Multi-Dialectal Representation Learning of Sinitic Phonology
Zhibai Jia
Machine learning techniques have shown their competence for representing and
reasoning in symbolic systems such as language and phonology. In Sinitic
Historical Phonology, notable tasks that could benefit from machine learning
include the comparison of dialects and reconstruction of proto-languages
systems. Motivated by this, this paper provides an approach for obtaining
multi-dialectal representations of Sinitic syllables, by constructing a
knowledge graph from structured phonological data, then applying the BoxE
technique from knowledge base learning. We applied unsupervised clustering
techniques to the obtained representations to observe that the representations
capture phonemic contrast from the input dialects. Furthermore, we trained
classifiers to perform inference of unobserved Middle Chinese labels, showing
the representations' potential for indicating archaic, proto-language features.
The representations can be used for performing completion of fragmented Sinitic
phonological knowledge bases, estimating divergences between different
characters, or aiding the exploration and reconstruction of archaic features.