Multilingual Gradient Word-Order Typology from Universal Dependencies
Emi Baylor Esther Ploeger Johannes Bjerva
While information from the field of linguistic typology has the potential to
improve performance on NLP tasks, reliable typological data is a prerequisite.
Existing typological databases, including WALS and Grambank, suffer from
inconsistencies primarily caused by their categorical format. Furthermore,
typological categorisations by definition differ significantly from the
continuous nature of phenomena, as found in natural language corpora. In this
paper, we introduce a new seed dataset made up of continuous-valued data,
rather than categorical data, that can better reflect the variability of
language. While this initial dataset focuses on word-order typology, we also
present the methodology used to create the dataset, which can be easily adapted
to generate data for a broader set of features and languages.