We describe automatic conversion of the SynTagRus dependency treebank\nof Russian to the PROIEL format (with the ultimate purpose of obtaining a single-format\ndiachronic treebank spanning more than a thousand years), focusing\non analysis of shared arguments in verbal coordinations. Whether arguments\nare shared or private is not marked in the SynTagRus native format,\nbut the PROIEL format indicates sharing by means of secondary dependencies.\nIn order to recover missing information and insert secondary dependencies\ninto the converted SynTagRus, we create a simple guessing algorithm\nbased on four probabilistic features: how likely a given argument type\nis to be shared; how likely an argument in a given position is to be shared;\nhow likely a given verb is to have a given argument; how likely a given verb\nis to have a given argument frame. Boosted with a few deterministic rules and\ntrained on a small manually annotated sample (346 sentences), the guesser\nvery successfully inserts shared subjects (F-score 0.97), which results\nin excellent overall performance (F-score 0.92). Non-subject arguments are\nshared much more rarely, and for them the results are poorer (0.31 for objects;\n0.22 for obliques). We show, however, that there are strong reasons\nto believe that performance can be increased if a larger training sample\nis used and the guesser gets to see enough positive examples. Apart from\ndescribing a useful practical solution, the paper also provides quantitative\ndata about and offers non-trivial insights into Russian verbal coordination.