A Probabilistic Generative Model of Linguistic Typology

ACL 2019

A Probabilistic Generative Model of Linguistic Typology

Jan 19, 2021
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Abstract: In the principles-and-parameters framework, the structural features of languages depend on parameters that may be toggled on or off, with a single parameter often dictating the status of multiple features. The implied covariance between features inspires our probabilisation of this line of linguistic inquiry---we develop a generative model of language based on exponential-family matrix factorisation. By modelling all languages and features within the same architecture, we show how structural similarities between languages can be exploited to predict typological features with near-perfect accuracy, outperforming several baselines on the task of predicting held-out features. Furthermore, we show that language embeddings pre-trained on monolingual text allow for generalisation to unobserved languages. This finding has clear practical and also theoretical implications: the results confirm what linguists have hypothesised, i.e.~that there are significant correlations between typological features and languages. Authors: Johannes Bjerva, Yova Kementchedjhieva, Ryan Cotterell, Isabelle Augenstein (University of Copenhagen, Johns Hopkins University, University of Cambridge)

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