Key performance indicators (KPIs) for localization have been a topic of endless debate for a long time. Localization teams the world over have not only struggled to create such metrics but to measure and track them consistently.
2020 was a big year for language technology. One lesser-known application for AI, dubbed the “digital shield,” is also set to become a more prominent part of the fight against misleading and manipulative content.
The solutions to address the impact of a global pandemic on recording studios requiring the presence of actors in recording booths were twofold: software and hardware.
Using high-precision and high-performance QA tools not only helps improve the quality of a text but can also speed up the turnaround time of localization projects, and can in turn lead to cost savings.
With a pandemic raging across the globe neither subtitling nor in-person recording was a viable option. Studios set their sights on building a remote recording framework. However, as the need for remote recording became more apparent, so too did the challenges that implementing it would pose.
It’s often said that “the show must go on,” but can the show really keep going when production is hamstrung by a global pandemic and the show’s crew are required to maintain physical distance from one another? COVID-19 and the resulting social distancing requirements have disrupted practically every industry on the planet. As a result, […]
In December 2020, Nimdzi was given an opportunity to test a brand new product — Spotlight. It is developed by Intento to support machine translation (MT) curation, enabling quick analysis of the MT training results. This product is intended mainly for those who train custom MT models and thus regularly face the task of evaluating MT quality.
New challenges brought about by doing business in our digital world demand new solutions. Some constants still remain, however, without which a text and the quality of its translation would be less than satisfactory. One good example of such a constant is terminology and terminology management. Terminology management includes a number of different aspects, but […]
We recently introduced you to the two- (or five-) second rule, which is essentially the reaction or decision-making time a linguist should spend judging whether to post-edit a segment of machine translation (MT) output or to retranslate it. This rule of thumb aims to help increase the linguist’s productivity when working with MT.
If you’re a driver, you’ve probably heard of the two-second rule. Staying at least two seconds behind any vehicle is considered a rule of thumb for drivers wanting to maintain a safe following distance at any speed. The two seconds don’t represent safe stopping distance but rather safe reaction time.