  With a growing need for robust and general discourse structures in many
downstream tasks and real-world applications, the current lack of high-quality,
high-quantity discourse trees poses a severe shortcoming. In order the
alleviate this limitation, 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 such
method to complement task-specific models in generating much larger and more
diverse discourse treebanks.
