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In contrast to the older writing system of the 19th century, modern Hawaiianorthography employs characters for long vowels and glottal stops. These extracharacters account for about one-third of the phonemes in Hawaiian, soincluding them makes a big difference to reading comprehension andpronunciation. However, transliterating between older and newer texts is alaborious task when performed manually. We introduce two related methods tohelp solve this transliteration problem automatically, given that there werenot enough data to train an end-to-end deep learning model. One method isimplemented, end-to-end, using finite state transducers (FSTs). The other is ahybrid deep learning approach which approximately composes an FST with arecurrent neural network (RNN). We find that the hybrid approach outperformsthe end-to-end FST by partitioning the original problem into one part that canbe modelled by hand, using an FST, and into another part, which is easilysolved by an RNN trained on the available data.

Composing RNNs and FSTs for Small Data: Recovering Missing Characters in Old Hawaiian Text