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.

本研究介绍了两种自动解决夏威夷文老字体和新字体之间转译问题的方法，分别采用有限状态转录器和深度学习方法。研究发现，一个混合模型可以将问题划分为由FST模型处理的部分和由RNN模型训练解决的部分，最终超越了使用有限状态转录器的端到端模型的表现。

将RNN和FST组合应用于小型数据：恢复古夏威夷文献中的缺失字符