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SYSTRAN, 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.

Anonymization 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.

"SYSTRAN's offering for Relativity enables organizations to maximize productivity during e-discovery
by tapping into automatic translation and Natural Language Processing (NLP) instead of the time-
intensive alternatives," says Ken Behan, SYSTRAN vice president of sales and marketing. "These new
integrations are going to change the way modern day legal teams handle multi-language files during
e-discovery."

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.