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This paper explores the possibility of improving the performance ofspecialized parsers for pre-modern Slavic by training them on data fromdifferent 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 datascarcity and attempt the training of a variety-agnostic parser. Previousexperiments on early Slavic dependency parsing are discussed, particularly withregard to their ability to tackle different orthographic, regional andstylistic features. A generic pre-modern Slavic parser and two specializedparsers -- one for East Slavic and one for South Slavic -- are trained usingjPTDP (Nguyen & Verspoor 2018), a neural network model for joint part-of-speech(POS) tagging and dependency parsing which had shown promising results on anumber 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 attachementscore (LAS)) and Old East Slavic (OES) (85.7\% UAS and 80.16\% LAS).

Exploiting Cross-Dialectal Gold Syntax for Low-Resource Historical Languages: Towards a Generic Parser for Pre-Modern Slavic