  This paper presents our experiments with applying TUPA to the CoNLL 2018 UD
shared task. TUPA is a general neural transition-based DAG parser, which we use
to present the first experiments on recovering enhanced dependencies as part of
the general parsing task. TUPA was designed for parsing UCCA, a
cross-linguistic semantic annotation scheme, exhibiting reentrancy,
discontinuity and non-terminal nodes. By converting UD trees and graphs to a
UCCA-like DAG format, we train TUPA almost without modification on the UD
parsing task. The generic nature of our approach lends itself naturally to
multitask learning. Our code is available at
https://github.com/CoNLL-UD-2018/HUJI
