Multilingual Gradient Word-Order Typology from Universal Dependencies
This provides more reliable typological data for NLP researchers, though it is incremental as it builds on existing databases like WALS and Grambank.
The authors tackled the problem of inconsistent categorical typological data by introducing a continuous-valued seed dataset for word-order typology, which better reflects language variability and can be adapted for broader features and languages.
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.