10 Ways to Improve Your Translation Output

10 Ways to Improve Your Translation Output

As Globalization 4.0 rears its head and the convergence of Industry 4.0 and remote work become commonplace in the business ecosystem, translation is an increasingly important component of productivity, engagement, and communication.  

But how do you iron out the knots? You need to effectively communicate with team members, colleagues, and customers across physical and linguistic borders. Unfortunately, there’s a tiny road bump in the road— language. 

Translation engines allow you to seamlessly communicate across language barriers. But creating a well-oiled, hyper-engaging translation solution isn’t always easy. Obviously, the source of your engine is important. Modern Neural Machine Translation (NMT) uses intelligent neural networks to instantly contextualize, digest, and output translations in micro-seconds.  

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4 Ways SYSTRAN Provides Industry-Leading Turnaround Times

SYSTRAN provides industry-leading turnaround times

For many businesses, translation is a time-consuming, labor-intensive process. Human translators can take days to fully process, translate, and proof a few thousand words. Between sending jobs, communicating time frames and service prices, and receiving the actual translated document, businesses can spend several days waiting for a complete translation. 

SYSTRAN’s Neural Machine Translation (NMT) solution cuts that process down to seconds. Our OpenNMT-powered neural engine and hyper-scalable architecture can almost instantly process translation requests. For example, it can translate a double-spaced, one-page Word document in around one second. NMT frees up human translators from grunt work and allows them to tackle more impactful, growth-oriented business problems. 

Today, let’s discuss some of the features that allow us to provide those one-second, industry-leading turnaround times that facilitate nearly instant translations. 

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Endless Possibilities with OpenNMT

Endless Possibilities with OpenNMT

SYSTRAN has been in the machine translation space since 1968. We’ve launched numerous closed-source solutions, iterated countless projects, and put forward a significant amount of R&D into the statistical machine translation, deep learning speech recognition, and (of course) neural machine translation. But, in 2017, we decided to do something a little unprecedented.

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