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

• URL: http://arxiv.org/abs/2402.01582v1
• Date: Fri, 2 Feb 2024 17:20:16 GMT
• Title: Automating Sound Change Prediction for Phylogenetic Inference: A Tukanoan Case Study
• Authors: Kalvin Chang, Nathaniel R. Robinson, Anna Cai, Ting Chen, Annie Zhang, David R. Mortensen
• Abstract summary: We train a neural network on sound change data to predict intermediate sound change steps between historical protoforms and their modern descendants. 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.
• Score: 12.78027959820939
• License: http://creativecommons.org/licenses/by/4.0/
• 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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