  While we live in an increasingly interconnected world, different places still
exhibit strikingly different cultures and many events we experience in our
every day life pertain only to the specific place we live in. As a result,
people often talk about different things in different parts of the world. In
this work we study the effect of local context in machine translation and
postulate that particularly in low resource settings this causes the domains of
the source and target language to greatly mismatch, as the two languages are
often spoken in further apart regions of the world with more distinctive
cultural traits and unrelated local events. We first formalize the concept of
source-target domain mismatch, propose a metric to quantify it, and provide
empirical evidence corroborating our intuition that organic text produced by
people speaking very different languages exhibits the most dramatic
differences. We conclude with an empirical study of how source-target domain
mismatch affects training of machine translation systems for low resource
language pairs. In particular, we find that it severely affects
back-translation, but the degradation can be alleviated by combining
back-translation with self-training and by increasing the relative amount of
target side monolingual data.
