When it comes to levels of linguistic accuracy and fidelity to corporate language and brand style, not all machine translations are created equal.
For example, Statistical Machine Translation engines that use non-customized data or lack augmented technologies struggle to accurately reflect an organization’s unique terminology or brand style in translation. This drop in quality is compounded by the “data dilution-effect” caused by the lack of sufficient quantities of a company’s source data needed to ‘teach’ its algorithms.
New technologies such as Safaba’s Language Optimization Technology™ which place a special focus on the choice of words, phrases, names and terminology used by an enterprise are proving effective in solving the “data-dilution effect” and brand fidelity issues. Additional Safaba technologies are also providing smarter ways to ensure that specific styling and formatting contained in corporate collateral – such as the particular use of bullets, caps and italicization – are faithfully reproduced across different languages.