  This paper explores the possibility of improving the performance of
specialized parsers for pre-modern Slavic by training them on data from
different related varieties. Because of their linguistic heterogeneity,
pre-modern Slavic varieties are treated as low-resource historical languages,
whereby cross-dialectal treebank data may be exploited to overcome data
scarcity and attempt the training of a variety-agnostic parser. Previous
experiments on early Slavic dependency parsing are discussed, particularly with
regard to their ability to tackle different orthographic, regional and
stylistic features. A generic pre-modern Slavic parser and two specialized
parsers -- one for East Slavic and one for South Slavic -- are trained using
jPTDP (Nguyen & Verspoor 2018), a neural network model for joint part-of-speech
(POS) tagging and dependency parsing which had shown promising results on a
number of Universal Dependency (UD) treebanks, including Old Church Slavonic
(OCS). With these experiments, a new state of the art is obtained for both OCS
(83.79\% unlabelled attachment score (UAS) and 78.43\% labelled attachement
score (LAS)) and Old East Slavic (OES) (85.7\% UAS and 80.16\% LAS).
