Gathering Machine Translation-Related Productivity Data in a Complex Production Environment
Productivity from machine translation is measured in post-edit distance and time-spent-in-segment. When available at scale, this data guides machine translation strategies and feeds the customization of machine translation and quality estimation models. Human translators generate this data, but it is difficult to harvest when they operate in a multi-vendor supply chain and a heterogeneous technology landscape. This GALA webinar presented by Georg Kirchner (Dell Technologies) discusses approaches on how to get to your data in a sustainable manner.
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