  Discourse structure is integral to understanding a text and is helpful in
many NLP tasks. Learning latent representations of discourse is an attractive
alternative to acquiring expensive labeled discourse data. Liu and Lapata
(2018) propose a structured attention mechanism for text classification that
derives a tree over a text, akin to an RST discourse tree. We examine this
model in detail, and evaluate on additional discourse-relevant tasks and
datasets, in order to assess whether the structured attention improves
performance on the end task and whether it captures a text's discourse
structure. We find the learned latent trees have little to no structure and
instead focus on lexical cues; even after obtaining more structured trees with
proposed model modifications, the trees are still far from capturing discourse
structure when compared to discourse dependency trees from an existing
discourse parser. Finally, ablation studies show the structured attention
provides little benefit, sometimes even hurting performance.
