  Can we construct a neural model that is inductively biased towards learning
human languages? Motivated by this question, we aim at constructing an
informative prior over neural weights, in order to adapt quickly to held-out
languages in the task of character-level language modeling. We infer this
distribution from a sample of typologically diverse training languages via
Laplace approximation. The use of such a prior outperforms baseline models with
an uninformative prior (so-called "fine-tuning") in both zero-shot and few-shot
settings. This shows that the prior is imbued with universal phonological
knowledge. Moreover, we harness additional language-specific side information
as distant supervision for held-out languages. Specifically, we condition
language models on features from typological databases, by concatenating them
to hidden states or generating weights with hyper-networks. These features
appear beneficial in the few-shot setting, but not in the zero-shot setting.
Since the paucity of digital texts affects the majority of the world's
languages, we hope that these findings will help broaden the scope of
applications for language technology.
