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。

通过问题和回答进行话语分析：解析讨论中问题的依存结构

Discourse Analysis via Questions and Answers: Parsing Dependency Structures of Questions Under Discussion

This paper presents a data-driven study focusing on analyzing and predicting
sentence deletion -- a prevalent but understudied phenomenon in document
simplification -- on a large English text simplification corpus. We inspect
various document and discourse factors associated with sentence deletion, using
a new manually annotated sentence alignment corpus we collected. We reveal that
professional editors utilize different strategies to meet readability standards
of elementary and middle schools. To predict whether a sentence will be deleted
during simplification to a certain level, we harness automatically aligned data
to train a classification model. Evaluated on our manually annotated data, our
best models reached F1 scores of 65.2 and 59.7 for this task at the levels of
elementary and middle school, respectively. We find that discourse level
factors contribute to the challenging task of predicting sentence deletion for
simplification.

本文基于一个大型英文文本简化语料库，对一种常见但鲜为人知的现象 —— 句子删除进行了数据驱动的研究和预测，检查了与句子删除相关的各种文档和语篇因素，揭示了专业编辑为满足小学和初中教育标准而采用的不同策略，并利用自动化对齐数据训练了分类模型以预测句子被删除的可能性。研究发现语篇层面的因素为句子简化中的句子删除预测带来了挑战。