  Small class-imbalanced datasets, common in many high-level semantic tasks
like discourse analysis, present a particular challenge to current
deep-learning architectures. In this work, we perform an extensive analysis on
sentence-level classification approaches for the News Discourse dataset, one of
the largest high-level semantic discourse datasets recently published. We show
that a multitask approach can improve 7% Micro F1-score upon current
state-of-the-art benchmarks, due in part to label corrections across tasks,
which improve performance for underrepresented classes. We also offer a
comparative review of additional techniques proposed to address resource-poor
problems in NLP, and show that none of these approaches can improve
classification accuracy in such a setting.
