Deep Learning NLP Techniques for Advanced Translation
Automatic translation has been available for years thanks to services like Google translate, but results have never been completely satisfying. Thanks to the recent progress made on deep learning, it is now possible to reach a more advanced level of translation.
Deep learning NLP models make translation very fluent. Even for advanced technical topics, it's hard to detect that translation was performed by a machine. Now that translation is more reliable than ever, it creates tons of new possibilities.
Why Use Automatic Translation?
Potential applications for automatic translation are countless, but let's show 2 examples.
Many customers cannot or don't want to speak English. Instead of just ignoring them and potentially losing them, why not automatically translate all the discussions?
Targeting customers based in multiple countries with English content only is not optimal. Most customers prefer to read about you in their own language. It is also a great way to improve SEO. Leveraging machine translation is a great way to easily get more customers.
import nlpcloud client = nlpcloud.Client("m2m100-1-2b", "", gpu=False) client.translation("John Doe has been working for Microsoft in Seattle since 1999.", source="en", target="af")
Control whether you want to use the model on a GPU. Machine learning models run much faster on GPUs.