Sequential transduction tasks, such as grapheme-to-phoneme conversion and machine transliteration, are usually addressed by inducing models from sets of input-output pairs. Supplemental representations offer valuable additional information, but incorporating that information is not straightforward. We apply a unified reranking approach to both grapheme-to-phoneme conversion and machine transliteration demonstrating substantial accuracy improvements by utilizing heterogeneous transliterations and transcriptions of the input word. We describe several experiments that involve a variety of supplemental data and two state-of-the-art transduction systems, yielding error rate reductions ranging from 12% to 43%. We further apply our approach to system combination, with error rate reductions between 4% and 9%.