  We present DiscoSense, a benchmark for commonsense reasoning via
understanding a wide variety of discourse connectives. We generate compelling
distractors in DiscoSense using Conditional Adversarial Filtering, an extension
of Adversarial Filtering that employs conditional generation. We show that
state-of-the-art pre-trained language models struggle to perform well on
DiscoSense, which makes this dataset ideal for evaluating next-generation
commonsense reasoning systems.
