The role of language data is evolving rapidly. Thanks to recent breakthroughs in artificial intelligence and natural language processing, it has become a valuable asset not only for authoring and translation, but also for training language and translation models. These models, in turn, can be used to create and maintain linguistic assets and even to make them meet increasingly diverse quality requirements and needs in terms of adapting content for different target groups and communication settings.
In this presentation, we present case studies showcasing how artificial intelligence technologies can help us maintain and adapt language data, e.g. by allowing us to apply custom quality checks and terminological changes to existing content, switch between formal and informal tone of voice, or generate training data for machine translation customization.
Get insights on how to leverage the potential of AI for language data management.
Daniel Zielinski has a university degree in translation and is managing director of Loctimize GmbH in Saarbrücken (Germany). He works as a consultant for translation and localization technologies and as a trainer holding training certificates for all relevant translation tools with more than 10 years of experience. He designs and develops training courses for translators, project managers, localizers and terminologists, and has organized and delivered numerous training courses and workshops worldwide. His consulting services include process analysis and tool configuration assessment, translation platform implementation and roll-out.
I am a lecturer and researcher at the Faculty of Translation Studies, Linguistics and Cultural Studies at Johannes Gutenberg University Mainz, Germany, where I teach, among others, translation technology and terminology classes. Besides my research and teaching, I work as a process and technology consultant with the language logistics experts at Loctimize GmbH.