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.
Language technology providers are scrambling to jump on the speech-to-text bandwagon which means users can view machine-generated live subtitles (translated from the original) as well as multilingual captions (monolingual transcripts available for different languages)of speeches in their preferred language.
This report is the first in an ongoing Business Confidence Study series that Nimdzi is kicking off to keep a pulse on the industry.
Cologne-based DeepL has announced the beta launch of DeepL Write, an AI-powered authoring tool intended to improve texts by fixing errors and making suggestions for word replacements while keeping an eye on style, grammar and formatting.
Machine interpreting (MI) is a hot topic right now as technology providers boast their latest advances in this field. It is likely that the advent of MI will revolutionize the interpreting industry as we know it, similarly to how machine translation (MT) upended the translation industry and ushered in a new era for all stakeholders involved. So, now is the perfect opportunity to take a deep dive into the world of machine interpreting.