As the industry matures, even small enterprises have formed a potential global client base which has increased the need for these companies to communicate across multiple languages and cultures. However, cross-context communication is difficult and expensive. It requires great supervision and attention to detail as context can be lost in translation due errors in translating or varying interpretations of accurately translated projects.
Inaccuracy in translation is more than just a financial hurdle. This can also cause a company its reputation, legal exposure and industrial disasters. This is why clear and effective communication among languages and cultures is becoming a priority as the accuracy of sharing information between multiple partners has led to localization.
Globalization and localization have both contributed immensely in the annual expense of translation at an enterprise. Many companies are reverting to artificial intelligence by utilizing machine translations in order to cut costs of translation. Artificial intelligence translation platforms such as Google Translate, Amazon Translate and Microsoft Translate have recently taken a leap forward in terms of accuracy because of the recent breakthrough amendments in neural machine translation algorithms and access to high volume of language data from the internet.
However, artificial intelligence translation platforms only represent a small fraction of the languages spoken across the globe. Hence, translating content into a language beyond the ones represented by the platform is near impossible to justify in terms of cost. Such barriers are about to be broken by merging scale efficiencies provided by cloud-based platforms and improved productivity provided by machine translations.
Along with the languages, the ability to translate and interpret domain-specific terminologies with precise definitions is equally important. If we take an example of the medical industry, the term “protocol” has a very specific definition, which refers to a set of standard actions taken to treat a condition. In comparison, “protocol” in a telecommunications industry refers to how the data must be amended when it is exchanged between two companies. In industries like medicine, more and more documents are being digitalized and there is an increasing global requirement for efficient and accurate translation for doctor-patient communication. Hence, the availability of expert linguists in developed and developing countries enables machine translations to fill a very important gap.
Such domain-specific context requires automatic intelligence to provide domain-specific language examples for the purpose of training. However, it is a very time consuming process to collect medical insurance and other language information in multiple language pairs at a time. This is why new automatic intelligence technologies such as transfer learning are encouraging as they allow one system to bootstrap another in the targeted situation.
Adding on to automatic intelligence, the growth of cloud-based machine translations is also promoting a drive down in the expense of translation for at an enterprise. Whereas both automatic intelligence and cloud-based platforms might come across as a potential threat to language service providers and human translations, in reality they create an enormous opportunity for companies with the foresight to pivot quickly into the changes in the translation industry.
Declining costs will expand the number of enterprises that can take into consideration the cosmopolitan translation services which they previously could not justify, resulting in high demand. Rather than canceling out the requirement for human translations with experience in specific languages and cultures, automatic intelligence will enable a huge improvement in human linguists’ capacity in terms of languages, efficiency and accuracy.
Companies willing to shift away from high-cost, hard-to-scale translation methods towards an enterprise-focused translation platform will be able to cater to high volume, offer value-added expertise which they can use as a competitive advantage and add new languages more efficiently.
This blog shares a massive transition in the translation industry which is driven mainly by machine translations and the shift from in-house human translation methods. It has identified some common assumptions held in regards to machine translations such as the challenges being faced by language service providers and partner vendors.
As discussed, the growth and improvements in machine translation platforms like Amazon, Google and Microsoft Translate have decreased the expense of translation to near-zero. These translations are viewed as “good enough” by many consumers and basic business-to-consumer applications. This is why many industry players perceive a threat that such industry trends will lower the value of language service providers and freelance translators, eliminating employment. On the contrary, the frequent demand for domain expertise and high level of accuracy merged together with automatic intelligence and the shift to a platform-based translation model appears to be promising.
Even small businesses possess a global reach hence, they must execute their marketing campaigns globally in multiple languages, which is a great expense. Automatic intelligence will augment rather than replace human translators at the high end of the industry, increasing the capacity of the provider in terms of volume while simultaneously meeting the specialized requirements of specific industries. The increased requirement of digitalizing business documents on a global scale will drive more demands from companies that previously were unable to justify quality translations and services, and will create new opportunities to provide native language services targeted to a small but swiftly growing emerging markets.