With neural machine translation evolving at a maddening tempo, it’s difficult even for larger companies to keep track of it. What methodologies and processes should we use in 2020 to assess and predict the quality of machine translation (MT) engines at scale across languages, content types and verticals? How do we combine automatic metrics, human evaluation, quality estimation methods and end-user input to give us the right granularity and reliability of data on our MT quality? How do we ensure all these processes are efficient and lead to solid, data-informed decisions? Join our panel discussion to find out.
Takeaways: Attendees will learn the current state-of-the-art of various MT quality evaluation methods; understand the strengths, weaknesses, costs and benefits of each method; and figure out how to make better decisions when selecting MT engines for production deployment.