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arXiv · 2402.01582

Automating Sound Change Prediction for Phylogenetic Inference: A Tukanoan Case Study

Kalvin Chang·Nathaniel R. Robinson·Anna Cai·Ting Chen·Annie Zhang·David R. Mortensen
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Abstract

We describe a set of new methods to partially automate linguistic phylogenetic inference given (1) cognate sets with their respective protoforms and sound laws, (2) a mapping from phones to their articulatory features and (3) a typological database of sound changes. We train a neural network on these sound change data to weight articulatory distances between phones and predict intermediate sound change steps between historical protoforms and their modern descendants, replacing a linguistic expert in part of a parsimony-based phylogenetic inference algorithm. In our best experiments on Tukanoan languages, this method produces trees with a Generalized Quartet Distance of 0.12 from a tree that used expert annotations, a significant improvement over other semi-automated baselines. We discuss potential benefits and drawbacks to our neural approach and parsimony-based tree prediction. We also experiment with a minimal generalization learner for automatic sound law induction, finding it comparably effective to sound laws from expert annotation. Our code is publicly available at https://github.com/cmu-llab/aiscp.

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Kalvin Chang, Nathaniel R. Robinson, Anna Cai, Ting Chen, Annie Zhang, David R. Mortensen. 2024-02-02. Automating Sound Change Prediction for Phylogenetic Inference: A Tukanoan Case Study. https://arxiv.org/abs/2402.01582

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