dynamic quality framework
It's now possible for DQF users on SDL Trados Studio to send metadata-only and still be able to generate detailed quality evaluation reports on the DQF Dashboard.
Data are the key to process improvements, quality control, and automation, and they can be collected in a GDPR-compliant way. Learn how TAUS DQF treats your personal data.
What is quality assurance? How can you do translation quality assurance in the most efficient and data-oriented way?
What is TAUS DQF? What does DQF stand for? Everything you want to know about the Dynamic Quality Framework.
The DQF plugin for productivity and quality tracking in SDL Trados Studio is the most popular DQF integration. Here are the six reasons why.
In a PhD study whose goal was to set up a method for evaluating the quality of machine translation output and for judging its readiness for use in production, DQM-MQM turns out to be an invaluable addition to automatic MT quality metrics.
The DQF data accumulated and processed over the years has reached the point where it can be used to inform strategic business decisions. The stats from that data will be shared quarterly as a Business Intelligence Bulletin to inform the translation and lozalization industry.
Close to 200 million words have been processed by DQF in the past year. Thousands of translators and reviewers have DQF plugged into their work environment. Now with BI Bulletins, Confidence Score, MY DQF Toolbox and DQF Reviewer, it is bringing the industry one step closer to ficing the operational gap.
Since July 2018, the TAUS Quality Dashboard features the first set of trend reports on translation productivity and correction density. In this post, we list the benefits that DQF (Dynamic Quality Framework) API users can get out of the TAUS trend reports.
Use Case: Dell-EMC produces high volumes of translated content – more than 200 million words in more than 34 languages and dialects annually using four different vendors. Since July of 2017, 78 million words have run through the DQF integration, with plans to have all volume flowing through the integration within the next year.
With machine translation and productivity challenges, LSPs need to become data owners and risk brokers, similarly to insurance companies. Managing your business means managing your data. If you collect and analyze productivity data, you can manage the risks of low productivity and profitability.
In this blog post we will highlight some of the standards and metrics used in translation quality management.
Error Typology is a venerable evaluation method for content quality. In this blog post Kirill Soloviev describes how to use it.
The TAUS efficiency score replaces traditional productivity measurement as it can be applied to every form of translation.