Automating Sound Change Prediction for Phylogenetic Inference: A Tukanoan Case Study
<|reference_start|>Automating Sound Change Prediction for Phylogenetic Inference: A Tukanoan Case Study: 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.<|reference_end|>
@article{chang2024automating,
    title={Automating Sound Change Prediction for Phylogenetic Inference: A
  Tukanoan Case Study},
    author={Kalvin Chang, Nathaniel R. Robinson, Anna Cai, Ting Chen, Annie Zhang,
  David R. Mortensen},
    journal={arXiv preprint arXiv:2402.01582},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.01582},
    primaryClass={cs.CL}
}