Whether you are in sales, marketing, localization, or engineering — every successful product and business begins first by knowing your audience. And as you take your business global, this principle becomes even more crystalized and important than ever before. The world’s most successful global companies speak their customers’ languages, no matter where in the world they may be. That’s why it’s really no coincidence that nearly three-quarters of Fortune 500 companies (and counting) have made active investments in their localization efforts. As we’ve explored time and time before, language is one of the key pieces that not only helps a business connect with its consumers in international markets — but is also a direct signal of the active time and energy they are investing to better understand and engage with their global customers.
While there are a range of translation methods available for companies of all shapes, sizes, industries, and budgets, one in particular has risen in popularity of late: Machine Translation (MT). Industry professionals have taken a keen interest in this new method because it is the next wave of the localization tech evolution. Recent reports revealed how a global company like eBay was not only able to boost their sales by 10% by leveraging Machine Translation, but do so by essentially reducing the distance between countries by 26% all through localization. While researchers and big tech companies are still working hard to better increase its efficiency and expand the number of use cases it can be applied to, it is clear that Machine Translation will indeed be the future of localization.
Of course, as with any technological advancement there comes the questions of what role humans will play in this workflow once the tech has been fully evolved. For Machine Translation specifically, the trends and new updates in the localization space this year have revealed that the most accurate and efficient combination is actually a strong balance of Machine Translation with human translation efforts (or “human-in-the-loop,” as we like to call it).
As we keep moving forward unlocking the potential of Machine Translation and enter the final quarter of another year of great localization growth, this next post of our #MachineTranslation series takes a look back at the key insights on the power of Machine Translation with human-in-the-loop.
Human Translators Are More Productive Editing MT Content
New experiments year took a closer look at the productivity of translators, both within a MT-driven workflow and then a more standard localization workflow. Their findings revealed that while MT alone was quite fast as predicted, it was not fully accurate (also as predicted). Because of this, integrating human translators into the process actually made the entire localization effort exponentially more productive for the translators, and made more accurate from the localization method side of things. After these findings, localization researchers remain optimistic and state that “ “understanding how human post-editors work could open the door to the design of better interfaces, smarter allocation of human translators to content, and automatic post-editing.”
Deep Learning is Impacting MT & Transforming Human Translation
Recent MT research has been closely connected with the deep and machine learning spaces. While this in and of itself is a fairly straightforward concept, new strides have been made in recent years that leads industry researchers and experts to believe that human translation and the overall concept of a translation agency as we know it today could be completely transformed due to these new technological strides. What a fast-paced time we lived in, where MT-driven innovations are now bringing to life translation feats previously not known possible, and also cracking the Machine Translation puzzle (that once confounded researchers of the 1950s) wide open.
Machine Translation and Human Translation Make the Perfect Pairing
Among other common misconceptions of Machine Translation, one major myth is the fear that MT means no more human translators are needed on the job. Following the previous two sections, it is clear that as long as MT is here to stay, so is the need for human translators to make sure everything is accurate and culturally attuned. While Machine Translation really has opened localization doors in terms of helping companies translate content at scale with great efficiency, accuracy will remain a big necessity as long as the audiences were are localizing for are human. Language is fluid, it evolves and it flows, with different jargons and phrases that may change from one era to another. Because of this, while we can do our best to train machines to learn how to be most culturally relevant, there is no replacing the human translation touch to make sure localized content is translated and delivered in a way that will best resonate with the intended audiences.
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