As reported in the 2021 edition of the Nimdzi 100, interpreting has arguably been the sector within the language industry that was the most heavily affected by the COVID-19 pandemic — both positively and negatively..
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.
Evaluating and migrating between translation management systems (TMS) is a lot of work and there are always reasons not to do it. It might be the fear of moving away from a familiar TMS, even if it isn’t fit for purpose, the impact on other teams and external stakeholders, or the prospect of the time, technical work, and costs involved. The number of TMS solutions on the market can also make the decision far from simple and straightforward.
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.
Webinar: At our company, meetings and planning follow the WIG (Wildly Important Goals) methodology. Why do we love it so much, and why are business executives implementing this methodology across companies?
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.
What's going on in the world of language technology? Nimdzi has organized a series of panel discussions to cover some of our favorite topics in the space.
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.