Discourse Analysis via Questions and Answers: Parsing Dependency Structures of Questions Under Discussion
Discourse Analysis via Questions and Answers: Parsing Dependency Structures of Questions Under Discussion
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DOI：
10.48550/arxiv.2210.05905
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发表时间：
2022-10
期刊：
影响因子：
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通讯作者：
Wei-Jen Ko;Yating Wu;Cutter Dalton;Dananjay Srinivas;Greg Durrett;Junyi Jessy Li
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作者：
Wei-Jen Ko;Yating Wu;Cutter Dalton;Dananjay Srinivas;Greg Durrett;Junyi Jessy Li
自动话语处理受到数据的瓶颈：当前的话语形式主义提出了要求很高的注释任务，涉及大量的话语关系分类，使得非专业注释者无法访问它们。这项工作采用讨论问题（QUD）的语言框架进行话语分析，并寻求自动导出 QUD 结构。 QUD 将每个句子视为对先前上下文中触发的问题的答案；因此，我们将句子之间的关系描述为自由形式的问题，而不是详尽的细粒度分类法。我们开发了首个 QUD 解析器，该解析器派生出完整文档中问题的依赖结构，并使用大型众包问答数据集 DCQA 进行训练（Ko et al., 2022）。人类评估结果表明，对于使用这种众包、可概括的注释方案训练的语言模型，QUD 依存解析是可能的。我们说明了 QUD 结构与 RST 树的不同之处，并演示了 QUD 分析在文档简化的背景下的实用性。我们的研究结果表明，QUD 解析是自动话语处理的一个有吸引力的替代方案。
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.