  Discourse parsing could not yet take full advantage of the neural NLP
revolution, mostly due to the lack of annotated datasets. We propose a novel
approach that uses distant supervision on an auxiliary task (sentiment
classification), to generate abundant data for RST-style discourse structure
prediction. Our approach combines a neural variant of multiple-instance
learning, using document-level supervision, with an optimal CKY-style tree
generation algorithm. In a series of experiments, we train a discourse parser
(for only structure prediction) on our automatically generated dataset and
compare it with parsers trained on human-annotated corpora (news domain RST-DT
and Instructional domain). Results indicate that while our parser does not yet
match the performance of a parser trained and tested on the same dataset
(intra-domain), it does perform remarkably well on the much more difficult and
arguably more useful task of inter-domain discourse structure prediction, where
the parser is trained on one domain and tested/applied on another one.
