  Bridging the performance gap between high- and low-resource languages has
been the focus of much previous work. Typological features from databases such
as the World Atlas of Language Structures (WALS) are a prime candidate for
this, as such data exists even for very low-resource languages. However,
previous work has only found minor benefits from using typological information.
Our hypothesis is that a model trained in a cross-lingual setting will pick up
on typological cues from the input data, thus overshadowing the utility of
explicitly using such features. We verify this hypothesis by blinding a model
to typological information, and investigate how cross-lingual sharing and
performance is impacted. Our model is based on a cross-lingual architecture in
which the latent weights governing the sharing between languages is learnt
during training. We show that (i) preventing this model from exploiting
typology severely reduces performance, while a control experiment reaffirms
that (ii) encouraging sharing according to typology somewhat improves
performance.
