  Text discourse parsing weighs importantly in understanding information flow
and argumentative structure in natural language, making it beneficial for
downstream tasks. While previous work significantly improves the performance of
RST discourse parsing, they are not readily applicable to practical use cases:
(1) EDU segmentation is not integrated into most existing tree parsing
frameworks, thus it is not straightforward to apply such models on newly-coming
data. (2) Most parsers cannot be used in multilingual scenarios, because they
are developed only in English. (3) Parsers trained from single-domain treebanks
do not generalize well on out-of-domain inputs. In this work, we propose a
document-level multilingual RST discourse parsing framework, which conducts EDU
segmentation and discourse tree parsing jointly. Moreover, we propose a
cross-translation augmentation strategy to enable the framework to support
multilingual parsing and improve its domain generality. Experimental results
show that our model achieves state-of-the-art performance on document-level
multilingual RST parsing in all sub-tasks.
