Our Neural Network just learned Syntax!

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.

A book in which the word "translation" is highlighted in green. SYSTRAN show the importance of translation in business & in life in general.

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.

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When Neural Machine Translation is boosting Customer Service

In today’s accelerated globalization, booming e-commerce and customer service digitalization, the languages spoken by potential consumers come in hundreds. Global companies are faced with a problematic equation: while they might have centralized their customer service operations, it is still costly to recruit an agent for every language covered. It is nevertheless crucial to respond to customers in their native language quickly, efficiently and at minimal cost to achieve excellent Customer Service.

Breaking the language barrier

It’s simple: translating client emails instantly, responding to them in their language just as quickly or automatically displaying the most common answers in FAQ databases in multiple languages is a real Game Changer. For global Customer Service teams, the response time in foreign languages can be divided by 10 after Neural Machine Translation implementation! Calls are reduced with increased usage of a multilingual online knowledge base and customer satisfaction is higher than ever!

Data leakage is the biggest threat

More importantly, unsecured online translation tools, often used by customer service teams to understand customer queries in foreign languages expose companies to data leaks. The nature of interactions  during customer service operations can be as critical as sharing account information, credit card numbers, passwords and so on…

To guarantee complete security of your customer data, it’s absolutely key to rely on a provider that is able to provide the translation service on-premise or accessible via a private cloud. It is also true for internal support interactions. SYSTRAN also provide translation solutions for enterprise IT departments following a trend of global support services centralization, and allow them to manage technical support requests worldwide while ensuring user data security.

For more information on SYSTRAN’s multilingual solutions for customer service visit https://www.systransoft.com/business-solutions/customer-service-success/

Video showing how SYSTRAN uses the Neural Machine Translation to help customer service satisfaction through the world. Boosting customer service satisfaction is easier!

Open Source, Multilingual AI and Artificial Neural Networks : The new Holy Grail for the GAFA

Jean Senellart, CTO & CEO of SYSTRAN is explaining how SYSTRAN represent a GAFA alternative when they took benefit from Open Source, Multilingual AI and Artificial Neural Networks. Since 2016, there has been a sharp increase in open source machine translation projects based on neural networks or Neural Machine Translation (NMT) led by companies such as Google, Facebook and SYSTRAN. Why have machine translation and NMT-related innovations become the new Holy Grail for tech companies? And does the future of these companies rely on machine translation?

Never before has a technological field undergone so much disruption in such a short time. Invented in the 1960s, machine translation was first based on grammatical and syntactical rules until 2007. Statistical modelling (known as statistical translation or SMT), which matured particularly due to the abundance of data, then took over. Although statistical translation was introduced by IBM in the 1990s, it took 15 years for the technology to reach mass adoption. Neural Machine Translation on the other hand, only took two years to be widely adopted by the industry after being introduced by academia in 2014, showing the acceleration of innovation in this field. Machine translation is currently experiencing a golden age of technology.

From Big Data to Good Data

Not only have these successive waves of technology differed in their pace of development and adoption, but their key strengths or “core values” have also changed. In rule-based translation, value was brought by code and accumulated linguistic resources. For statistical models, the amount of data was paramount. The more data you had, the better the quality of your translation and your evaluation via the BLEU score (Bilingual Evaluation Understudy, the most widely used algorithm measuring machine translation quality). Now, the move to Machine translation based on neural networks and Deep Learning is well underway and has brought about major changes. The engines are trained to learn language as a child does, progressing step by step. The challenge is not only to process exponential data (Big Data) but more importantly to feed the engines the most qualitative data possible. Hence the interest in “Good data.”

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Complying with MiFID II: 4 reasons why you need an intelligent translation solution

As of January 3rd 2018, companies in the financial industry operating in Europe are required by law to fully comply with the new MiFID II regulation. A good portion of the new rules requires translating various documents for a multilingual audience.

Smartphone in through which Intelligent Translation Solution is adopt with the motto "Complying with MiFID II" With that in mind, here are four reasons why you need neural machine translation to help you lead your compliance project to success:

1 – Effortlessly translate detailed information on tons of transactions

2 – Easily provide investors with multilingual research reports and articles

3 – Produce E-Learning and other company material to educate employees across the EU on complying with these new regulations

4 – Translate contracts and other official investment documents 

You can find the whole infographic here.

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

MILIPOL PARIS 2017 poster shows a sunset in the background & a satellite antenna, SYSTRANs & VOCAPIA reseach logos in the foreground.

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.

Logo of Alvarez & Marsal, a famous consultancy firm and also a SYSTRAN's translation partner. SYSTRAN Pure Neural™ Machine TranslationThe 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.

Picture of Phil Beckett, Managing Director at Alvarez & Marsal a SYSTRAN's translation partner. SYSTRAN Pure Neural™ Machine Translation

Phil Beckett – Managing Director at Alvarez & Marsal

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The use of machine translation in eDiscovery

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Professional is working on a laptop and writing something on a sheet. SYSTRAN's & A&M logos are below the picture. The use of Machine Translation in eDiscovery is the tagline.

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

Officiel Poster of SYSTAN and its partner Relativity at Digital Summit on October 16th-17th. Showing a woman in a Department of security controlOn 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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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.

A man is holding a tablet and reading something - Neural Machine Translation

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.

Woman is holding a tablet. She's also reading on it and smile - Neural Machine Translation

“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.

Plane passager is holding a smartphone - Neural Machine TranslationOf 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.