Since the publication of the Executive Order on Maintaining American Leadership in Artificial Intelligence by the White House this past February, many government agencies are struggling with getting started in AI. They realize use of this technology will help them be more efficient. However, finding those tasks that will be “quick wins” in moving towards AI adoption is the main challenge.Continue reading
For staff of multinational companies who want to translate a simple phrase or word, systems like Google or Microsoft come in just handy. They help you order a taxi in Japan, pay your restaurant bill in France, and impress your clients with a hearty “jó reggelt” (“good morning”) in Budapest. The problem is such tools are notorious for imprecise translations and data leaks.
Would you really want to use Google Translate for that internal email to your affiliates in another country?
On the other hand, research from the European Parliament shows that on average a common language increases trade flows by 44%. So, how do you – and your staff – hack through language barriers and achieve professional communication in the business world?Continue reading
Experience unprecedented integration of customer terminology with neural networks!
SYSTRAN Pure Neural® Server, our state-of-the-art translation technology tailored for businesses, delivers quality, fast, and secure translations using Neural Networks and Artificial Intelligence. We have just added support for a unique feature that takes it a step further. Users can now add custom terminology to be used in their translation tasks. Seasoned users know about User Dictionaries in our previous rule-based and hybrid technology, but this feature was not fully implemented by the Neural Networks. Until now.
Translation tailored to your need
User Dictionaries (UDs) are key in customizing translation to users’ needs by allowing them to determine their own terminology and ensure that it is translated as such regardless of context. They can also be used to disambiguate between a word with multiple meanings. In this case, translation profiles can be created that apply user dictionaries with the ambiguous term translated differently in each. For example, “mettre sous tension” would be translated from French as “to turn on” in a Generic profile, but a user could create an Aeronautical profile and add the entry to a UD as “to energize” and if needed create an Electronics profile for the term to be translated as “to apply power.” User Dictionaries can also quickly correct any translations that are not accurate for the user’s context. User dictionaries are primarily used so that industry jargon and brand, model and product names are translated accurately.
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,
Parc des Expositions de Paris-Nord Villepinte
ZAC de Paris Nord 2 – CD 40
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
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)
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.
This article was originally published in The Next Web. How Neural Machine Translation (NMT) is Creating a Global Post-Language Economy.
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.”
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 article was originally published in Entrepreneur Magazine.
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.
This article was originally published in Entrepreneur Magazine. This New Translation Tech Will Smash the Language Barrier to Doing Business Globally.
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.
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.
This article was originally published on Inc. What Does the Post-Language Economy Look Like.