How to select the best machine translation system and overview of the different machine translation offerings on the market.

Data is crucial when training a machine translation system, as it serves as the foundation for the system to learn and generate accurate translations. The quality, quantity, and diversity of the training data directly impact the performance of the machine translation model. In this session we will cover approaches to identifying, preparing and refining data for MT Engine training.

The quality of machine translation is one of the essential factors in determining whether the use of this technology will be effective and useful in the localisation process. In this session we will cover a range of different quality assessment methods and the tools needed to implement them.

Machine translation programmes often do not progress as quickly and effectively as they should because translators/editors are not properly trained or put-up resistance. In this session we will discuss training guidelines for post-editors, as well as essential communication aspects with the linguists who will be carrying out the post-editing task.

This session will look in detail at which aspects to consider when implementing machine translation in production; the importance of starting with a pilot programme and how its introduction affects different aspects of production (quality, resource management, sales, etc.).
Also, aspects to consider when buying and selling machine translation and post-editing.

LSPs tend to focus on the many concerns around AI, but the goal of this session is to explore the applications and opportunities LSPs can benefit from going forward.

All participants will receive ongoing support from Laura for the duration of the course, as well as a Post Masterclass Consultancy session.

Each participating company will be supported through a dedicated consulting session with Laura.