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
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.