  Discourse parsing is an essential upstream task in Natural Language
Processing with strong implications for many real-world applications. Despite
its widely recognized role, most recent discourse parsers (and consequently
downstream tasks) still rely on small-scale human-annotated discourse
treebanks, trying to infer general-purpose discourse structures from very
limited data in a few narrow domains. To overcome this dire situation and allow
discourse parsers to be trained on larger, more diverse and domain-independent
datasets, we propose a framework to generate "silver-standard" discourse trees
from distant supervision on the auxiliary task of sentiment analysis.
