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Machine Translation (MT) has experienced a surge in popularity in recent years. However, achieving the right level of quality output can be challenging, even for the most expert MT engineers.
MT engines learn from carefully selected bilingual and monolingual training data, and engine quality is enhanced through the use of terminology, fine tuning and a series of pre and post processing steps. Since these practices have a significant effect on the results of an MT workflow, it’s important to map out each step and develop a clear training strategy before deploying an MT solution.
Joining KantanMT’s Founder and Chief Architect, Tony O’Dowd is Selçuk Özcan, Co-founder of Transistent Language Automation Services. Transistent helps companies invest and integrate new language automation procedures into translation workflows. It is also the first company to focus on MT and quality automation services in Turkey and the Middle East.
During this webinar, Selçuk will talk about Transistent’s experience using KantanMT.com to build and deploy high quality KantanMT engines.
During this webinar you will learn:
• About the potential uses of Machine Translation
• Importance of training data and how it impacts on MT quality
• Tips for Preparing Training Data for High Quality MT