In the previous episode of Globally Speaking, we delved into the topic of Neural Machine Translation (NMT)—one of the most talked about issues in the localization and translation industry today.
During the last segment, we defined and discussed neural MT in general, and how it differs from rule-based machine translation and phrase-based statistical machine translation.
In the current episode, the second in our three-part series on neural MT, we dive deeper into what advancements in neural MT will mean for our industry. And why it’s a significant paradigm shift—more of a revolution, in fact, than an evolution of pre-existing technologies.
But that doesn’t mean neural MT is a panacea for every linguistic problem. Nor is it the preferred solution over traditional MT approaches in every instance.
What are some of the biggest barriers to using neural MT in professional environments today? What are its major challenges? When will it be ready to hit the market in a big way—if ever—or will it only be used to integrate specific components, rather than becoming a primary MT solution?
- Alon Lavie, Senior Manager at Amazon and head of the Amazon Machine Translation Research and Development Group
- Diane O’Reilly, head of Global Sales and Marketing for Iconic Translation Machines
- Marco Trombetti, tech entrepreneur in the language industry and co-founder, Pi Campus
- Mike Dillinger, former President of the Association for Machine Translation for the Americas, and Manager, Taxonomy Team and Machine Translation at LinkedIn
Learn more about what neural MT means, not only for professional translators, but also for everyone impacted by the language industry.
Globally Speaking Radio, sponsored by RWS Moravia and Nimdzi.