Disentangling dialects: a neural approach to Indo-Aryan historical phonology and subgrouping

Abstract

This paper seeks to uncover patterns of sound change across Indo-Aryan languages using an LSTM encoder-decoder architecture. We augment our models with embeddings representing language ID, part of speech, and other features such as word embeddings. We find that a highly augmented model shows highest accuracy in predicting held-out forms, and investigate other properties of interest learned by our models’ representations. We outline extensions to this architecture that can better capture variation in Indo-Aryan sound change.