“So, You Think Your Game Is Localized?”

Game Is Localized? Player holds a controller and plays video game - Systran
Highlights from Webinar with Elizabeth Senouci And Victor Ramirez

Last month, we conducted a webinar “So, You Think Your Game Is Localized?”, the first of a 3-part-series given by Elizabeth Senouci from XTM International, and Victor Ramirez from SYSTRAN.

If you couldn’t guess by the title, “So, You Think Your Game Is Localized?” was a webinar focused on Video Game Localization. Senouci and Ramirez are both experts on the topic and thus decided to share their knowledge with the video games community.

In the webinar, Senouci and Ramirez discussed the need for game localization, some basic terminologies associated with it, user interfaces, global marketing, and the importance of customer service.

“Localization isn’t just one thing you can do and just get done with it. It’s a holistic process and it’s actually customized based on your game, your product,” Elizabeth said in her intro.

Why Localize?

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Four Ways SYSTRAN’s Neural Machine Translation Supports Global Commerce

Explatory infographic the neural machine translation through Systran pure neural server and its process.
SYSTRAN Pure Neural Server Process // SYSTRAN

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

Reading a Czech book through eye glasses. Systran gives 4 reasons why NMT is primary for such complex & important structure as 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.

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

Logo of SYSTRAN Pure Neural Server technology, a huge gap in AI quality & speed improvement for translation

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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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.