Over decades, the translation industry has been proposing the use of “similar” translations in CAT tools, allowing human translators to visualize one or several matches retrieved from a translation memory (TM) when translating new documents. A translation memory (TM) is a database that stores segments of text and their corresponding translations. Segments can be sentences, paragraphs or sentence-like units (headings, titles, elements in a list, etc.). While the ideal situation is to find perfect matches, these are not always available. In such a case, translators resort to matches showing sufficient content in common with the document to be translated. These partial matches are then slightly “repaired” to achieve correct translations.
The use of TM matches relies on the idea that repairing a given TM match requires less effort than producing a translation from scratch, thus leading to higher productivity and consistency rates. The following figure illustrates human translation via repairing a TM match. The English sentence How long does the flight last? is translated into French considering the TM match How long does a flu last? —Quelle est la durée d’une grippe?
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.
SYSTRAN, the pioneer of neural machine translation solutions and technology, recently launched SYSTRAN Model Studio to help language experts build powerful and robust domain-specific translation models. By converging SYSTRAN’s world-class neural machine translation technologies with a global network of talented language and translation experts, SYSTRAN Model Studio unlocks higher translation quality and in-domain specialization for niche industries and businesses and allows LSPs to profit further from their data.
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.
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.
Your website is your business’s portal to the planet, right? Well, that depends. Can visitors access your content in their native language? If not, then you’re not really targeting the world, just one small portion of it.
An Opinion on Why Organizations Need Multilingual Chat For Their CRM
Yes, ANOTHER article on CSAT and Net Promoter scoring as the preeminent models for gauging customer satisfaction and loyalty. Despite the enormous body of knowledge on these topics, customer experience leaders who are in tune with their audiences must continuously ask: What might my overall customer satisfaction measurement models be missing?
We would suggest that it’s something called Customer Effort Scoring.
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.
“This is really a new start of taking our technology and our partner technology and rethinking how software internationalization and localization is performed,” says Adam Asnes, Founder & CEO of Lingoport.
In March, Ken Behan, VP of Sales and Marketing at SYSTRAN joined Adam Asnes, the Founder & CEO of Lingoport, Olivier Libouban, Product Development Head at Lingoport, and Yuka Kurihara, Globalization Consultant, to discuss the undiscovered potential of internationalization and localization they recently unfurled through SYSTRAN and Lingoport’s partnership.
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.