Digital platforms and the post-language economy

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.

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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.

Gisting

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.

New solutions

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.”

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.

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Positive Feedback from PNMT Beta Tester

This article was originally published on ITProPortal Digital platforms and the post-language economy by Denis Gachot.

Managing Compliance Programs in Multiple Languages

Regulatory compliance is expensive, but the cost of compliance related failure is much higher. The top US banks paid out fines amounting to $204 billion in 175 settlements dating back to 2009*. It is challenging for banks and financial institutions to adapt to the complexities of regulations in a global marketplace. Policies need to support the modern pace of transactions and international trade. Therefore, compliance professionals and the tools they use have an increasing need to make sense of communications or data in foreign languages.

Current software platforms address the need to understand, analyze, and review archived communications proficiently in English. However, a critical subset of that content happens in foreign languages that most of the existing platforms do not handle. The key to ensuring compliance of data in foreign languages is being able to perform
real-time translations at scale, either as a stand-alone capability or an integrated function of the review platform. No textual data – no matter its language – is left out of the monitoring or review processes.

In the world of risk management, the focus areas below are becoming increasingly multilingual and require a consistent effort to ensure sustained compliance:

  • Trading communications and conduct
  • BSA/AML Programs
  • Insider leaks, business conduct, and HR issues
  • Corporate social media policy

REAL-TIME MACHINE TRANSLATIONS FOR RISK ASSESSMENT PROCESSES

SYSTRAN’s real-time translation supports multilingual data compliance with class leading performance in data centers and enterprise networks around the globe. The incremental capabilities and gains include:

  • Automated or on-demand machine translation (MT)
    of the messages for review, audit, or discovery processes
  • Identify the foreign languages in messages at volume and scale
  • Store translated files for audit or review cycles
  • Increase productivity and optimize cost simultaneously with MT technology in comparison to acquiring human translation resources
  • Streamline workflows by eliminating deviations to ad-hoc processes just to discover content in other languages
  • Increase workflow efficiency by integrating MT capability into the workflow that is designed for one language

SYSTRAN, a global leader in language translation technology, will showcase its newest translation software, Pure Neural Machine Translation, at FIBA AML compliance conference in Miami, FL, March 6-8.

For more information, contact Craig Stern at craig.stern@systrangroup.com and to set up a meeting, click here.

* source: Keefe, Bruyette & Woods (2015)

About SYSTRAN

SYSTRAN has been helping commercial, defense, and national security organizations capture mission-critical data for the last 49 years. We operate globally with locations in Americas, Europe, and Asia.

As the first software company to introduce Neural Machine Translation technology, SYSTRAN is continuing to lead the innovation in language technologies. SYSTRAN’s brand new Purely Neural Machine Translation (PNMT) products utilize Neural Networks and Deep Learning algorithms to achieve unprecedented translation quality that is near human translation levels.

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Positive Feedback from PNMT Beta Tester

SYSTRAN to Host Private Viewings of Pure Neural Machine Translation Technology at FIBA AML Compliance Conference

 

SYSTRAN to Host Private Viewings of Pure Neural Machine Translation Technology at FIBA AML Compliance Conference

This article was originally published on Newswire SYSTRAN to Host Private Viewings of Pure Neural Machine Translation Technology at FIBA AML Compliance Conference

 

SYSTRAN, a global leader in language translation technology, will showcase its newest translation software, Pure Neural Machine Translation, at FIBA AML compliance conference in Miami, FL, March 6-8.

Harvard partnered with SYSTRAN to develop the next generation of AI-based Language Translation that runs on Neural Networks. They call it Pure Neural Machine Translation (PNMT). SYSTRAN will sponsor FIBA AML to showcase how PNMT is helping companies solve the linguistic challenges related to multinational compliance and AML.

 

“PNMT is giving the banking industry a more powerful tool to maintain compliance, even when working with terabytes of data stored in multiple languages around the world,” says Ken Behan, Vice President of Sales and Marketing of SYSTRAN.

The PNMT engine is revolutionary in that it processes an entire sentence or paragraph in the context of the overall document topic, instead of translating segment by segment. This creates a far more accurate output than ever before seen with machine translation, especially for Asian languages. For compliance, accuracy in translation is important in being able to flag issues or suspicious transactions.

SYSTRAN’s software provides the ability to perform machine translation on both audio and text in 45+ language pairs.

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

To learn more about SYSTRAN’s machine translation solutions, visit https://demo pnmt.systran.net

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Positive Feedback from PNMT Beta Tester

This article was originally published on Newswire SYSTRAN to Host Private Viewings of Pure Neural Machine Translation Technology at FIBA AML Compliance Conference

SYSTRAN to Hold Private Viewings of Pure Neural Machine Translation Technology for eDiscovery Translation at Legaltech 2017

This article was originally published on PR Newswire SYSTRAN to Hold Private Viewings of Pure Neural Machine Translation Technology for eDiscovery Translation at Legaltech 2017

SYSTRAN, a global leader in language translation technology, will showcase its newest translation software, called Pure Neural Machine Translation (PNMT), at Legaltech in New York this month.

As the demand for multi-lingual litigation continues to increase, law firms need a way to translate eDiscovery data quickly, reliably and cost-effectively. PNMT is the perfect solution to this challenge.

“PNMT offers an incredible opportunity for legal firms to perform multi-lingual eDiscovery more efficiently,” says Ken Behan, Vice President of Sales and Marketing of SYSTRAN. “Having the ability to automatically translate terabytes of data and get reliable results is invaluable for law firms, especially when timelines and resources are tight.”

The PNMT engine is revolutionary in that it processes an entire sentence or paragraph in the context of the overall document topic, instead of translating segment by segment. This creates a far more accurate output than ever before seen with machine translation, especially for Asian languages.

In fact, early tests show that PNMT translated documents are of the same or even higher quality than human-translated content. Test subjects could not correctly identify which translated samples were done by machine translation versus a human. The quality is that good.

SYSTRAN’s software provides legal organizations the ability to perform eDiscovery translation on both audio and text in real-time in 45+ language pairs. The PNMT software can be used as a connector to eDiscovery software, such as Relativity, or on its own.

SYSTRAN’s team is setting private meetings for an exclusive view of the PNMT concept and how it can be utilized by legal teams to boost productivity and cut translation costs during eDiscovery. To set up a meeting or schedule a demo during Legaltech, contact Craig Stern at craig.stern@systrangroup.com

To learn more about SYSTRAN’s machine translation solutions for eDiscovery, visit http://www.systransoft.com/translation-products/integrations/cmless-for-relativity.

 

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SYSTRAN for Relativity – More Information

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This article was originally published on PR Newswire SYSTRAN to Hold Private Viewings of Pure Neural Machine Translation Technology for eDiscovery Translation at Legaltech 2017

How Neural Machine Translation Will Change E-Discovery

When a global enterprise gets sued, it’s vital to know who is involved and how. But finding out who to blame isn’t always simple.

Global law firms are tasked with sifting through thousands, sometimes millions of emails, chats, and legal documentation during eDiscovery. These documents and audio recordings could be in many different languages and stored around the world. Sometimes that data is stored in countries with strong data protection regulations, such as Brazil and parts of the EU, so it cannot under any circumstances leave the country.

So, how can an office in the U.S. review hundreds of days of correspondence in multiple languages?

If the firm hires translators, they’ll need dozens with a strong knowledge of everything from slang to deep subject matter expertise of the topic in discovery. If instead they decide to go with an e-discovery translation solution, they’ll still need help during the review process, especially for data in Asian languages – there are several ways to interpret one word, for which there may be five slang alternatives. In either case, the team must spend a lot of time and money to get reliable and accurate results.

Until now, that is.

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Supporting Lean Manufacturing Efforts with Machine Translation technology

 Don’t let language be a hurdle in your business

Lean Manufacturing[1] involves constant efforts to eliminate or reduce ‘muda’ (Japanese term defining waste or any activity that consumes resources without adding value) in design, manufacturing, distribution and customer service processes.

As an operational system, Lean Manufacturing maximizes added value, reduces essential support and eliminates waste in all processes throughout the value chain. Waste in this regard may include over-production, inventory tasks, waiting time, correction, transportation and over-processing.

In summary, the equation for Lean Management is: Increased profitability equals increasing prices or reducing costs. A big part of the cost is the turnaround time between an order being placed and when it is shipped.

What is your Manufacturing Lead Time from the placement of an order to shipping? What would you expect as the standard for your business? What do you foresee as a likely evolution for both you and your customers?
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A multilingual trade information system: the road to global success!

The world has become a global village: electronic commerce is truly international and must be standardized

The 2nd Logistics Information Standardization Forum held in Seoul, South Korea on September 2016, brought together a host of actors in an effort to standardize international logistics information as a key factor in improving logistic processes in business. The forum put forth the creation of an international consensus for a cooperation system on international logistics information.

At the AFNET association, we promote and develop such standards so as to improve relationships between organizations and enterprises. Standardizing electronic commerce for logistics means defining a common document structure for order processing transactions and product deliveries.

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SYSTRAN’s Continuing Neural MT Evolution

by Kirti Vashee on eMpTy Pages, a blog about translation technology, localization and collaboration

Recently, I had the opportunity and kind invitation to attend the SYSTRAN community day event where many members of their product development, marketing, and management team gathered with major customers and partners.

The objective was to share information about the continuing evolution of their new Pure Neural MT (PNMT) technology,  share detailed PNMT output quality evaluation results, and provide initial customer user experience data with the new technology. Also, naturally such an event creates a more active and intense dialogue between company employees and customers and partners.  This, I think has substantial value for a company that seeks to align product offerings with its customer’s actual needs.

Ongoing Enhancements of the PNMT Product Offering

The event made it clear that SYSTRAN is well down the NMT path, possibly years ahead of other MT vendors, and provided a review of the current status of their rapidly evolving PNMT technology.

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Round-trip translation: no more entertainment with PNMT™ systems

Round-trip translation (RTT), also known as back-and-forth translation, recursive translation and bi-directional translation, is the process of translating a word, phrase or text into another language (forward translation), then translating the result back into the original language (back translation), using machine translation (MT) software.
It is often used by laypeople to evaluate a machine translation system, or to test whether a text is suitable for MT when they are unfamiliar with the target language. Because the resulting text can often differ substantially from the original, RTT can also be a source of entertainment*.

When we translate the paragraph below…

…with SYSTRAN Pure Neural™ Machine Translation (PNMT™) we get the translation into French : Continue reading

Pure Neural™ Machine Translation (PNMT™) Beta Test

by Lori Thicke Founder & CEO at Lexcelera

neural

Yesterday I translated our company presentation with Systran’s new Pure Neural™ Machine Translation (PNMT™) engine, and I was amazed at the results.

The presentation in question was a complete overview of all of our services, 59 pages of French text that was edited three separate times to make sure the quality was perfect. (Thanks Faten, Boris and Laurence!)

Then, two days ago, just as I was putting the finishing touches on the presentation for a response to an RFP (Request For Proposals), I found out that our prospective client (a major French manufacturer) wanted our response in English. I had just one day to deliver 59 pages of perfect English content!

Let me give you some background to explain why I, the CEO of a translation company, decided to use Neural Machine Translation for one of our most important commercial documents for one of our most important tenders.

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