  Discourse information, as postulated by popular discourse theories, such as
RST and PDTB, has been shown to improve an increasing number of downstream NLP
tasks, showing positive effects and synergies of discourse with important
real-world applications. While methods for incorporating discourse become more
and more sophisticated, the growing need for robust and general discourse
structures has not been sufficiently met by current discourse parsers, usually
trained on small scale datasets in a strictly limited number of domains. This
makes the prediction for arbitrary tasks noisy and unreliable. The overall
resulting lack of high-quality, high-quantity discourse trees poses a severe
limitation to further progress. In order the alleviate this shortcoming, we
propose a new strategy to generate tree structures in a task-agnostic,
unsupervised fashion by extending a latent tree induction framework with an
auto-encoding objective. The proposed approach can be applied to any
tree-structured objective, such as syntactic parsing, discourse parsing and
others. However, due to the especially difficult annotation process to generate
discourse trees, we initially develop a method to generate larger and more
diverse discourse treebanks. In this paper we are inferring general tree
structures of natural text in multiple domains, showing promising results on a
diverse set of tasks.
