The future of neural translation looks promising. More data and resources, invested in developing this technology, will allow neural machine translation to become increasingly effective and efficient. New techniques and approaches are constantly being developed that may improve the ability of neural machine translation to handle a more extensive range of language pairings.
The Future of Human Translation
As neural networks improve, people have wondered what role is left for translators.
There’s no concrete answer to this in the middle of so much change. Some suggest that translators should learn the technology. Others say they should work side by side with machines. But it’s really left up albania mobile database to the translator to decide.
Human translators can play a critical role in shaping machine translation. After all, they have already made a huge impact. At innovative language companies like Tomedes, Lionbridge, and memoQ, human translators are interacting with machine translators on a daily basis.
Hybrid Workflows: The Combination of Human and Machine Translation
Hybrid translation workflows combine the speed of machine translation and the expertise of human translation to achieve consistent and natural-sounding results. This system allows for the efficient translation of specialized texts. With the empathy and experience of human translators, machine translation post-editing makes content and translated texts more relatable to consumers.
Moving Forward
Without a doubt, machine translation is very different from human translation. It is by no means perfect, but human translators are adapting and using it for good. Researchers at MIT are uncovering lost languages, and Google Zero-Shot Translation has added 24 underserved languages, giving 300 million people the chance to connect in their native language.