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
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.
The latest version of our AI-powered Translation Software designed for Businesses
SYSTRAN Pure Neural® Server is our new generation of enterprise translation software based on Artificial Intelligence and Neural networks. It provides outstanding professional quality with the highest standards in data safety.
Our R&D team, extremely active to provide corporate users with state-of-the-art translation technology tailored for business, just released a new generation of Neural MT engines. SYSTRAN new engines are developed with OpenNMT-tf, our AI framework using latest TensorFlow features, and backed by a proprietary new training process: Infinite Training.
– SYSTRAN celebrates its golden anniversary as a machine translation company by looking back at their most memorable milestones.
In the last 50 years, SYSTRAN has had the great pleasure of delivering machine translation capabilities to the Fortune 500, unicorn start-ups, education institutions, non-profits, government communities and LSPs worldwide. They’ve arrived at a unique vantage point across industries such as banking, finance, manufacturing, legal, internet, security, software, wearable devices and IoT.
“To have experienced decades of SYSTRAN’s impact on technology and culture has been a gift,” says Denis A. Gachot, CEO of SYSTRAN Software Inc. “However, what I find more inspiring is the intention of our founder Peter Toma when starting SYSTRAN.”
“I felt deeply that I had to devote my energy to the elimination of world conflict causing factors. As a first step to overcome the language problem, I felt that I should know as many languages as possible and use technology so others could be understood.” – Peter Toma
From powering the translation that helped the U.S. and Soviet astronauts communicate, bringing on-line translation to the internet and assisting the F500 corporations to collaborate globally, these moments not only commemorate their longevity, but they also show their values.
Commenting on reaching 50, Chairman Mr. Chang-Jin Ji believes that SYSTRAN would not be celebrating today if it was not for the dedication of employees around the globe to customer support and innovation. “I truly thank them and the loyal support we have received from our customers.”
Looking to the future, this month SYSTRAN will launch a new generation of their server solution, SYSTRAN Pure Neural® Server, that pushes the quality and fluency boundary further than ever before explains Jean Senellart, Global CTO of SYSTRAN. “This new release benefits from the state-of-the-art research in neural translation and brings to our customers these technologies for their specialized models in a fully integrated solution. Our commitment to Open Source through the OpenNMT project, now comprising more than 1,600 members, has been pushing our development teams to achieve excellence, and is raising the bar for the whole industry.”
See SYSTRAN’s most memorable moments in this commemorative video.
Contact: | Craig Stern | Director of Marketing | firstname.lastname@example.org
[This article originally appeared on Kirti Vashee’s Blog]
This is the final post for the 2017 year, a guest post by Jean Senellart who has been a serious MT practitioner for around 40 years, with deep expertise in all the technology paradigms that have been used to do machine translation. SYSTRAN has recently been running tests building MT systems with different datasets and parameters to evaluate how data and parameter variation affect MT output quality. As Jean said:
” We are continuously feeding data to a collection of models with different parameters – and at each iteration, we change the parameters. We have systems that are being evaluated in this setup for about 2 months and we see that they continue to learn.”
This is more of a vision statement about the future evolution of this (MT) technology, where they continue to learn and improve, rather than a direct reporting of experimental results, and I think is a fitting way to end the year in this blog.
It is very clear to most of us that deep learning based approaches are the way forward for continued MT technology evolution. However, skill with this technology will come with experimentation and understanding of data quality and control parameters. Babies learn by exploration and experimentation, and maybe we need to approach our continued learning, in the same way, learning from purposeful play. Is this not the way that intelligence evolves? Many experts say that AI is going to be driving learning and evolution in business practices in almost every sphere of business.
Meet us at EUROPEAN MANUFACTURING SUMMIT 2017 – 27-29 th November – and discover how Artificial Intelligence enhances multilingual collaboration & content production. We will handle a speaking session the Day 2, 28th at 2:40PM: “Supporting Lean Manufacturing Efforts with Machine Translation Technology“
Today’s Manufacturers are more than ever embracing the digital age and globalization. Global organizations must become more inter-connected to enhance their real-time multilingual collaboration.
To support global lean manufacturing efforts, it is essential to integrate machine translation into the core of the value chain. This will break down language barriers while reducing time to market and achieve the desired levels of quality and cost.
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 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.
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.
This article was originally published on Forbes This Translation Tool Is Helping Global Brands Break Language Barriers.
This article was originally published on ITProPortal Digital platforms and the post-language economy by Denis Gachot.
The world can get even smaller, and new technology is making that happen.
When we imagine international companies, we think large, publicly traded conglomerates that have substantial resources and funds to facilitate operations on opposite ends of the globe. But that is changing.
Already the Internet has shrunk the world so that small companies now rely on software engineers in Pakistan and marketing agencies contract with graphic designers in the Philippines. But the world can get even smaller, and new technology is making that happen.
Today, the new frontier is language. Individuals have a plethora of platforms that allow them to access consumers all over the globe and work with other companies in faraway places – if only they could speak the same language. In an ironic twist, language has turned from something that first facilitated human cooperation and growth, to something that currently impedes our ability to work together.
Technology may finally be ready to abolish that barrier forever. It is somewhat remarkable that in 2017, more than 20 years after widespread use of the Internet began, we still rely almost exclusively on humans to translate language in commercial formats. But translation bears all of the earmarks of those functions that artificial intelligence ought to be capable of replicating, and a technology called Neural Machine Translation (NMT) does just that.
Contextual translation ability
By leveraging its contextual translation ability alongside its deep learning functions, NMT has achieved historic results in the journey to a post-language economy. In a side-by-side comparison with human translators, in a technical domain translation for English-Korean, SYSTRAN’s NMT translations were preferred 41 per cent of the time. That success is achieved by advancing language translation beyond rule-based translation methods.
Before NMT, machine translation models – known as rules-based or ‘phrase-based’ – were only able to reference five to seven words at a time when determining meaning. Each language pair has its own linguistic challenges, but it made the translation for certain languages, like Japanese, more challenging because you need to know the entire sentence to put all of the words into context.
Let’s say a colleague forwards you an urgent email, and it includes this sentence: パリに出張の時に私はCEOに会いました.
With a phrase-based machine translation, you would receive this output: ‘I met Paris in the CEO trip doing business.’ With NMT, you would get: ‘I met the CEO when I was in Paris on a business trip.’
In Japanese, main verbs are placed at the end, so you need to reference the end of a sentence to make sense of the phrases within it. NMT processes the entire sentence (and soon paragraph) from end-to-end without intermediate stages.
That is why context is so important. The effect of machine translation being able to better understand context results in a huge jump in BLEU score (the industry measurement for accuracy). SYSTRAN’s Pure Neural Machine Translation (PNMT) program has seen increases in all 61 language pairs and where we see the biggest increase is in Asian languages. For some languages, we saw a jump of 200 per cent in BLEU score.
With machine translation, we have a metric called ‘Gisting,’ as in ‘you get the gist.’ In addition to this metric, we test whether or not a user can solve their problem with the translated output. Were they able to search a FAQ and customise a piece of software with the answer? Were they able to search a digital database of products and images and find what they were looking to purchase? If yes, then they got the gist.
“Gisting requires extensive post-editing. NMT has moved us into fluency. What fluency allows is the ability to read and understand so that you no longer need to post edit,” says Ken Behan, V.P. of Sales and Marketing. NMT is allowing us to focus on ‘meaning’ and ‘fluency’ scores. With fluency, we ask if the translation sounds like a native speaker wrote it. With fluency and meaning, we can ask:
Were they able to understand a review and make an educated decision? Were they able to read the manual specs and assemble a piece of heavy machinery? Were they able to find a product, read the description and purchase what they were expecting?
Refer to the English translations above. With the first sentence, did you get the gist? Yes! You could infer someone went to Paris on something business related.
With the second, did you understand the meaning and the fluency? Yes, you can understand what kind of trip it was and what happened.
Neural Machine Translation will further advance traditional MT solutions and create new ones in communication, customer support, e-Learning, eDiscovery, compliance and user-generated content to name a few. Also, early-adopting linguists using NMT are already increasing their productivity.
Similar to the human brain, the neural machine translator learns through a process in which the machine receives a series of stimuli over several weeks. This development, based on complex algorithms at the forefront of Deep Learning, enables the PNMT engine to learn, generalise the rules of a language from a given translated text, and produce a translation close to human levels of competency.
You can think of NMT as part of your international go-to-market strategy. In theory, the Internet erased geographical barriers and allowed players of all sizes from all places to compete in what we often call a ‘global economy,’ But we’re not all global competitors because not all of us can communicate in the 26 languages that have 50 million or more speakers. NMT removes language barriers, enabling new and existing players to be global communicators, and thus real global competitors. We’re living in the post-internet economy, and we’re stepping into the post-language economy.
The difference between previous language technologies and what NMT can do today is remarkable, and business leaders should take note. Just ask yourself who you would want to do business with: the guy that says, “I met Paris in the CEO trip doing business,” or the one that says, “I met the CEO when I was in Paris on a business trip.”
This article was originally published on ITProPortal Digital platforms and the post-language economy by Denis Gachot.