  Informal romanization is an idiosyncratic process used by humans in informal
digital communication to encode non-Latin script languages into Latin character
sets found on common keyboards. Character substitution choices differ between
users but have been shown to be governed by the same main principles observed
across a variety of languages---namely, character pairs are often associated
through phonetic or visual similarity. We propose a noisy-channel WFST cascade
model for deciphering the original non-Latin script from observed romanized
text in an unsupervised fashion. We train our model directly on romanized data
from two languages: Egyptian Arabic and Russian. We demonstrate that adding
inductive bias through phonetic and visual priors on character mappings
substantially improves the model's performance on both languages, yielding
results much closer to the supervised skyline. Finally, we introduce a new
dataset of romanized Russian, collected from a Russian social network website
and partially annotated for our experiments.
