A recent TAUS market study reports that 52 of 129 Language Service Providers (LSPs) are already using machine translation (MT) in their production environment and 86% of the remainder informed they plan to adopt MT within two years.
This technical guide is intended for anyone faced with preparing translation training data for statistical machine translation. It examines data preparation processes which are the catalysts that enable data and algorithms to work in unison. It explores how to define an organization's training data strategy to match overall system design, identifies potential data sources, introduces the challenges of merging multiple corpora to create large data sets and explores several methods to prepare these translation memories into SMT training data. Finally, it looks into the speech roots of SMT and introduces the concept of exception management as a context for preparing SMT training data.
This update on post-editing focuses on three evolving aspects of post-editing practice: the impact of statistical machine translation on the post-editing cycle, problems in specifying target quality for post-edited texts, and efforts to improve guidelines for human post-editing practices. Now that end users can in certain contexts choose between statistical and rule-based MT engines, the post-editing stage may become a selection criterion. Before undertaking full deployment, LSPs and end users will need to run quantifiable evaluation pilots to inventory post-editing tasks, identify recurrent modifications for automatic solutions, and base expectations about quality and pricing on objective data.
At TAUS we're forward-thinking. Which means we try to know our history. So explore with us the story of translation automation in the digital age. See timeline
The content value chain
A use case to share knowledge on enabling a global content value chain with an emphasis on the integration of automated translation technology. Challenges and opportunities of working with small languages and under resourced domains with a focus on Baltic Languages. Results of a study for the SAS Institute on the impact of Global English on machine translation readiness and post-editing productivity, and key learnings from the field about global content challenges that companies are trying to solve and new customer requirements they are working to meet.
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