The Past, Present and Future of Machine Translation with Alex Zekakis of XTM
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The Past, Present and Future of Machine Translation with Alex Zekakis of XTM

With many organizations expanding their reach and going global, the role of translation is becoming increasingly important. Businesses need effective multilingual communication with their partners, employees and customers across cultural borders if they are to succeed in international markets.

However, the human-dependent profession of translation is facing a major shift as technological advancements bring about more efficient and reliable machine translation processes.

Globally Speaking Radio guest and Global Solutions Architect Manager at XTM International Alex Zekakis has spent the last 11 years with his feet firmly planted in the localization industry. Alex uses his technical, communications and project management experience to create solutions for clients seeking to localize their content.

The rise of machine translation

Machine translation is hardly a new phenomenon; the first public demonstration took place in 1954. Although this rudimentary system was limited to translating a mere 250 words, it was the spark that ignited countless decades of research into fully automated communication.

Fast-forward to today and machine translation is unavoidable. Even the once-unreliable Google Translate has advanced from providing awkward literal translations to offering accurate materials that help users worldwide.

“Whats unique about the translation industry is that it essentially follows other trends. We are heavily influenced by the evolution of other industries. And we follow those trends quite quickly,” Alex explains. For example, when organizations migrated to cloud computing, so did translation.

“Whenever you introduce technology, you introduce a machine,” Alex explains. “Even more so when it comes to machine translation.”

Many translation companies and global enterprises now make use of automated translation, and there are several reasons why.

The benefits of machine translation

The translation needs of organizations continue to rise exponentially, and that volume of data is simply too high for human translators to keep up with. The most crucial advantage of machine translation is the sheer efficiency with which it produces results.

“If a human can translate 2,000 words a day and you can accomplish 100,000 words a day with a machine translation engine, you would need an equivalent of 50 translators,” Alex remarks. “That’s something you wouldn’t be able to effectively find or even pay for.”

It’s the significant productivity improvement of automated translation that continues to attract organizations. It allows you to translate into many languages in real time, thus reducing both costs and time to market. Also, professional translators typically specialize in a certain area, whereas machine translation offers universality.

However, machine translation should be viewed as a long-term investment. While the immediate costs may be substantial, the return will be immense. Over time, the machine can be trained to improve its results and increase consistency across texts.

The fears of human translators

With the continued advances in automation, it is natural to question the repercussions for human translators and whether a machine will ever truly produce perfect results. Although machine translation has made major strides, it is still far from producing human-quality translations.

Alex expresses his view on the matter, stating: “The debate about machine versus human, in my opinion, is void. I don’t see humans being replaced by machines. I see machines contributing.”

Alex was faced with this very question during his time as a Technical Communications and Localization Lecturer at the University of Strasbourg. His students naturally questioned the employ-ability of their skill set. Alex explained that industries can never fully automate; it’s simply impossible.

“Fortunately, professions adapt. If you translated in a certain way, you would need to learn to translate in a different way. You would still contribute. Humans will always be a part of the process.”

Automated factories will still need foremen, driver-less transport will still need human support teams and machine translation programs will still need human linguists. Language is complex and ambiguous and there is always an exception to the rule. Add cultural nuances to the equation and you have a really challenging artificial intelligence task.

As such, translators are finding new roles as post-editors. With their invaluable input, machine-generated translation will continue to improve and become an even more viable and cost-effective option for businesses.

Alex describes that its not a question of choosing between either machine translation or human translation, but rather, a unification of the two will ultimately produce higher-quality results. Believing that you have to choose is grossly misguided.

“There’s an opportunity for strong collaboration between humans and machines,” Alex stresses.

The future: collaboration between man and machine

To effectively translate content for a global audience, there needs to be an integration between human contribution and machine translation. Companies need to understand that there are two sides of the same coin: with one offering context and empathy and the other delivering unmatched efficiency and volume.

“The first step is for people to be prepared for an industry that is ever-changing and to learn to be flexible. Processes are not defined in stone in this industry. It’s a relatively new industry and we still don’t know what the full potential is. Our best hope is to be adaptable.”

The impressive history and progression of machine translation strongly suggests that we will see significant innovative advancements in the coming decades. Whatever the future holds, automation can only develop as far as supporting human translator capabilities, not challenging or replacing them, and global businesses will stand to benefit from improved translation and localization workflows.

 

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