Automatic discourse processing, which can help understand how sentences
connect to each other, 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 question-answering dataset DCQA
annotated in a manner consistent with the QUD framework. Importantly, data
collection is easily crowdsourced using DCQA's paradigm. We show that this
leads to a parser attaining strong performance according to human evaluation.
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.

本文介绍了一种自动语篇处理方法 ——Questions Under Discussion（QUD），采用了一种新的语言框架，通过大数据集训练 QUD 解析器从而可以获得强大的性能，同时通过 QUD 分析可以实现文档简化。