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.

Continue reading

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.”

Continue reading

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.

Continue reading

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

Continue reading

The use of machine translation in eDiscovery

Quote

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)

Continue reading

SYSTRAN at the Digital Forensics and Analysis Summit

Digital SummitOn 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.

Continue reading

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.