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.
On June 10, 2020, we published our Nimdzi Language Technology Atlas, the comprehensive resource that maps hundreds of language technology solutions from all around the world. Two months later, after receiving and reviewing feedback from more than three dozen companies who submitted requests to add new tools or change their categorization, we released an update to the infographic on August 27.
We all know that human input is still invaluable when reviewing localized content. But with ever-improving localization technologies, where does a manual approach to auditing matter most?
Do you remember the last time when people were NOT talking about machine translation (MT)? We don't. Wherever you go, there’s someone talking about MT. With few exceptions, it seems like the only major disruptors in our industry over the past few decades have been breakthroughs in language technology.
Some machine translation providers are holding out hope for MT systems that adapt to document context. Could this development eliminate the need for custom MT engines? Will context-enabled MT help MT achieve human parity? Will we still need to customize a few years from now? Let’s discuss further.
Before the rise of Translation Management Systems (TMS), there were CAT tools. A CAT (Computer-Assisted or Computer-Aided Translation) tool is software that allows a user to work with bilingual text – the source and the target (translation).