  Discourse relations describe how two propositions relate to one another, and
identifying them automatically is an integral part of natural language
understanding. However, annotating discourse relations typically requires
expert annotators. Recently, different semantic aspects of a sentence have been
represented and crowd-sourced via question-and-answer (QA) pairs. This paper
proposes a novel representation of discourse relations as QA pairs, which in
turn allows us to crowd-source wide-coverage data annotated with discourse
relations, via an intuitively appealing interface for composing such questions
and answers. Based on our proposed representation, we collect a novel and
wide-coverage QADiscourse dataset, and present baseline algorithms for
predicting QADiscourse relations.
