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