Tackling Low-Resource Machine Translation with Participation, Data and Scale

This talk will feature three aspects that have recently changed the landscape 
for low-resource machine translation: First, we'll discover the role of 
participatory approaches that place native speakers at the core of the 
development, with the Masakhane community as an example for African languages.
Second, we'll dive deep into quality issues of multilingual public datasets that
affect low-resource languages disproportionately. And last, we'll learn about 
the tricks behind Google Translate's most recent success in launching NMT for 
languages without any parallel data.