Engaging and communicating with consumers in their own language has become a significant component in business communications and marketing translation.
In the same way that HR translation can help your business reach multilingual employees and improve internal organization, producing content in multiple languages can expand your brand’s client reach externally.
Language service providers' (LSPs), through translation and localization services, can companies improve their marketing efforts, find new clients in untapped markets, and realize more return-on-investment for their products and services.
Gone are the days when an LSP simply took a document in one language, handed it to someone fluent in another, and produced an expert translation for its client.
Today’s LSPs are involved in marketing strategies, staff training, cultural advising, and utilizing advancements in translation technology to make their services more efficient and precise. Contrary to appearances, translation tools are not replacing LSPS, but expanding their role and industry.
The translation services market size was valued at USD 39.37 Billion in 2020 and is projected to reach USD 46.22 Billion by 2028, growing at a CAGR of 2.07% from 2021 to 2028. And the number of jobs in translation and interpretation services is predicted to increase 29% from 2017 to 2024. The most important reason for this growth is globalization.
For LSPs, using technologies like machine translation, terminology management and connectors for language translation services offers the ability to produce more work, faster and more accurately, in more languages, and with a better return on investment for clients.
The smart players are not fearing technology; they are embracing it. Moreover, the progressive improvement of these translation applications has had demonstrable success for ROI.
First level translation technology uses a form of artificial intelligence (AI) known as machine learning (ML). Initially, standard dictionary-style definitions of words were entered, text was scanned, and the system produced a word-for-word translation.
This linear approach did not take into account the subtleties of word choice, syntax and grammar, or cultural issues. A human translator was thus needed to review the material for refinement. Over time, algorithms were employed that used statistical analysis to produce better, but still imperfect, translations.
The next level of translation programming came in the form of deep learning (DL). Today, deep learning represents the most sophisticated version of translation technology. Rather than taking a linear, machine language approach, the algorithms of deep learning mimic the way the human brain processes information.
Layers of analysis have different nodes where decisions are made, and the results are then passed along to nodes in the next layer. The nodes attempt to synthesize understanding from multiple inputs. The more layers, which is where the idea of “deep” comes from, the more sophisticated the translation.
With each successful translation, deep learning machines incorporate what they learned and apply it to the next translation. In theory, the translations will improve with each iteration.
Both machine translation and deep learning have enabled LSPs to provide more sophisticated services. And as LSPs' primary role is still to provide translation and localization services, they can also advise on the best technology mix for clients to make sure no underutilized technology investments are made.
As a prime customer for advances in artificial intelligence software, LSPs will continue to lead the way on how these technologies can further develop and integrate with existing systems.
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