In order to effectively incorporate the otherlanguage data, we apply SVM re-ranking in a manner that has previously been shown to provide significant improvement for grapheme-to-phoneme conversion (Bhargava and Kondrak, 2011) . This method is flexible enough to incorporate multiple languages; it employs features based on character alignments between potential outputs and existing transliterations from other languages, as well as scores of these alignments, which serve as a measure of similarity. We apply this approach on top of the same DIRECTL+ system as submitted last year <cite>(Jiampojamarn et al., 2010b)</cite> for English-to-Hindi machine transliteration. Compared to the base DI-RECTL+ performance, we are able to achieve significantly better results, with a relative performance increase of over 10%.