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.
Remote interpreting solutions have been both in development and in use for a long time now. However, prior to the COVID-19 pandemic, uptake was slow. The onset of the pandemic changed this drastically, and, ever since, it seems that the growth, innovation, and investment in this field has been unstoppable. Once considered an afterthought or sub-par alternative to onsite services, remote interpreting has stepped out of the shadows to become the key to continuity of business and care in many industries.
Introduction The language services industry is undergoing a profound transformation with the emergence of cutting-edge technologies such as ChatGPT and large language models (LLMs). These powerful language generation models have captivated the attention of businesses and language professionals alike, offering exciting possibilities for translation, localization, and content creation. In this article, we will explore the […]
The year is 2023. Six years after the big neural MT push of 2017, it seems appropriate to say that machine translation (MT) has finally found its way in the localization industry. Most MT providers are producing reasonably acceptable baseline quality and MT solutions have never been more accessible. As a result, MT is becoming a reality in many organizations. What’s more, MT technology has reached a certain level of maturity in terms of customization and training.
Developing your own approach to using generative AI models such as ChatGPT — one that is both practical AND ethically sound — is perhaps the best way of proving naysayers wrong and ensuring that you get the most out of this promising piece of technology. Perhaps surprisingly, the first key to success with generative AI models is to learn how to talk to them.