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This paper investigates the ability of neural network architectures toeffectively learn diachronic phonological generalizations in a multilingualsetting. We employ models using three different types of language embedding(dense, sigmoid, and straight-through). We find that the Straight-Through modeloutperforms the other two in terms of accuracy, but the Sigmoid model'slanguage embeddings show the strongest agreement with the traditionalsubgrouping of the Slavic languages. We find that the Straight-Through modelhas learned coherent, semi-interpretable information about sound change, andoutline directions for future research.

In search of isoglosses: continuous and discrete language embeddings in Slavic historical phonology