  Automatic discourse processing is bottlenecked by data: current discourse
formalisms pose highly demanding annotation tasks involving large taxonomies of
discourse relations, making them inaccessible to lay annotators. This work
instead adopts the linguistic framework of Questions Under Discussion (QUD) for
discourse analysis and seeks to derive QUD structures automatically. QUD views
each sentence as an answer to a question triggered in prior context; thus, we
characterize relationships between sentences as free-form questions, in
contrast to exhaustive fine-grained taxonomies. We develop the
first-of-its-kind QUD parser that derives a dependency structure of questions
over full documents, trained using a large, crowdsourced question-answering
dataset DCQA (Ko et al., 2022). Human evaluation results show that QUD
dependency parsing is possible for language models trained with this
crowdsourced, generalizable annotation scheme. We illustrate how our QUD
structure is distinct from RST trees, and demonstrate the utility of QUD
analysis in the context of document simplification. Our findings show that QUD
parsing is an appealing alternative for automatic discourse processing.
