Discriminative Methods for Transliteration

We present two discriminative methods for name transliteration. The methods correspond to local and global modeling approaches in modeling structured output spaces. Both methods do not require alignment of names in different languages – their features are computed directly from the names themselves. We perform an experimental evaluation of the methods for name transliteration from three languages (Arabic, Korean, and Russian) into English, and compare the methods experimentally to a state-of-theart joint probabilistic modeling approach. We find that the discriminative methods outperform probabilistic modeling, with the global discriminative modeling approach achieving the best performance in all languages.