  Text discourse parsing plays an important role in understanding information
flow and argumentative structure in natural language. Previous research under
the Rhetorical Structure Theory (RST) has mostly focused on inducing and
evaluating models from the English treebank. However, the parsing tasks for
other languages such as German, Dutch, and Portuguese are still challenging due
to the shortage of annotated data. In this work, we investigate two approaches
to establish a neural, cross-lingual discourse parser via: (1) utilizing
multilingual vector representations; and (2) adopting segment-level translation
of the source content. Experiment results show that both methods are effective
even with limited training data, and achieve state-of-the-art performance on
cross-lingual, document-level discourse parsing on all sub-tasks.
