  This paper investigates the ability of neural network architectures to
effectively learn diachronic phonological generalizations in a multilingual
setting. We employ models using three different types of language embedding
(dense, sigmoid, and straight-through). We find that the Straight-Through model
outperforms the other two in terms of accuracy, but the Sigmoid model's
language embeddings show the strongest agreement with the traditional
subgrouping of the Slavic languages. We find that the Straight-Through model
has learned coherent, semi-interpretable information about sound change, and
outline directions for future research.
