Nevertheless, this straightforward approach also faces scalability challenges because it requires training an independent parser for each language, which results in over 1.8 billion parameters for ten languages.
we hypothesize that the gains/losses in accuracy for different languages stem from two competing effects: the multilingual model has access to more data, but there are now multiple objective functions competing over the same parameters.