Latent Geographical Factors for Analyzing the Evolution of Dialects in Contact (2020)

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.

## introduction how languages have changed over time is a question that has attracted a lasting interest. observing the present state of a language, we typically want to trace it back to the past. historical-comparative linguists have done this by systematically comparing related languages and representing them as a tree. the success of this approach led to the establishment of language families such as indo-european and austronesian @xcite . the recent adoption of computer-intensive statistical methods offer additional insights @xcite @xcite . when it comes to dialects, or closely-related languages, foot_0 the situation is very different. when we draw an isogloss, or the geographical boundary of a linguistic feature, and collect such isoglosses, it often happens that they conflict with each other @xcite . conflicting isoglosses violate the assumption of the tree model, where after a branching event, two daughter languages evolve without any contact. nevertheless, some historical-comparative linguists have recently tried to apply the tree model to dialects in intense contact, with the assumption that at least some portion of observed data reflects treelike vertical inheritance while the rest may result from horizontal contact @xcite @xcite . while these efforts have been met with some success, it seems to us that the inherent difficulty in disentangling the two modes of transmission remains unresolved. this motivates us to turn to statistical modeling because computers are better at handling uncertainty than humans. as a statistical model to analyze the evolution of dialects, admixture analysis has received attention in recent years @xcite @xcite . it assumes that each dialect is generated from a mixture of latent ancestral populations. unfortunately, such ancestral populations can hardly be used for humans to infer a tree. covering all the dialects with varying degrees of membership, an ancestral population only offers vague information about subgrouping if it does. in this paper, we propose a probabilistic generative model that represents latent factors as geographical distributions (figure 1 ). the geographical distribution of an observed feature is assumed to be stochastically generated from a weighted combination of the latent geographical factors. these factors are much more easier to interpret in the context of the tree model than latent ancestral population of the admixture model because an internal node of a tree can be geographically represented as the set of its descendant leaves. some latent factors may be associated with vertical inheritance while others reflect horizontal transfer. we revisit this point in section 5.3. ## conclusions in this paper, we proposed a bayesian generative model to analyze dialectal variation. with this model, we successfully induced a large number of latent factors from a set of noisy surface features. each latent factor is associated with an intuitively appealing geographical interpretation. in the experiments, we used synthetic data and fijian lexical data. future directions include the incorporation of phonological and morphosyntactic features, application to other languages, and most importantly, a model extension to infer temporal ordering. figure a.1 visualizes the dataset. a high degree of contact is evident from the neighbornet analysis @xcite .