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.

Since the new European Union law in existence with General Data Protection Regulation (GDPR), companies have been receiving heavy fines when found guilty of data breaches.

GDPR provides citizens with the right to ask businesses to keep their information secure. This means that they have some kind of authority to prevent criminals from stealing any kind of personal information.

When organizations use online translation tools to translate customer information, they are literally serving the information to criminals in a silver platter. Thus, whenever using an online translator, organizations have to be wary about the type of information being disclosed and whether they are in compliance with GDPR or not.

Learn if your team is violating the data compliance law with online translation tools.

Generally speaking, even professional human translators require the distribution of source content across translators and editors in order to perform specific tasks. Basically, there are two ways in which confidential information is leaked.

First, the information is stolen ‘in transit’. This means, when data is transferred over unsecured servers such as public Wi-Fi hotspot or cloud servers, there occurs data leaks which are clear indications of lax oversight.

Secondly, data can be leaked when using online machine translation tools as mentioned earlier. The data translated online can be found by anyone who conducts an Internet search.

To sum up, completely discarding the use of Machine Translation tools is never the only option to overcome data breach issues. The solution is to use an on-premise NMT to secure the information that is being translated and to mask the sensitive data with tool like the AnonymizerTM.

With secure and Ai-powered tools like these, your company not only avoids any traces in the open internet, but you will also save money and help build assets for reuse. Furthermore, the AnonymizerTM mechanism works in compliance with GDPR and aids your data stay secure within the corporate network. *

It’s time you start using it to avoid data leaks, and more importantly to stay away from heavy GDPR fines.

*Anonymization cannot guarantee GDPR compliance. However, this technique can help lessen the risk of violations before personal data needs to be transferred outside of the European Union.

To learn more, contact us via email Anonymizer or visit NMT

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.

2. Save Budget for Higher Cost Tasks

Many government linguists perform tasks outside of translations, such as analysis, interpretation, cultural consulting, and executive-level reporting. With MT, a linguist has more time to devote to these tasks best performed by a person.  Since translation work is less expensive to produce with automation, the saved budget can be re-allocated to the higher cost labor tasks.  The result is a faster path to achieving the end goal, which may be locating the bad guy or perhaps better servicing a constituent.

3. Build Data Assets for Re-Use

A little known fact about SYSTRAN’s MT is the capability to save post-edited translations into “Translation Memory (TM)” for re-use purposes.  These translations are typically saved at the sentence level in a format that links the source language to the target language. Translators can also use and develop their own dictionaries with the SYSTRAN software.   These linguistic data assets, which belong to the government client, can be employed as needed to tailor translation work for specific domains and style preferences.

4. Secure Government Data

Neural MT is ubiquitous through free internet offerings.  In fact, many government linguists now visit these online MT portals to aid them in their translation work.  However, for cybersecurity reasons linguists are advised to use good judgement when choosing between open internet portals versus an on-premise software.  On-premise software, like SYSTRAN’s, is preferred by agencies with sensitive data.  Even open source analyst projects (e.g. OSINT, PAI) that need to avoid leaving any traces of identity in the open internet should consider an on-premise solution.

 Increasingly, the linguist and MT roles will become more synergistic with the advancement of neural technology.  Linguists will naturally become more technically-savvy, and better apt to loop that knowledge into real-case improvements to neural MT.  This cycle of continuous improvement using neural MT fits perfectly into the government’s mantra to become world leaders in the field of artificial intelligence.

The AI technology to advance language projects now exists.  We believe this advancement will support both budget savings and faster turnaround times whilst keeping your data secure and improving  the overall quality of your translation work.  Contact SYSTRAN today and get started!

To learn more, contact Maggie Nguyen

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/

Seven Tips for Better Translation Results

Whether you are using SYSTRAN’s Desktop, Enterprise Server, SaaS or online software, one question our IT Support is asked all the time is “How Can I Improve My Translation Output?” If incorrect or incomplete text or data is input into Machine Translation software, (also known as “garbage in, garbage out”) the outcome will, more often than not, also be incorrect or incomplete.

Here are seven tips to a better result:

  1. Use complete, grammatical sentences – Sentences should always start with a capital letter and end in either a period, exclamation point or question mark. A complete sentence always contains a verb, expresses an idea and makes sense standing alone.
  1. Avoid the passive voice – The passive voice is used to show interest in the person or object that experiences an action rather than the person or object that performs the action.
  1. Punctuation is important; clauses will translate best if separated by commas – Punctuation is the feature of writing that gives meaning to the written word. An error in punctuation can convey a completely different meaning to the one that is intended.
  1. Try to use simple, declarative sentences – A declarative sentence makes a statement, is in a present tense, and ends in a period. These are the most common sentences in the English language. It can either be a simple or compound sentence.
  1. Avoid ambiguity – To avoid ambiguity keep your sentences short, start with the subject, then the verb and end with the object. Use words and tenses consistently throughout.
  1. Avoid abbreviations, acronyms, jargon and colloquialisms – An abbreviation or acronym should first be spelled out if there are to be used consistently in a document. Colloquialisms are informal forms of speech and should be used mainly for speaking and not writing. Abbreviations, acronyms and jargon can be added to your User Dictionary or Translation Memories.
  1. Use your Dictionary Manager – SYSTRAN software includes a feature called the Dictionary Manager, which allows you to create your own dictionaries to supplement or override the main dictionary that comes with the program.  Using this feature can make substantial improvements to the translation.

The accuracy of the translation varies with the input.  If the input text is grammatically correct and unambiguous, it should translate well enough to convey the gist of what’s been written.

By: Ashley Shuler, Technical Support Analyst and Brooke Palm, Director of Customer Care SYSTRAN Software, Inc.

SYSTRAN presents its latest translation engines: huge quality & speed improvement!

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.

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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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The use of machine translation in eDiscovery

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This article originally appeared on Kirti Vashee’s Blog.

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

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

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

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

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

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

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

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

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

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

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

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

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

So how should we anticipate seeing the effects of NMT?

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

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

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

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

Related Links

SYSTRAN PNMT DEMO

Positive Feedback from PNMT Beta Tester

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