  Generating the English transliteration of a name written in a foreign script
is an important and challenging step in multilingual knowledge acquisition and
information extraction. Existing approaches to transliteration generation
require a large (>5000) number of training examples. This difficulty contrasts
with transliteration discovery, a somewhat easier task that involves picking a
plausible transliteration from a given list. In this work, we present a
bootstrapping algorithm that uses constrained discovery to improve generation,
and can be used with as few as 500 training examples, which we show can be
sourced from annotators in a matter of hours. This opens the task to languages
for which large number of training examples are unavailable. We evaluate
transliteration generation performance itself, as well the improvement it
brings to cross-lingual candidate generation for entity linking, a typical
downstream task. We present a comprehensive evaluation of our approach on nine
languages, each written in a unique script.
