Online commerce has the ability to connect products and services with consumers on a global scale. Whether providing skills training, retail items, information-sharing, or more, businesses across all industries navigate borders in search of end-users.
As a result, engaging and communicating with consumers in their own language has become a significant component in 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) are leveraging their skills to help companies improve their marketing efforts, find new clients in untapped markets, and realize more return-on-investment for their products and services.
LSPs Expand With Technological Advances
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 software to make their services more efficient and precise.
Contrary to appearances, translation software is not replacing LSPS, but expanding their role and industry. According to the U.S. Bureau of Statistics, the translation industry is expected to grow by 42% from 2010 to 2020.
The most important reason for this growth is globalization. In the past ten years, the translation industry has grown exponentially. According to the US Bureau of Labor Statistics, the growth is expected to continue at an overall rate of 7% per year, with human translator jobs increasing by 18%.
Language Translation Software Expands LSPs Business Opportunities
For LSPs, using language translation services offers the ability to produce more work, faster and more accurately, in more languages, and at more affordable prices. The smart players are not fearing software tech; they are embracing it. Moreover, the progressive improvement of these translation applications has had demonstrable success for ROI.
First level translation software uses a form of AI known as machine learning. 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. Today, deep learning represents the most sophisticated version of translation software. 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 to their clients. They can develop customized dictionaries for specific industries and incorporate idiomatic analogs. Translation memory banks allow LSPs to create standardized translations of common texts like a contract boilerplate or business regulations.
Since the primary role of LSPs is to provide translations, companies do not need to make underutilized softwared investments. LSPs also are positioned to understand the services available and make best-use recommendations to their clients.
Artificial Intelligence Has Taken a Leap Forward
Recently, Google took neural networking in a new direction. Its flagship translation product, Google Translate, offers translation between 103 different languages. Each language translation needs its own neural network, a redundant process for each language. What’s more, if one of the languages is not English, the translation has to move from Language A to English, then English to Language B. This opened the door for multiple translation inaccuracies.
Google’s solution is the Google Neural Machine Translation (GNMT), a single neural network that uses deep learning techniques not only between two languages, but also to process ideas learned from all language translations in the system. So, if GNMT was learning something about English-to-Portuguese that it had not encountered in Spanish, it could incorporate those ideas into English-to-Spanish tasks.
The results were better than expected: Not only did GNMT provide smoother, more accurate translations in natural-sounding language, but also it improved itself by skipping the English-language midstep entirely.
This process, called “zero shot” translation, indicated that GNMT is able to translate one language into another by using concepts about language, rather than definitional algorithms based on English alone. In a sense, GNMT created its own language concept to execute the translation.
LSPs Remain Relevant
Despite all these innovations, LSPs remain an important part of global marketing. As the tools they use become more sophisticated, they can expand their services into vital areas like marketing, business consultancy, manufacturing, and more.
As a prime customer themselves for advances in artificial intelligence software, they will continue to lead the way on how these systems can grow and improve.