-Language Economy.

How Neural Machine Translation Will Help Online Marketplaces Turn their Individual Sellers into Global Players

This article was originally published in inside BIGDATA by Ken Behan. How Neural Machine Translation Will Help Online Marketplaces Turn their Individual Sellers into Global Players.

In this special guest feature, Ken Behan, Chief Growth Officer at SYSTRAN, discusses new technology that is aiming to totally eradicate the problem of language as one of the primary factors limiting small businesses from operating abroad. Powered by big data, AI and deep learning, Neural Machine Translation (NMT) advances from previous models that translated words one at a time, to the more human-like method of reading sentences for context and meaning. Ken is responsible for defining and implementing the growth strategy for SYSTRAN who have a global presence in the US, Europe and Asia. With 20 years’ experience in the language intelligence industry, he is considered a thought leader in language translation having held several Senior Executive roles within the industry. A native of Ireland he is also a serial entrepreneur as well as business mentor to several Irish Start-ups.

“귀걸이가 맘에들어요. 친구들에게 추천하겠습니다.” As an online seller, is this good news or bad? In a survey commissioned by Education First, 49% of executives admitted that language barriers and communication difficulties had prevented significant international business deals from being done. In the same way that language barriers hamper big business, they hamper small business.

 

There are three ways to increase revenue: acquire more customers, increase the average spend per customer, and increase the number of transactions per customer.

Here’s the question for eCommerce platforms with millions of creative, ambitious, well-intended people looking to increase their income: can your seller in France close a deal in Japan? Can your seller in China provide customer support to his buyers in Spain? Can your users see reviews in their native language, no matter what the source language was?

Historically, these capabilities have been reserved for the captains of industry. eBay has been using their proprietary Machine Translation for years. But machine learning is making it available to everyone.

Technology firms have been working on the language problem for decades, but the last several years have seen significant advances that merit the attention of business leaders who are eyeing international markets. The technology is called Neural Machine Translation (NMT), a deep learning system that captures meaning in the context of translated sentences, not the single word. The net result is fluency, where previously, only a “gist” was possible. By combining NMT with existing big data tools that scrape, structure and analyze, new value propositions have suddenly become more attainable.

For example, let’s take a case from a peer-to-peer e-commerce site where a Korean customer inquires of an American Vendor, “미국에서 귀걸이를 한국으로 배송하면 얼마나 걸릴까요?”

When translated with NMT  the result is “How long will it take to get earrings from America to Korea?”  where as a statistical engine will return “In the United States, how long will it take to deliver the Korean earrings,” making commerce far more difficult to transact and probably several emails to clarify.

Similarly, reviews on web sites are extremely important with 88% of people saying that they now incorporate reviews as part of their buying process. In an “English only” world this tends not to be a challenge but, as HBR reported, 72.4% of people said  they would be more likely to buy a product with information in their own language. NMT makes this possible today and with annual E-commerce revenue growth in double digits, e-tailers have a tremendous opportunity to accelerate revenue growth with minimal investment.

Customer self-service has also exploded over the last few years with many companies relying on “super users” to solve their clients’ problems. Again those outside the top 4 global languages find themselves in the dark most of the time. By implementing NMT solutions, companies are not only benefiting from happier customers in languages they struggled with, but also fend off local “copycat” technologies.

Of the three revenue growth options mentioned at the top of this piece, increasing spend per transaction and increasing number of transactions per customer are the easiest of the three. At the opposite end, losing a customer is a heavier cost then all. We live in a world where if two tech companies are created equal – the user will buy from the one with the better experience or the more relatable values. Offering cross-language chat, multilanguage reviews and knowledge based would put you in the upper echelon of competitors.

By the way, this ‘reads’ this. It was good news. This text means this: “I like the earrings. I’ll recommend them to friends.”

SYSTRAN’s team is setting private meetings for an exclusive view of the PNMT concept. For more information, contact Craig Stern at craig.stern@systrangroup.com and to set up a meeting, click here.

Related Links

SYSTRAN PNMT DEMO

Positive Feedback from PNMT Beta Tester

This article written by Ken Behan was originally published in inside BIGDATA. How Neural Machine Translation Will Help Online Marketplaces Turn their Individual Sellers into Global Players.

This New Translation Tech Will Smash the Language Barrier to Doing Business Globally

This article was originally published in Entrepreneur Magazine. This New Translation Tech Will Smash the Language Barrier to Doing Business Globally.

The ability to translate between languages quickly and inexpensively opens vast new possibilities.

Behind-the-scenes technology is not usually the sexy stuff that makes big headlines. Unless you are the IT guy in the back room, this kind of difficult-to-explain stuff is not the leading topic of discussion at your dinner party. Neural Machine Translation (NMT) is different. Few things on the horizon currently have as much importance or as much appeal. What it does behind the scenes changes the face of the whole economy.

In short, NMT is a deep learning technology that translates within context, not just one word at a time. Recent advancements have made this approach nearly fluent, making previous iterations of machine translation irrelevant overnight. The usual language translation heavyweights, Google and SYSTRAN, are pioneering this technology and making it available to different segments of the market.

So what does this have to do with business? In short, everything. Here are four ways NMT will impact the market.

1. Small businesses with global reach. 

Small businesses are the driving force of the U.S. economy. According to the Small Business Administration, these companies employ 99.7 percent of America’s workforce. Their impact on the economy is far ranging, from innovative products to essential services. But they are also limited in reach, typically restricted by small operational budgets. That means they don’t frequently sell to international markets, and especially not to foreign language economies.

“After the internet arrived, we started hearing the term ‘global economy,’” says Denis Gachot, CEO at SYSTRAN. “It implies an ability to communicate, connect and transact with anyone in the world. But most don’t have that ability because, despite the internet removing geographic barriers, there is still very much a communication barrier in language.”

A large corporation can hire multilingual professionals to run remote offices and provide customer service in numerous languages. That is a luxury most small businesses cannot afford. NMT allows these businesses to immediately translate their web pages and online communications into more than 100 languages. “Neural Machine Translation is going to change the economy by giving more businesses a language capability they can use to communicate and understand in real time,” says Gachot.

That means that the shop owner in Milwaukee, Wisconsin, can market her products to people in Germany, Japan, Brazil and dozens of other countries.

2. Automatic translation of thousands of documents.

But NMT is not just an opportunity for growth for small business. Larger corporations stand to benefit from the quick processing capabilities of NMT as well. Company documents can quickly be translated into multiple languages with reliable accuracy and precision.

Previously, that kind of work would have required a team of highly skilled linguists and would have taken weeks to translate the original and check the resulting copy. But NMT changes that. The open network set-up of NMT technology allows for “soft alignment,” which means the system can search for the context of phrases and sentences instead of translating word by word. The reliability of this kind of machine translation, and the speed in which it is accomplished, can dramatically change the way companies are able to operate and ultimately serve their clientele all over the world.

3. Radically changes specific industries. 

A change in translation technology means a huge change in specific industries. For example, legal eDiscovery can be extremely complicated for legal teams trying to access emails, chats and online communications in other languages. Each communication has to be carefully assessed for meaning and intent within the context of colloquial uses of the language and varying forms of slang. This is a nightmarish recipe for anyone working on such a case.

NMT changes this by rapidly learning terminology nuances and then producing high-quality translations at a fraction of the time it takes a human team to do the same work.

By using our own brain as a model, this technology is able to apply human intuition at machine speed. “Techniques for understanding slang include custom dictionaries and custom translation engines,” Gachot explains. “These engines are trained from hundreds of thousands of pieces of human translated content and are able to mimic the fluidity of expressions found in those human translations, including when there is colloquialism involved. We also have custom dictionaries for Information Technology (IT), economics, tourism, dialog and so on.”

4. Opens up isolated areas to the global market.

With the expansion of small businesses in global trade and more accessibility to unique products from a greater range of places, previously unreachable geographical markets will open up.

Right now, many countries are left out of the global marketplace because small businesses have no way of marketing to them or handling transactions across language barriers. Consumers in emerging markets may be the first to see the impact, but more remote countries will feel the effects soon after. Online marketplaces like Etsy and Zazzle can make language translation automatic, allowing users to conduct business in their own language. This reduces the friction in global commerce and increases the opportunities that remote consumers have to products and services around the world.

Business owners in remote corners of the world will now have a grander stage for their products, larger companies will be able to better care for their clients, and service industries will evolve to meet the changing tides. That is a complex technology worth talking about at your dinner party.

SYSTRAN’s team is setting private meetings for an exclusive view of the PNMT concept. For more information, contact Craig Stern at craig.stern@systrangroup.com and to set up a meeting, click here.

Related Links

SYSTRAN PNMT DEMO

Positive Feedback from PNMT Beta Tester

This article was originally published in Entrepreneur Magazine. This New Translation Tech Will Smash the Language Barrier to Doing Business Globally.

What Does the Post-Language Economy Look Like

This article was originally published on Inc. What Does the Post-Language Economy Look Like.

CREDIT: Getty Images

How translation technology is changing the face of small business on a global scale.

The greatest barrier to global trade is not space and time, or even geopolitics. The greatest barrier today is language. Only major corporations have been able to set up mirror offices and global operations networks to facilitate international trade. Human translators, an expensive resource, act as bridges between producers and consumers or business partners who speak different languages. But this is an imperfect solution to a significant challenge. Very few people are fluent, or even competent, in more than a handful of languages, making it difficult for companies to do business in more than a handful of countries.

Much of the world’s economy is comprised of small businesses that do not have the resources to overcome dozens of language barriers in countries all over the world. But if small businesses could overcome the language barrier, what would the global market look like? Where would we be if the ‘mom and pop’ stores that fuel our local economies were able to join the global market?

“There is a wealth of potential in start-ups and entrepreneurial businesses all around the world,” says Denis Gachot, CEO of Systran Group. “One of the biggest obstacles to harnessing that potential is the language barrier. Neural Machine Translation (NMT) is working to bring that wall down.”

Neural Machine Translation

A natural step in the progression of communication technologies, NMT is a tool that connects people who would otherwise have no means to understand one another. The technology is aimed at translating large volumes of business communications almost instantly and with more effectiveness than human operators.

Unlike preceding translation technologies, NMT builds a neural center of information that can be tuned for more apt translations. The network approach sidesteps the bottleneck often seen in translation technology that hinders the improvement of encoder-decoder systems. This “soft-alignment” is a reflection of our own intuition, so these language translations done by a machine are more human than ever. “This technology is of a caliber that deserves the attention of everyone in the field,” says Gachot. “It can translate at near-human levels of accuracy and can translate massive volumes of information exponentially faster than we can operate.”

NMT is an end-to-end learning system, so it learns and corrects itself through continued use. Picking up patterns in languages for more accurate translations, NMT systems will continue to improve with time and application, making them the ideal employee.

But part of the beauty of this system is that it is a scalable technology and capable of processing volumes of information exponentially faster than humans are able to. This makes NMT an affordable option for small businesses looking to take their products into more markets. Doors previously closed because of the language barrier are now swinging open. And the world is changing for it.

Companies Using NMT as Plugin

The last year has seen huge leaps in NMT technology. While it remains a tool predominantly for larger businesses, NMT is proving its salt as a revolutionary force in the international market. In September, Google researchers announced their version for this technology, which translates entire sentences instead of just single words, providing a more authentic and relevant translation. Currently, it functions in eight major world languages, able to service some 35 percent of the earth’s population.

Already, NMT is being used in clouds and network plugins. Facebook announced in March of last year that they would be using neural networks for their own page translations.

But most exciting in this progression of NMT is its ability to change the way small businesses have been able to contribute to the global market. Google and Systran are racing to roll out NMT in new languages, already delivering dozens of language pairs. The linguistic technology now facilitates communication in 130 different languages, providing real solutions for internal collaboration, online customer support and eDiscovery in multilingual contexts.

What this means practically is that an online store owner in St. Petersburg, Russia is now able to reach a customer in Montevideo, Uruguay and market and discuss her product with reliable translation technology as the go-between.

Economic Outcomes

Small businesses are key to economic strength. Properly equipped with tools and resources, they have the power to grow and shape economies at any scale. Smarter regulations and tax structures are not the only factors at play here. These businesses need to be able to pursue consumers wherever they may be.

According to the U.S. Small Business Administration (SBA), 99.7 percent of all employer firms are small businesses. They have generated 64 percent of new jobs, and paid 44 percent of the total United States private payroll in the last 20 years. The prospect of enabling these small businesses to broaden their customer base and compete in the global market is more than exciting; it is world-changing.

Gachot adds, “For many years, we have tried to deal with language as if it was a barrier for communication – while with neural machine translation, language difference is what makes the richness of communication between different cultures.”

NMT looks to be the pivoting step for global trade. With the language wall crumbling, more small businesses will be able to bring their unique, quality-crafted products and services onto the international market scene, which is good for everyone.

SYSTRAN’s team is setting private meetings for an exclusive view of the PNMT concept. For more information, contact Craig Stern at craig.stern@systrangroup.com and to set up a meeting, click here.

Related Links

SYSTRAN PNMT DEMO

Positive Feedback from PNMT Beta Tester

This article was originally published on Inc. What Does the Post-Language Economy Look Like.

This Translation Tool Is Helping Global Brands Break Language Barriers

This article was originally published on Forbes This Translation Tool Is Helping Global Brands Break Language Barriers

 

Since launching in 2006, Google Translate has grown to over 500 million users worldwide, translating more than 100 billion words daily. In 2016, the tool supported 103 languages, with 92% of its users residing outside of the United States.

While the tech giant sits comfortably atop the growing list of translator apps, there’s one longstanding giant in the shadows, actively innovating and developing the blueprint for how companies like Google define the future of global communications.

Founded in 1968, SYSTRAN stands as the leading provider of language translation software products, delivering real-time language solutions compatible for desktop, mobile, and web-based platforms. Credited as a pioneer in machine translation for over four decades, SYSTRAN remains committed to advancing multilingual communications around the world, removing language barriers between people and businesses to make forging meaningful connections seamless.

SYSTRAN’s software facilitates communication in 140 language pairs, across 20 vertical domains, making them the most sought-after translation software provider amongst top-tier global companies and public agencies. Their translation software improves relevant searches, content management, customer support, B2B communications, and plays a pivotal role in scaling global e-commerce companies.

The tech company has developed a proprietary software product called Pure Neural Machine Translation (PNMT), which is an independently developed variation of a technology called Neural Machine Translation. Instead of translating one word at a time, the technology reads full sentences to determine the meaning and assure each translation is properly contextualized. Pure Neural Machine Translation has proven to be more effective than translation software and services used on Facebook or Google Translate.

Equipped with a trained and experienced team of engineers and linguists, SYSTRAN not only expects to compete, but looks to ultimately surpass power players like Google in the race to design a truly connected world without language divides.

I spoke with SYSTRAN CEO Denis Gachot about the vision behind his company, eliminating language barriers, and his plans for transforming how people and businesses communicate.

What was the specific void or opportunity that inspired the idea behind SYSTRAN’s PNMT?

Denis Gachot: In 1968, the United States Air Force called us to translate Russian to English during the Cold War. The stakes had never been higher; precision, security and speed were required to translate a high volume of data quickly. The only difference today is that we’re not just helping the intelligence community. Large corporations are in a battle, and have global teams that need to get languages right and uphold tight security.Thus, the void we needed to solve for was quality. Pure Neural Machine Translation (PNMT) has brought us translation quality that raised the standard, and is now measured by how well it sounds like a native speaker; a remarkable attribute for a machine to be judged on. PNMT is furthering the opportunity to allow people from anywhere in the world to be able to connect with anyone and understand anything.

What were some of the notable challenges you faced while developing your business?

Denis Gachot: One challenge is that most people are unaware of how they can apply machine translation to their business. Imagine an auto manufacturer who would like translate 200,000 pages of product manuals for communication amongst teams in Germany, Columbia and Japan, using email and instant messaging. They would have two translation engines: One that is optimized for the manuals, and another that is optimized for colloquialism (casual speak and slang). Custom translation engines are created with domain specific dictionaries, customized vocabulary, and preferences. For example, if you have a manufacturing profile, the word ‘PIN’ is defined as a metal object. However, if you have a banking profile, the word ‘PIN’ is defined as a password. Further, you can customize vocabulary based on your company’s nomenclature. You can save settings such as ‘never translate your brand’s name’. Also, anything that’s been edited can be saved in the profile to optimize future translations.

Can you provide an example of a company that uses this form of machine translation and how it has benefited them?

Denis Gachot: Adobe, a company with over 100 products, is an example of how to leverage machine translation. They have detailed support FAQs and product education documents in dozens of languages. If they didn’t, phone lines would start to light up with customer service requests in Russian, Hebrew, Japanese, Chinese, and Spanish, because users can’t find support in their language. The applications are nearly limitless: translating patents from Chinese to English, helping law firms find evidence in hundreds of thousands of files in different languages, and translating scientific research, just to name a few.

Your company was founded in the 1960’s — How has both the language translation technology and the market for this technology evolved over the past several decades?

Denis Gachot: Language technology has advanced in line with technology and culture in general. In 1960, the average person spoke to five people a day. Today, you have over 2,000 friends and colleagues anywhere on Earth that can instantly message each other. We’re seeing our customers apply neural machine translation and big data to evolve everything: sales, e-learning, publishing, customer service, email, eDiscovery, compliance, big data manipulation, and mobile apps. Needs have evolved to fluency. With fluency, we now judge the translations based on how well the message captured the meaning, and how much it sounded like a native speaker; that’s phenomenal.

What goes into the process of developing translation technology and what other aspects of human communication or behavior must be studied in the process?

Denis Gachot: Technically speaking, it’s artificial intelligence. Like the human brain, the neural machine translator learns through a process in which the machine receives a series of stimuli over several weeks. Over the course of those few weeks, the process mimics ‘deep learning.’ Think of ‘if, then’ statements — that’s considered ‘shallow learning.’ Deep learning is multiple ‘if, then’ statements stacked. Our technology runs complex algorithms that keep the engine learning, generalizing the rules of a language from a given translated text, and producing a translation that is eerily close to one done by an actual human. The linguistic expertise (the understanding of language), is a more unique set of knowledge than the software coding, and our machines have been undergoing that process of learning for over 49 years. We’ve amassed such a knowledge base that most users think the translation is done by a human. The reason it is so good is because of the underlying study of how a human being uses language.

How do you see a company like SYSTRAN shaping how people from all parts of the world build connections with each other?

Denis Gachot: If two people can’t speak the same language, there is no connection. Dale Carnegie once said, ‘to understand someone is to repeat back to them what they said better than they originally described it.’ When you confide in your best friend, you do it because you feel heard. It transforms businesses, personal relationships, and even random encounters with strangers. In the future, we’d love to release devices that you can talk into and they translate instantly. Imagine having something as small as a lapel pin that gave you the ability to understand what is being said and respond in any language, this is the future that SYSTRAN will be a part of, and hopefully, in the process help enrich all our lives. Communication fosters connection. Neural Machine Translation fosters connection through sophisticated algorithms that not only translate, but provide fluency so people are understood.

While America is multicultural, people live in somewhat of a westernized bubble — How does a company like SYSTRAN help connect these international communities and enrich culture in nations like the U.S.?

Denis Gachot: To understand and gain command of a new language is to know how a different set of humans and cultures view life. Take the expression of love, time, and death. In America, the word ‘love’ is used abundantly. I ‘love’ these shoes, this house, this drink and so forth. In most other cultures, that word is held for special times with loved ones. This is the foundation of connecting. At a practical level, the difference between the U.S. and the Continents is that our country’s language is the global language, so most Americans don’t know another language or haven’t gotten to know another culture. How many people do you know that have studied a foreign language in college but forgot it all because they didn’t’ use it. Now, imagine being able to send an email in French, an instant message in Korean, and co-create a PowerPoint Presentation in Spanish? Language is like mixing primary colors to make new colors. In Miami, for example, they speak Spanglish. There is a subset of rules about when to use English and Spanish in the same conversation. It’s beautiful.

How do you see SYSTRAN evolving over the next 3-5 years and where do you see your company fitting in the future of where this technology is heading?

Denis Gachot: Most, if not all industry leaders will continue to deploy artificial intelligence in some way. We are a part of that revolution. I am at liberty to tell you that we’re already embedded in many of the leading companies’ internal applications, hardware, and proprietary communication tools. The spoken word is the next frontier, as people move away from communicating with electronic devices in their hands — typing, swiping, tapping — and use their voices instead. Everyday functions like typing will be replaced by dictation, and even human-machine conversation. We already talk with smart home assistants that turn on other devices, answer our questions, and send us alerts. Language translation is a key component of the future of technology.

SYSTRAN’s team is setting private meetings for an exclusive view of the PNMT concept. For more information, contact Craig Stern at craig.stern@systrangroup.com and to set up a meeting, click here.

Related Links

SYSTRAN PNMT DEMO

Positive Feedback from PNMT Beta Tester

This article was originally published on Forbes This Translation Tool Is Helping Global Brands Break Language Barriers.