  Many character-level tasks can be framed as sequence-to-sequence
transduction, where the target is a word from a natural language. We show that
leveraging target language models derived from unannotated target corpora,
combined with a precise alignment of the training data, yields state-of-the art
results on cognate projection, inflection generation, and phoneme-to-grapheme
conversion.
