  Neural networks are capable of translating between languages -- in some cases
even between two languages where there is little or no access to parallel
translations, in what is known as Unsupervised Machine Translation (UMT). Given
this progress, it is intriguing to ask whether machine learning tools can
ultimately enable understanding animal communication, particularly that of
highly intelligent animals. We propose a theoretical framework for analyzing
UMT when no parallel translations are available and when it cannot be assumed
that the source and target corpora address related subject domains or posses
similar linguistic structure. We exemplify this theory with two stylized models
of language, for which our framework provides bounds on necessary sample
complexity; the bounds are formally proven and experimentally verified on
synthetic data. These bounds show that the error rates are inversely related to
the language complexity and amount of common ground. This suggests that
unsupervised translation of animal communication may be feasible if the
communication system is sufficiently complex.
