Historical linguists have identified regularities in the process of historic
sound change. The comparative method utilizes those regularities to reconstruct
proto-words based on observed forms in daughter languages. Can this process be
efficiently automated? We address the task of proto-word reconstruction, in
which the model is exposed to cognates in contemporary daughter languages, and
has to predict the proto word in the ancestor language. We provide a novel
dataset for this task, encompassing over 8,000 comparative entries, and show
that neural sequence models outperform conventional methods applied to this
task so far. Error analysis reveals variability in the ability of neural model
to capture different phonological changes, correlating with the complexity of
the changes. Analysis of learned embeddings reveals the models learn
phonologically meaningful generalizations, corresponding to well-attested
phonological shifts documented by historical linguistics.

本文研究历史语言学中的声音变化规律及该规律如何使用比较法进行原形词的重构，提出使用神经序列模型对比较法数据集中的超过 8000 个比较条目进行原形词的预测，结果表明神经序列模型优于现有的传统方法。语音学变化的复杂性存在一定的差异，但该模型仍学习了有意义的语音规律。