Latent Geographical Factors for Analyzing the Evolution of Dialects in Contact

Yugo Murawaki


Abstract
Analyzing the evolution of dialects remains a challenging problem because contact phenomena hinder the application of the standard tree model. Previous statistical approaches to this problem resort to admixture analysis, where each dialect is seen as a mixture of latent ancestral populations. However, such ancestral populations are hardly interpretable in the context of the tree model. In this paper, we propose a probabilistic generative model that represents latent factors as geographical distributions. We argue that the proposed model has higher affinity with the tree model because a tree can alternatively be represented as a set of geographical distributions. Experiments involving synthetic and real data suggest that the proposed method is both quantitatively and qualitatively superior to the admixture model.
Anthology ID:
2020.emnlp-main.69
Volume:
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
Month:
November
Year:
2020
Address:
Online
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
959–976
Language:
URL:
https://aclanthology.org/2020.emnlp-main.69
DOI:
10.18653/v1/2020.emnlp-main.69
Bibkey:
Cite (ACL):
Yugo Murawaki. 2020. Latent Geographical Factors for Analyzing the Evolution of Dialects in Contact. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 959–976, Online. Association for Computational Linguistics.
Cite (Informal):
Latent Geographical Factors for Analyzing the Evolution of Dialects in Contact (Murawaki, EMNLP 2020)
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PDF:
https://aclanthology.org/2020.emnlp-main.69.pdf
Video:
 https://slideslive.com/38938993