This is very cool. One thing I wonder about though is whether small companies will be able to compete with large ones like Google in ML in the future. One reason Google's translator is better is because they have way more data. In the past they digitized tons of books so they have an excellent dataset that has been translated by professional, human translators. This data collection is effectively cross-subsidized by Google's primary business: advertising.<p>Since most competitors to Google offerings aren't going to have a hugely profitable core business with which to fund all the data collection and normalization that goes into building a high quality ML system, the future for poorly capitalized competitors to compete seems bleak to me. This seems to support some of the growing rumblings about enforcing antitrust laws against the large tech companies.<p>Edit: better, not bigger.
The transformer paper was quite influential in machine translation space. This resource [0] posted here a while back is a good place to learn and get a better idea how it works.<p>[0]: <a href="http://nlp.seas.harvard.edu/2018/04/03/attention.html" rel="nofollow">http://nlp.seas.harvard.edu/2018/04/03/attention.html</a>
Machine translation has made some pretty impressive progress over the last decade. Unfortunately no methods will ever cover the very last mile as languages don't have perfect 1 to 1 mappings. Though it is amusing watching the machines try.
The grammar correction in Google Translate is a little too good. I was trying to create some broken Russian phrases to send a Russian friend, but I’d put in weird or bad English as an input and get very good Russian as an output!
I find that google translator does very well when the text to be translated has no spelling errors and is grammatically correct. Add any errors, and it falls to pieces, even though a human reader doesn't have any issues with it.