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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NMT Tech Enables Diversity and Inclusion Efforts

NMT tech enables diversity and inclusion efforts

Diversity & Inclusion (D&I) have quickly become staples of HR playbooks, yet organizations still struggle with how to fully integrate the practice across both people culture and enablement tools (technology).

The data supporting D&I practice is clear. Sales teams with the highest levels of racial diversity see 15x greater sales. Companies with diverse employee pools (across age, race, gender, culture, religion, etc.) see 2.3x higher bottom-line revenue.

Studies have shown that diverse teams are 87 percent better at decision-making — a largely intangible asset. In addition to being a moral imperative, D&I is a legitimate competitive advantage with echoing consequences and rewards across the business hierarchy. Although we’ve seen a focus in D&I HR strategies around hiring and leadership growth, businesses often fail to invest in technology frameworks that perpetuate D&I strategy internally, and as a customer engagement proposition.

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Terminology & Neural Machine Translation: Our User Dictionary feature Explained!

Glossaries usually prove helpful to welcome a new colleague in your team, what if they were one of the best entry point to your domain for our models?

In various workplaces, a lot of knowledge is accumulated in lexicons, which uncover a wide variety of usages, from specifying specialized terms to introducing brand names and business concepts.

Based on more than 50 years of dedicated experience, our research team have presented at COLING 2020 the technique behind the User Dictionary feature, designed to polish machine translation and give it an appropriate flavor through words. This presentation has been recorded and is available here.

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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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SYSTRAN’s Role in Preventing Cross-Border Corruption

The FCPA or the Foreign Corrupt Practices Act of 1977 made it illegal for U.S. citizens to bribe foreign government officials in exchange for new business or business favors.

Since then, this legislation has become a cornerstone for business compliance programs. Organizations around the country engage in compliance training to ensure that their employees aren’t found to be acting unethically or illegally when procuring new business in foreign countries.

Legislations for business compliance programs
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Transform Your Customer Care Team Into Language Ninjas

As part of our webinar series, one of our latest broadcast discussed and demonstrated the unique and innovative Language I/O + SYSTRAN solution, created in collaboration with our partner company Language I/O.

Hosted by J. Obakhan from SYSTRAN and Heather Shoemaker, CEO of Language I/O, the webinar discussed the power of integrating machine language translation technology into the customer care workflow.

Transform Your Customer Care Team Into Language Ninjas
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Webinar Highlights: Get More From SPNS9

Our webinar “Get More From SPNS9” on May 15th, 2020 was a huge success. The webinar demonstrated 6 new exciting upgrades to the SYSTRAN Pure Neural Server 9.6’s, further scaling its technological capabilities. Thank you to those who joined us.

In this post, we have compiled the highlights from the presentation and answers to the questions we receive after.

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Specialized AI-Based Translation Technology 101

The minds behind SYSTRAN sit down for an interview regarding the complexities and the capacities of specialized neural machine translation engines.

Participants: Peter Zoldan, Senior Data Engineer -Software Engineer Linguistic Program, Svetlana Zyrianova, Linguistic Program, Petra Bayrami, Jr. Software Engineer – Linguistic Program, Natalia Segal, R&D Engineer.

How much data is required to create a specialized engine?

The more bilingual data, the better the quality. For broad domains such as news, millions of bilingual sentences will be required. However, if the domain is narrow, such as technical support documents for certain products, then even a small set of sentences of 50,000, noticeably improves the quality. 

The amount of data required will depend on how broad or narrow the domain is you are specializing the engine into.

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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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