![]() However, with the advancements in the AI world, it becomes more accessible for all machine translation companies to train new language models or improve existing ones. In the early days, only huge companies could have both the computational power and human resources to do it. The training of one translation model is a complex process that requires a lot of expertise, data and time. Several prominent machine translation companies now support over 100 most frequently spoken languages. In this case, companies control both the costs and the safety of their translation.Īccording to a famous reference publication Ethnologue, there are over 7,000 languages worldwide, and you’ll be hard pressed to find a machine translation service that accommodates them all. The only safe way out is to use on-premise solutions, like Lingvanex On-Premise Translation Server, where all of a company's data is stored and processed within its own infrastructure, working offline, without any need to use other services. As businesses can not simply create their own machine translation tools and the services of a human translator can be very costly and time-consuming, they have no other choice but to trust somebody else, which can cause a problem. If the machine translation software you chose sends your data to a cloud service, it can lead to potential safety breaches, as your data gets processed by third parties. Sensitive data leakage is a valid concern when using machine translation services, especially in an era where data breaches frequently make headlines. As for people who decide to use machine translation tools by themselves to improve their business, they can always ask a company that provides MT solutions to train their translation models to suit specific linguistic needs. Now, professionals can tackle more tasks than ever before in shorter periods of time. ![]() The market of translation has been growing exponentially since the 1990s with the arrival of statistical machine translation and improvements in computing power. Rather than replacing human translators, machine translation has been aiding them by handling straightforward, mundane translation tasks, allowing professionals to focus on complex texts that require deep cultural and contextual understanding. However, completely the opposite happened. Many doomsayers have predicted that the arrival of machine translators will replace human translators for good. If you need a completely accurate translation, the quickest and cheapest thing to do is first to translate the text using machine translation software and then ask a proofreader to eliminate any uncertainties in the text. If your client or partner speaks a different language, but you are still able to understand most of what is written or spoken, you’ve got a greater chance at solving any problem easily. People look for value and familiarity, and even with imperfect machine translation they are more likely to visit your site, make a purchase, read your blog or write a review. The gap between human and machine translation might seem significant, but here’s the deal: various surveys have shown that people prefer to interact with information in their own language, even if it’s poor quality. Linguistic experts claim that every year it improves by 3-7%. In 2020 it was estimated that neural translation engines were capable of instant text translation with 60-90% accuracy. However, most businesses shouldn’t worry on that score, as today’s neural machine translation is developing rapidly. Considering the innate complexity of languages, multiple grammar rules and contextual nuances, mistakes are bound to happen occasionally.
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