Four Ways SYSTRAN’s Neural Machine Translation Supports Global Commerce

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?

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The art of speeding up NMT with SYSTRAN 2nd generation engines

Machine Translation users care about quality and performance. Based on our own observations and the feedback we’ve received; the quality of our Neural MT is impressive. Evaluating performance is a stickier subject, but we’d like to dig our hands in and present our innovations and achievements and how it benefits NMT users.

By performance we mostly mean the manner in which a system performs in terms of speed and efficiency in varying production environments. It is important to note that performance and quality in Neural MT are tightly connected: it is easy to accelerate a given model compromising on the quality. Therefore, when evaluating performance improvement, we always check that quality remains very close to optimal quality.

Since switching to NMT at the end of 2016, we’ve invested our R&D efforts into optimizing our engines to be more efficient, while maintaining and even improving translation accuracy. Our latest, 2nd generation NMT engines, available in our latest release of SYSTRAN Pure Neural® Server, implements several technical optimizations that make the translation faster and more efficient.

New model architecture

The first generation of neural translation engines was based on recurrent neural networks (RNN). This architecture requires the source text to be encoded sequentially, word by word, before generating the translation.

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Online Translation Tools and Data Breaches

Is Your Team Violating Data Compliance Laws?

Data leakage and lack of information are two critical issues that can harm businesses. Nonetheless, due to the ever-growing global marketing and communication needs, the temptation to use the fast and free online translation tools are rising.

Apart from the apparent dangers that these tools pose to businesses such as miscommunication, loss of business, and cultural insults, there is critical important threat that many enterprises often fail to recognize. 

Whenever an employee uses a free online translation tool, they may cause massive data privacy breaches by making the consumer data searchable. Data breaches as such mainly happen due to employee negligence looking for quick machine translation, and it can often put millions of customers’ sensitive data at exposed on the internet.

Companies thus struggle to find the right balance between enabling business and securing information. Without the capability of translating software, potentially hundreds, if not thousands, of employees could turn to free translation tools to get their content translated in turn making the content available online.

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Four Reasons to Use NMT for Translation Work in the U.S. Federal Government

Until recently, using machine translation (MT) was considered a hindrance by serious translators.  Now that machine translation is powered by artificial intelligence, translators in the government are intrigued by this new technology.  Forward-thinking linguist programs recognize the value of MT, and it’s only a matter of time when others will follow suit.  Consider these four reasons as motivation for modernizing the status quo:

1. Translate Smarter

As with many other skilled professions – accountants, doctors, analysts – technology is a time-saver.  Translators now have the same benefit.  In fact, commercial benchmarks show that neural MT helps translators post-edit at 2000 words per hour.  Without technology, which is typically the case in the government, translators translate at 300 words per hour.  Imagine the time-savings — the same 6000-word document can now be translated in 3 hours instead of 20.   Additionally, SYSTRAN MT will retain the formatting of the original document, which further saves time.

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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.

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 http://www.systransoft.com/business-solutions/customer-service-success/

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

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.

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

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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