Significant breakthroughs in machine translation only seem possible if human translators are taken into the loop. While automatic
\nevaluation and scoring mechanisms such as BLEU have enabled the fast development of systems, it is not clear how systems can meet
\nreal-world (quality) requirements in industrial translation scenarios today. The TARAX\u02ddU project paves the way for wide usage of hybrid
\nmachine translation outputs through various feedback loops in system development. In a consortium of research and industry partners,
\nthe project integrates human translators into the development process for rating and post-editing of machine translation outputs thus
\ncollecting feedback for possible improvements.<\/p>\n","protected":false},"excerpt":{"rendered":"
Significant breakthroughs in machine translation only seem possible if human translators are taken into the loop. While automatic evaluation and scoring mechanisms such as BLEU have enabled the fast development of systems, it is not clear how systems can meet real-world (quality) requirements in industrial translation scenarios today. 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