What’s So Special About Domain Specialization?

Student learning a second language

Language is messy. Ask any person who has ever had to learn a second language and they will tell you that the most difficult aspect isn’t learning all the rules, but understanding the exceptions to the rules — the real-world application of the language. 

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NMT Scaling: 4 Ways to Create a Translation Powerhouse

e-Discovery can be a long, daunting process even in the best of times. In today’s globalized world of data, however, you not only have to worry about the sheer amount of information but also what language the content is in. This is where Neural Machine Translation comes in to break that language barrier. As fast as NMT is, though, odds are you have dreamed about how to make your systems even more efficient. How do you ensure any job can get completed on even the most ambitious of timelines?

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Blackout Redaction is Making Your Job Harder than it Needs to Be

How blackout redaction is making your job harder

When it comes to protecting classified data, blackout redaction has been in use for at least a century. While it is not the only acceptable form of data sanitization, it is historically the oldest and most commonly utilized by eDiscovery firms. This is despite the fact there are more modern and easy-to-use alternatives that save time and reduce errors. The two main data sanitization alternatives that meet legal requirements include anonymization and pseudonymization.

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The California Consumer Protection Act’s (CCPA) Impact on eDiscovery Firms

As noted by Anju Khurana, Head of Privacy of the Americas, Bank of New York Mellon, “There are now over 100+ privacy laws in the world and GDPR is driving other countries to adopt similar regulations.” (corpcounsel.com, Oct. 2019). The California Consumer Protection Act (“CCPA”) which comes into effect on January 1, 2020, is the latest, and very likely not the last. Most data privacy experts anticipate additional states enacting data privacy regulations and think it likely that Congress will eventually do so at the federal level.

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OpenNMT-tf 2.0: a milestone in the OpenNMT project

OpenNMT-tf 2.0 workshop. Red Kakemonos and French Tech Central logo in front of the entrance door of Station F held in Paris in March 2018.
OpenNMT workshop held in March 2018 at Station F in Paris // Copyright SYSTRAN

SYSTRAN has been wholeheartedly involved in open source development over the past few years via the OpenNMT initiative,whose goal is to build a ready-to-use, fully inclusive, industry and research ready development framework for Neural Machine Translation (NMT). OpenNMT guarantees state-of-the-art systems to be integrated into SYSTRAN products and motivates us to continuously innovate.

In 2017, we published OpenNMT-tf, an open source toolkit for neural machine translation. This project is integrated into SYSTRAN’s model training architecture and plays a key role in the production of the 2nd generation of NMT engines.

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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

Man is holding a tablet through which he has access to users data.

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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This was SYSTRAN Community Day’18!

Last week we hosted the 2018 edition of SYSTRAN Community Day! The conference was an exciting day full of energy, from Jean Senellart’s opening speech to our client success stories and celebrating SYSTRAN 50th anniversary! Here is a quick look at the conference highlights:

Jean Senellart announces the launch of a marketplace connecting the expertise of neural model trainers with the needs of industrial MT users

Full room for Jean Senellart conference at SYSTRAN Community Day, took place on November 8th at the Cloud Business Center in Paris

Jean Senellart, CEO of SYSTRAN France and CTO of the group opened the conference with a bold statement: the high quality of Neural Machine Translation has “commoditized” Machine Translation. As a commodity, NMT framework provides raw technology that needs to be refined, adapted and integrated for any industrial usage. After a look at the available NMT open source frameworks, including OpenNMT, cofounded and actively maintained by SYSTRAN, he made clear that streamlined training processes and data quality are the most crucial points to industrialize high quality neural machine translation.

Jean concluded his talk with the announcement of SYSTRAN marketplace, an open online platform where language experts have access to best of breed technology and framework to build, share, and sell language or domain models that can be accessed by industrial users. They will be able to select among hundreds of available models for any language pair and share feedback or evolution requests as per their specific needs.

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

Jean Senellart, CTO & CEO of SYSTRAN is explaining how SYSTRAN represent a GAFA alternative when they took benefit from Open Source, Multilingual AI and Artificial Neural Networks. 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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