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Are European Union parallel multilingual texts - regulations, directives, especially the debates of European parliament - ideal for machine learning of machine translation, e.g. with neural networks? My guess is that they are ideal, but I have not seen they to be used in actual research papers. If not, then - why they can not be ideal?
I am specifically interested in the grammar induction as the by-product of the machine translation learning a la https://arxiv.org/abs/1805.10850 .
Europarl is a classic corpus for research papers, used at the main conference - WMT - and by some of the top people in the field.
It would be useful for training a translation system specifically for European parliament domain.
But Europarl, like any domain-specific corpus, is not ideal for training a production-strength open-domain machine translation system.
How many times do the top queries like how r u or ai eu se te pego in the corpus? To say nothing of laham taz-ziemel or gradient descent.
