  Proto-form reconstruction has been a painstaking process for linguists.
Recently, computational models such as RNN and Transformers have been proposed
to automate this process. We take three different approaches to improve upon
previous methods, including data augmentation to recover missing reflexes,
adding a VAE structure to the Transformer model for proto-to-language
prediction, and using a neural machine translation model for the reconstruction
task. We find that with the additional VAE structure, the Transformer model has
a better performance on the WikiHan dataset, and the data augmentation step
stabilizes the training.
