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



San Diego, CA - October 27, 2016 - (Newswire.com)

SYSTRAN, a global leader in language translation technology, is set to present at the Association for Machine Translation in America (AMTA) conference in Austin this month. The talk, titled “Building Renewable Language Assets in Government Domains,” includes insights from the company’s latest efforts in Neural Machine Translation.

The presentation, given by Beth Flaherty, SYSTRAN’s Director of Government Solutions, and Joshua Johanson, a computational linguist with SYSTRAN, will discuss 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.

The AMTA conference takes place in Austin, TX from October 28 through November 1. SYSTRAN’s presentation is scheduled for October 31 from 5 – 5:30 p.m. on the Government track. The company will also be exhibiting 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.systransoft.com/.


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