  In contrast to the older writing system of the 19th century, modern Hawaiian
orthography employs characters for long vowels and glottal stops. These extra
characters account for about one-third of the phonemes in Hawaiian, so
including them makes a big difference to reading comprehension and
pronunciation. However, transliterating between older and newer texts is a
laborious task when performed manually. We introduce two related methods to
help solve this transliteration problem automatically, given that there were
not enough data to train an end-to-end deep learning model. One method is
implemented, end-to-end, using finite state transducers (FSTs). The other is a
hybrid deep learning approach which approximately composes an FST with a
recurrent neural network (RNN). We find that the hybrid approach outperforms
the end-to-end FST by partitioning the original problem into one part that can
be modelled by hand, using an FST, and into another part, which is easily
solved by an RNN trained on the available data.
