  When a bilingual student learns to solve word problems in math, we expect the
student to be able to solve these problem in both languages the student is
fluent in,even if the math lessons were only taught in one language. However,
current representations in machine learning are language dependent. In this
work, we present a method to decouple the language from the problem by learning
language agnostic representations and therefore allowing training a model in
one language and applying to a different one in a zero shot fashion. We learn
these representations by taking inspiration from linguistics and formalizing
Universal Grammar as an optimization process (Chomsky, 2014; Montague, 1970).
We demonstrate the capabilities of these representations by showing that the
models trained on a single language using language agnostic representations
achieve very similar accuracies in other languages.
