Choosing the right Machine Translation for a project is hard: the performance of every engine varies across domains and language pairs, and changes often. With domain-adaptive NMT it's even harder, as there's an additional difference in training corpus requirements and complex cost of ownership models.
In this talk, we describe the approach to address those issues. A large-scale reference-based scoring filters candidate MT engines and identifies a "decisive" set of segments for human LQA. A specific LQA procedure is tuned to choosing the best rather than just an acceptance. Also, we look at the learning gradient to estimate if the same engine will be suitable as more data become available.
About the speaker:
About the speaker: Konstantin Savenkov is CEO of Intento, Inc. After earning a PhD in 2008, he led research and development efforts for online content services, then worked as CTO at Zvooq and as a chief operating officer at Bookmate. In 2016, he contributed his experience in artificial intelligence (AI), tech and operations to launch Intento, Inc., where they build tools to discover, evaluate and use machine translation and other cognitive AI services.
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