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.
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Post-editing is key to successful MT deployment. This report looks at the background conditions and current practice of post-editing machine output in organizations such as the PAHO and companies such as Symantec, with a focus on emerging tools and quality metric issues. 
