  Transliteration converts words in a source language (e.g., English) into
words in a target language (e.g., Vietnamese). This conversion considers the
phonological structure of the target language, as the transliterated output
needs to be pronounceable in the target language. For example, a word in
Vietnamese that begins with a consonant cluster is phonologically invalid and
thus would be an incorrect output of a transliteration system. Most statistical
transliteration approaches, albeit being widely adopted, do not explicitly
model the target language's phonology, which often results in invalid outputs.
The problem is compounded by the limited linguistic resources available when
converting foreign words to transliterated words in the target language. In
this work, we present a phonology-augmented statistical framework suitable for
transliteration, especially when only limited linguistic resources are
available. We propose the concept of pseudo-syllables as structures
representing how segments of a foreign word are organized according to the
syllables of the target language's phonology. We performed transliteration
experiments on Vietnamese and Cantonese. We show that the proposed framework
outperforms the statistical baseline by up to 44.68% relative, when there are
limited training examples (587 entries).
