SYSTRAN Demos Two New Integrations for Relativity at Ing3nious’ SoCal E-Discovery & Information Governance Retreat

This article was originally published on PR Newswire SYSTRAN Demos Two New Integrations for Relativity at Ing3nious’ SoCal E-Discovery & Information Governance Retreat

screen-shot-2016-11-16-at-1-54-07-pmSYSTRAN, a global leader in language translation technology, demonstrated two new integrations for SYSTRAN’s offering of Relativity at Ing3nious’ SoCal E-discovery & Information Governance Retreat, November 13-14.
SYSTRAN demonstrated two new integrations, aDiscovery and Anonymizer, for SYSTRAN’s offering of Relativity at the event. The aDiscovery feature will aid in audio discovery by transcribing audio files, detecting the source language and then translating the content. Anonymizer applies rigorous anonymization techniques to the full text and metadata of electronic documents within Relativity.

20161114_171147Anonymization can be used to mask identifying details in documents such as names, addresses, identification numbers, places, amounts and so forth when reading the anonymized documents; however, anonymized documents retain sufficient information for most relevancy reviews. Users also have the ability to “pseudononymize” selected names replacing pre-identified names with chosen pseudonyms on a mass basis to provide another option for privacy protection.

By 2020, Gartner predicts that 80 percent of litigation will involve multiple languages. The features are meant to assist with multi-language or cross-border e-discovery, giving legal teams a cost-effective method to translate files efficiently.

“These new integrations are going to change the way modern day legal teams handle multi-language files during e-discovery by maximizing productivity through automatic translation and Natural Language Processing (NLP),” says Ken Behan, SYSTRAN Vice President of Sales & Marketing. “These methods are far more efficient than manual translation and will save firms time and money during the e-discovery process.”

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In addition to the new integrations, SYSTRAN’s offering for Relativity automatically detects languages of files, translates documents that have multiple languages, and bulk translates using the Mass Action feature in Relativity. Organizations using Relativity are also able to support their billing process by accurately reflecting the workload completed.

To learn more about SYSTRAN’s offering of Relativity visit http://www.systransoft.com/translation-products/integrations/cmless-for-relativity.

About SYSTRAN

For over 48 years, SYSTRAN transformed the way global organizations such as Apple, Adobe, Daimler, HSBC, and Symantec meet the challenges of communicating globally via advanced machine-based translation technology. With the ability to facilitate communication in over 130 languages and 20 vertical domains, SYSTRAN enables instantaneous and automatic multilingual translations for texts, emails, chat, web pages, mobile apps, documents, user-generated content and more.

For more on SYSTRAN visit www.systrangroup.com

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This article was originally published on PR Newswire  SYSTRAN Demos Two New Integrations for Relativity at Ing3nious’ SoCal E-Discovery & Information Governance Retreat

Voyage en traduction automatique

« Mais enfin, papa, tu ne vas pas aller là ! La traduction automatique, ça marche pas ! c’est pourri …», ainsi s’exprime la fille de l’auteur, 19 ans. Trait générationnel, elle est spontanée, directe.
L’auteur se gratte le front, légèrement ébranlé. Dans la main, il tient sa proposition d’embauche chez le leader mondial de la « machine translation ».

« Mais pourquoi dis-tu ça ? » Continue reading

SYSTRAN to Present on Neural Machine Translation at Association for Machine Translation in America Conference

This article was originally published on PR Newswire SYSTRAN to Present on Neural Machine Translation at Association for Machine Translation in America Conference

SYSTRAN, a global leader in language translation technology, presented at the Association for Machine Translation in America (AMTA) conference in Austin this month. The talk, titled "Building Renewable Language Assets in Government Domains," included insights from the company's latest efforts in Neural Machine Translation.

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The presentation, given by Beth Flaherty, SYSTRAN's Director of Government Solutions, and Joshua Johanson, a computational linguist with SYSTRAN, discussed the company's work in specific domains and languages of interest to the government.

"SYSTRAN welcomes this opportunity to share our accomplishments to date with the government community," says Flaherty. "We will also reveal some of our plans to integrate neural network technology into our offerings to further serve the public sector with faster, smarter machine translation."

As demand for multilingual content continues to increase, public-sector organizations struggle to produce content efficiently, reliably and cost-effectively. SYSTRAN's software driven by machine translation provides organizations the ability to automatically translate audio and text in more than 130 different language pairs. The software can also be leveraged to perform real-time translation on intranets and other tools. At any given moment, users can translate entire sites, blogs or document to find and understand foreign language information in real-time.

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The AMTA conference took place in Austin, TX from October 28 through November 1. SYSTRAN's presentation was scheduled for October 31 from 5 – 5:30 p.m. on the Government track. The company also exhibited at the Technology showcase on October 30 from 12:30 – 3:30 p.m.

To learn more about SYSTRAN and its machine translation technology, visit http://www.systrangroup.com/.

This article was originally published on PR Newswire SYSTRAN to Present on Neural Machine Translation at Association for Machine Translation in America Conference

We are SYSTRAN and we love languages…

Click here to read the french version

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We are SYSTRAN. We love languages, lots of languages. We are a human-sized company but we have linguists for almost all of the 140 language pairs we support.  That’s a big number, but don’t be misled- some of us are fluent in many languages. Nevertheless, we love languages and we don’t believe in the one-fits-all technology regarding language processing.

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How does Neural Machine Translation work?

The representation of meaning in Neural, Rule-Based and Phrase-Based Machine Translation

In this issue of step-by-step articles, we explain how neural machine translation (NMT) works and compare it with existing technologies: rule-based engines (RBMT) and phrase-based engines (PBMT, the most popular being Statistical Machine Translation – SMT).

The results obtained from Neural Machine Translation are amazing, in particular, the neural network’s paraphrasing. It almost seems as if the neural network really “understands” the sentence to translate. In this first article, we are interested in “meaning,” that which gives an idea of the type of semantic knowledge the neural networks use to translate.

Let us start with a glimpse of how the 3 technologies work, the different steps of each translation process and the resources that each technology uses to translate. Then we will take a look at a few examples and compare what each technology must do to translate them correctly.

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SYSTRAN Demos Two New Integrations for Relativity at Relativity Fest 2016

This article was originally published on PR Newswire SYSTRAN Demos Two New Integrations for Relativity at Relativity Fest 2016

20161010_105927SYSTRAN, a global leader in language translation technology, will demo two new integrations for SYSTRAN’s offering of Relativity at Relativity Fest 2016 this Sunday in Chicago.

SYSTRAN is attending Relativity Fest as a silver sponsor. The company plans to demo two new integrations, aDiscovery and Anonymizer, for SYSTRAN’s offering of Relativity at the event. The aDiscovery feature will aid in audio discovery by transcribing audio files, detecting the source language and then translating the content. Anonymizer applies rigorous anonymization techniques to the full text and meta data of electronic documents within Relativity.

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Systran is far along the NMT path

A Deep Dive into SYSTRAN’s Neural Machine Translation (NMT) Technology

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

Neural Machine Translation

[…] So, I recently had a conversation with Jean Senellart , Global CTO and SYSTRAN SAS Director General, to find out more about their new NMT technology. He was very forthcoming, and responded to all my questions with useful details, anecdotes and enthusiasm. The conversation only reinforced in my mind that “real MT system development” is something best left to experts, and not something that even large LSPs should dabble with. The reality and complexity of NMT development pushes the limits of MT even further away from the DIY mirage.

In the text below, I have put quotes around everything that I have gotten directly from SYSTRAN material or from Jean Senellart (JAS) to make it clear that I am not interpreting. I have done some minor editing to facilitate readability and “English flow” and added comments in italics within his quotes where this is done.

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Discover the first issue of Purely Neural Machine Translation insight

PNMT Insight Number 1Project “PNMT” for Purely Neural Machine Translation was this year’s flagship project for the researchers and developers at SYSTRAN.

SYSTRAN brings its expertise in several ways: contributing to research on neural models; applying its know-how in terminology to increase the potential of Neural Machine Translation; and industrializing technology to make it available to companies, organizations and individuals.
We will keep you posted each month and share best practices, research paper, customers insights, product news…

You can download the first issue here.

SYSTRAN announces the launch of its “Purely Neural MT” engine, a revolution for the machine translation market

The first engine to be based on neural models and deep learning, delivering unparalleled translation quality!

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Project “PNMT” (Purely Neural Machine Translation) was this year’s flagship project for the researchers and developers at SYSTRAN, the leading provider in machine translation and natural language processing, confirming its pioneer position for over 40 years.

SYSTRAN brings its expertise to the sector in several ways: contributing to research on neural models; applying its know-how in terminology to increase the potential of Neural Machine Translation; and industrializing technology to make it available to companies, organizations and individuals.

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