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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2402.01582 |
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| _version_ | 1866914664449835008 |
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| author | Chang, Kalvin Robinson, Nathaniel R. Cai, Anna Chen, Ting Zhang, Annie Mortensen, David R. |
| author_facet | Chang, Kalvin Robinson, Nathaniel R. Cai, Anna Chen, Ting Zhang, Annie Mortensen, David R. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_01582 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Automating Sound Change Prediction for Phylogenetic Inference: A Tukanoan Case Study Chang, Kalvin Robinson, Nathaniel R. Cai, Anna Chen, Ting Zhang, Annie Mortensen, David R. Computation and Language 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. |
| title | Automating Sound Change Prediction for Phylogenetic Inference: A Tukanoan Case Study |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2402.01582 |