3 months ago
May 3, 2021
TAUS and SYSTRAN Form Alliance to Address Domain-specific Machine Translation
TAUS and SYSTRAN announce that they have formed a strategic alliance. Both companies operate in the market of automated translation, but each of them brings a unique perspective and solution. TAUS specializes in language data that is needed to train and customize the translation engines, whereas SYSTRAN specializes in machine translation technology for professional users. By combining the expertise of the two, translation users will access a unique solution on the market.
This strategic alliance with TAUS gives SYSTRAN access to large volumes of domain-specific language data, the TAUS Matching Data Service and Data Library and the specialized TAUS corpus cleaning, crawling, clustering and curation services. TAUS and SYSTRAN also agreed to integrate their marketplaces, providing an end-to-end solution for their customers.
“Generic translation available online offers a quality that fits well in a general context. However, it does not meet the needs for a ready-to-use accurate translation in every language pair or in specific domains,” says Jean Senellart, CEO of SYSTRAN. “We want to bring tailored translation quality to everyone and this requires high-quality, clean data matching in use case. By joining forces with TAUS, we trust that we will be able to strengthen our machine translation offer with a larger scope of languages and domains while ensuring optimal quality.”
TAUS and SYSTRAN have both launched marketplaces with the intention to give the growing number of machine translation users faster and easier access to the technology and services. SYSTRAN’s Marketplace is a catalog of domain-specific translation models that gives language experts in the global translation industry more autonomy in training and selling their own customized translation engines. The TAUS Data Marketplace addresses the phase prior to the model training: the collection of clean, high-quality, domain-specific language data. It is a central resource for all operators in the global publishing and translation industries to clean, curate, cluster, anonymize and possibly also monetize their language data and legacy translation memories.
“Despite the latest technological advances in the field of ML, finding high-quality, domain-specific language data in as many language pairs as possible remains to be a challenge to achieve optimal results”, says Jaap van der Meer, Director of TAUS. “With TAUS Data Services, we generate tailor-fit datasets to be used in the training of machine translation models. Together with SYSTRAN, we are able to address both ends of the MT training problem: strong training models and high-quality language data meeting project requirements.”
With more than 50 years of experience in translation technologies, SYSTRAN has pioneered the greatest innovations in the field, including the first web-based translation portals and the first neural translation engines combining artificial intelligence and neural networks for businesses and public organizations.
SYSTRAN provides business users with advanced and secure automated translation solutions in various areas such as: global collaboration, multilingual content production, customer support, electronic investigation, Big Data analysis, e-commerce, etc.
SYSTRAN offers a tailor-made solution with an open and scalable architecture that enables seamless integration into existing third-party applications and IT infrastructures.
For more information, visit https://www.systransoft.com/
TAUS was founded in 2005 as a think tank with a mission to automate and innovate translation. Ideas transformed into actions. TAUS became the language data network offering the largest industry-shared repository of data, deep know-how in language engineering and a network of Human Language Project workers around the globe.
Our mission today is to empower global enterprises and their service and technology providers with data solutions that help them to communicate in all languages, faster, better and more efficiently.
For more information, visit https://www.taus.net/