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With a growing need for robust and general discourse structures in manydownstream tasks and real-world applications, the current lack of high-quality,high-quantity discourse trees poses a severe shortcoming. In order thealleviate this limitation, we propose a new strategy to generate treestructures in a task-agnostic, unsupervised fashion by extending a latent treeinduction framework with an auto-encoding objective. The proposed approach canbe applied to any tree-structured objective, such as syntactic parsing,discourse parsing and others. However, due to the especially difficultannotation process to generate discourse trees, we initially develop suchmethod to complement task-specific models in generating much larger and morediverse discourse treebanks.

Unsupervised Inference of Data-Driven Discourse Structures using a Tree Auto-Encoder