  This paper describes a simple UCCA semantic graph parsing approach. The key
idea is to convert a UCCA semantic graph into a constituent tree, in which
extra labels are deliberately designed to mark remote edges and discontinuous
nodes for future recovery. In this way, we can make use of existing syntactic
parsing techniques. Based on the data statistics, we recover discontinuous
nodes directly according to the output labels of the constituent parser and use
a biaffine classification model to recover the more complex remote edges. The
classification model and the constituent parser are simultaneously trained
under the multi-task learning framework. We use the multilingual BERT as extra
features in the open tracks. Our system ranks the first place in the six
English/German closed/open tracks among seven participating systems. For the
seventh cross-lingual track, where there is little training data for French, we
propose a language embedding approach to utilize English and German training
data, and our result ranks the second place.
