So you jumped on the automatization bandwagon and now want to run automatic Quality Assurance (QA) on translations.
The general approach to run automatic QA is to use:
QA tools normally work with bilingual (source and target) files. They help you to find:
To avoid false positives that these QA tools may generate, it’s highly recommended to create special configurations that would be used to automatically reduce the noise. For example, for Verifika it would be a quality profile per project/language.
Though some tools still provide QA reports in Excel sheets, the better way is to utilize the solutions which offer automatic updates of the segments being QAed – right from the report. Otherwise, it takes a lot of time to switch between working environments and implement all the needed changes into the working files.
Increasingly, companies around the world (and not just the media and entertainment giants) are producing video content to connect with their audiences and offer even more added value to their customers.
Data-driven decision-making is the process of making organizational and strategic decisions based on actual objective data instead of on intuition or observations. Today, every company in every industry aims to minimize the likelihood of business decisions going awry. Data is usually the answer.
When choosing a translation provider there are many factors to consider, such as the level of quality, turnaround times, human vs machine translation, pricing, specialization, and much, much more. But there is more at stake than just getting the best product on the market. One of the most important factors when choosing a language service provider (LSP) to partner with is to make sure their data security measures are air tight.