Extensive research in Natural Language Processing, technological advancements and computational linguistics training have resulted in the outstanding development of sophisticated translation technology, producing grammatically coherent translated output and showing promising results in a variety of language pairs.
To manage increasing translation volumes and the need for fast turnaround times, we now support a cutting-edge high-quality machine translation solution using Neural Machine Translation (NMT) technology. Thanks to continuous progress in quality and output and seamless integration with our translation environment, Machine Translation is the ideal tool for high-volume, organised, structured content in certain language pairs and documents where timelines and budgets are a concern.
We have a number of innovative technology solutions that make localisation seamless and cost-effective
It is the translation of content without human involvement. Pioneered in the 1950's machine translation has significantly evolved to include new models that include cutting-edge technologies such as artificial intelligence and machine learning. There are 3 types of machine translation as well as hybrid systems, a mix of them.
The rule-based system functions with a combination of language and grammar rules plus dictionaries for common words. Specific glossaries or term bases are created to focus on certain industries or disciplines or for a certain client. Rules-based systems typically deliver consistent translations with consistent terminology when trained with a specific termbase.
Statistical systems do not have implemented language rules. Instead they "learn" to translate by analysing large amounts of data for each language pair. They can be trained for specific industries or disciplines using additional terminology relevant to the sector needed. Typically statistical systems deliver more fluent-sounding but less consistent translations.
Neural Machine Translation (NMT) is a new approach that makes machines learn to translate through one large neural network. The approach has become increasingly popular amongst researchers and developers, as trained NMT systems have begun to show better translation quality in many language pairs compared to the statistical approach.