QUDSELECT：面向讨论中问题解析的选择性解码
QUDSELECT: Selective Decoding for Questions Under Discussion Parsing
AI总结：
针对流水线式 QUD 解析难以兼顾理论准则的问题，提出联合训练框架 QUDSELECT，通过多候选选择性解码与准则评分提升结构预测性能。
摘要：
Question Under Discussion（QUD，讨论中问题）是一种使用隐式问题揭示句子间语篇关系的语篇框架。在 QUD 解析中，每个句子都被视为对先前语境中某个锚点句子所触发问题的回答。由此产生的 QUD 结构需要符合若干理论准则，例如回答兼容性（即问题被回答的程度），这使 QUD 解析成为一项具有挑战性的任务。以往工作以流水线方式构建 QUD 解析器（即先在语境中检测触发句，然后生成问题）。然而，这些解析器缺乏对任务的整体认识，几乎无法满足所有准则。在本工作中，我们提出 QUDSELECT，这是一个联合训练框架，能够在考虑 QUD 准则的情况下选择性地解码 QUD 依赖结构。我们利用指令微调训练模型，使其同时预测锚点句子并生成相关问题。为显式纳入这些准则，我们采用选择性解码策略：在推理期间采样多个 QUD 候选，随后使用准则评分器选择最佳候选。我们的方法在人工评估中比最先进的基线模型高出 9%，在自动评估中高出 4%，证明了该框架的有效性。
英文摘要：
Question Under Discussion (QUD) is a discourse framework that uses implicit questions to reveal discourse relationships between sentences. In QUD parsing, each sentence is viewed as an answer to a question triggered by an anchor sentence in prior context. The resulting QUD structure is required to conform to several theoretical criteria like answer compatibility (how well the question is answered), making QUD parsing a challenging task. Previous works construct QUD parsers in a pipelined manner (i.e. detect the trigger sentence in context and then generate the question). However, these parsers lack a holistic view of the task and can hardly satisfy all the criteria. In this work, we introduce QUDSELECT, a joint-training framework that selectively decodes the QUD dependency structures considering the QUD criteria. Using instruction-tuning, we train models to simultaneously predict the anchor sentence and generate the associated question. To explicitly incorporate the criteria, we adopt a selective decoding strategy of sampling multiple QUD candidates during inference, followed by selecting the best one with criteria scorers. Our method outperforms the state-of-the-art baseline models by 9% in human evaluation and 4% in automatic evaluation, demonstrating the effectiveness of our framework.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
