  Typological information has the potential to be beneficial in the development
of NLP models, particularly for low-resource languages. Unfortunately, current
large-scale typological databases, notably WALS and Grambank, are inconsistent
both with each other and with other sources of typological information, such as
linguistic grammars. Some of these inconsistencies stem from coding errors or
linguistic variation, but many of the disagreements are due to the discrete
categorical nature of these databases. We shed light on this issue by
systematically exploring disagreements across typological databases and
resources, and their uses in NLP, covering the past and present. We next
investigate the future of such work, offering an argument that a continuous
view of typological features is clearly beneficial, echoing recommendations
from linguistics. We propose that such a view of typology has significant
potential in the future, including in language modeling in low-resource
scenarios.

Computation and Language