@inproceedings{chang-etal-2023-automating-sound,
title = "Automating Sound Change Prediction for Phylogenetic Inference: A Tukanoan Case Study",
author = "Chang, Kalvin and
Robinson, Nathaniel and
Cai, Anna and
Chen, Ting and
Zhang, Annie and
Mortensen, David",
editor = "Tahmasebi, Nina and
Montariol, Syrielle and
Dubossarsky, Haim and
Kutuzov, Andrey and
Hengchen, Simon and
Alfter, David and
Periti, Francesco and
Cassotti, Pierluigi",
booktitle = "Proceedings of the 4th Workshop on Computational Approaches to Historical Language Change",
month = dec,
year = "2023",
address = "Singapore",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.lchange-1.14",
doi = "10.18653/v1/2023.lchange-1.14",
pages = "129--142",
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 less effective than sound laws from expert annotation. Our code is publicly available.",
}
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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 less effective than sound laws from expert annotation. Our code is publicly available.</abstract>
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%0 Conference Proceedings
%T Automating Sound Change Prediction for Phylogenetic Inference: A Tukanoan Case Study
%A Chang, Kalvin
%A Robinson, Nathaniel
%A Cai, Anna
%A Chen, Ting
%A Zhang, Annie
%A Mortensen, David
%Y Tahmasebi, Nina
%Y Montariol, Syrielle
%Y Dubossarsky, Haim
%Y Kutuzov, Andrey
%Y Hengchen, Simon
%Y Alfter, David
%Y Periti, Francesco
%Y Cassotti, Pierluigi
%S Proceedings of the 4th Workshop on Computational Approaches to Historical Language Change
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F chang-etal-2023-automating-sound
%X 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 less effective than sound laws from expert annotation. Our code is publicly available.
%R 10.18653/v1/2023.lchange-1.14
%U https://aclanthology.org/2023.lchange-1.14
%U https://doi.org/10.18653/v1/2023.lchange-1.14
%P 129-142
Markdown (Informal)
[Automating Sound Change Prediction for Phylogenetic Inference: A Tukanoan Case Study](https://aclanthology.org/2023.lchange-1.14) (Chang et al., LChange 2023)
ACL