  Transliterations play an important role in multilingual entity reference
resolution, because proper names increasingly travel between languages in news
and social media. Previous work associated with machine translation targets
transliteration only single between language pairs, focuses on specific classes
of entities (such as cities and celebrities) and relies on manual curation,
which limits the expression power of transliteration in multilingual
environment.
  By contrast, we present an unsupervised transliteration model covering 69
major languages that can generate good transliterations for arbitrary strings
between any language pair. Our model yields top-(1, 20, 100) averages of
(32.85%, 60.44%, 83.20%) in matching gold standard transliteration compared to
results from a recently-published system of (26.71%, 50.27%, 72.79%). We also
show the quality of our model in detecting true and false friends from
Wikipedia high frequency lexicons. Our method indicates a strong signal of
pronunciation similarity and boosts the probability of finding true friends in
68 out of 69 languages.
