  We show how to predict the basic word-order facts of a novel language given
only a corpus of part-of-speech (POS) sequences. We predict how often direct
objects follow their verbs, how often adjectives follow their nouns, and in
general the directionalities of all dependency relations. Such typological
properties could be helpful in grammar induction. While such a problem is
usually regarded as unsupervised learning, our innovation is to treat it as
supervised learning, using a large collection of realistic synthetic languages
as training data. The supervised learner must identify surface features of a
language's POS sequence (hand-engineered or neural features) that correlate
with the language's deeper structure (latent trees). In the experiment, we
show: 1) Given a small set of real languages, it helps to add many synthetic
languages to the training data. 2) Our system is robust even when the POS
sequences include noise. 3) Our system on this task outperforms a grammar
induction baseline by a large margin.
