  Expressive text encoders such as RNNs and Transformer Networks have been at
the center of NLP models in recent work. Most of the effort has focused on
sentence-level tasks, capturing the dependencies between words in a single
sentence, or pairs of sentences. However, certain tasks, such as argumentation
mining, require accounting for longer texts and complicated structural
dependencies between them. Deep structured prediction is a general framework to
combine the complementary strengths of expressive neural encoders and
structured inference for highly structured domains. Nevertheless, when the need
arises to go beyond sentences, most work relies on combining the output scores
of independently trained classifiers. One of the main reasons for this is that
constrained inference comes at a high computational cost. In this paper, we
explore the use of randomized inference to alleviate this concern and show that
we can efficiently leverage deep structured prediction and expressive neural
encoders for a set of tasks involving complicated argumentative structures.
