Machine translation (MT) with post-editing (PE) is fast becoming a standard practice in our industry. This means that organizations need to be able to easily identify, qualify, train and evaluate post-editors’ performances.
Today, there are many methodologies in use, resulting in a lack of cohesive standards as organizations take various approaches for evaluating performance. Some use final output quality evaluation or post-editor productivity as a standalone metric. Others analyze quality data such as “over-edit” or “under-edit” of the post-editor’s effort or evaluate the percentage of MT suggestions used versus MT suggestions that are discarded in the final output.
An agreed set of best practices will help the industry fairly and efficiently select the most suitable talent for post-editing work and identify the training opportunities that will help translators and new players, such as crowdsourcing resources, become highly skilled and qualified post-editors.