  Phonological reconstruction is one of the central problems in historical
linguistics where a proto-word of an ancestral language is determined from the
observed cognate words of daughter languages. Computational approaches to
historical linguistics attempt to automate the task by learning models on
available linguistic data. Several ideas and techniques drawn from
computational biology have been successfully applied in the area of
computational historical linguistics. Following these lines, we adapt MSA
Transformer, a protein language model, to the problem of automated phonological
reconstruction. MSA Transformer trains on multiple sequence alignments as input
and is, thus, apt for application on aligned cognate words. We, hence, name our
model as Cognate Transformer. We also apply the model on another associated
task, namely, cognate reflex prediction, where a reflex word in a daughter
language is predicted based on cognate words from other daughter languages. We
show that our model outperforms the existing models on both tasks, especially
when it is pre-trained on masked word prediction task.
