语义链接不确定下省略三段论补全的成对逻辑选择
Pairwise Logical Selection of Enthymeme Completions under Semantic-Link Uncertainty
AI总结：
本研究提出PWAL方法，将神经符号管道扩展到缺失结论选择，用逻辑阻力分数替代二元蕴含，在五项任务上提升省略三段论补全的严格准确率并降低平局率，且提供透明分数跟踪。
AI中文摘要：
论证常省略前提或结论，形成省略三段论。我们研究针对省略成分的两个候选的成对逻辑选择问题。现有自然语言方法可识别或生成候选，但通常不说明所选候选如何完成推理；而基于逻辑的方法通常假定所需公式和背景知识可用。我们将先前的神经符号管道从缺失前提选择扩展到缺失结论选择，并用逻辑阻力分数替代二元蕴含结果。Top-Link 在单一最高置信度语义链接配置下使用加权部分最大可满足性（Partial MaxSAT）。随后我们引入可能世界原子链接形式化（PWAL），该方法保持翻译后的公式固定，对跨公式语义链接的替代配置的逻辑阻力进行边缘化处理。我们在五项任务上评估 PWAL：用于缺失前提选择的 ARCT 和源自 CDED 的任务，用于缺失结论选择的 iDebate 和源自 AAE2 的任务，以及用于溯因假设选择的 alphaNLI。相较于 Top-Link，PWAL 在全部五项任务上将严格准确率提高了 2.95 至 30.86 个百分点，并将平局率降低了 4.57 至 58.00 个百分点；当平局计半分时，准确率仍提高了 0.45 至 6.04 个百分点。PWAL 还记录了每次比较的翻译公式、采样的链接配置和阻力组件，为每个分数提供了透明的跟踪记录。
英文摘要：
Arguments often omit premises or claims, forming enthymemes. We study pairwise logical selection between two candidates for the omitted component. Existing natural language methods can identify or generate candidates but often do not expose how the selected candidate completes the inference, while logic-based approaches usually assume that the required formulae and background knowledge are available. We extend a prior neuro-symbolic pipeline from missing-premise to missing-claim selection and replace binary entailment outcomes with logical-resistance scores. Top-Link uses weighted Partial MaxSAT under a single configuration of highest-confidence semantic links. We then introduce Possible-World Atom-Link Formalization (PWAL), which keeps translated formulae fixed and marginalizes logical resistance over alternative cross-formula semantic-link configurations. We evaluate PWAL on five tasks: ARCT and a CDED-derived task for missing-premise selection, iDebate- and AAE2-derived tasks for missing-claim selection, and alphaNLI for abductive hypothesis selection. Relative to Top-Link, PWAL raises strict accuracy by 2.95-30.86 percentage points and reduces tie rates by 4.57-58.00 percentage points on all five tasks. When ties receive half credit, accuracy still increases by 0.45-6.04 percentage points. PWAL also records the translated formulae, sampled link configurations, and resistance components for every comparison, providing a transparent trace of each score.
