Abduction has long been seen as crucial for narrative comprehension and reasoning about everyday situations. The abductive natural language inference ($\alpha$NLI) task has been proposed, and this narrative text-based task aims to infer the most plausible hypothesis from the candidates given two observations. However, the inter-sentential coherence and the model consistency have not been well exploited in the previous works on this task. In this work, we propose a prompt tuning model $\alpha$-PACE, which takes self-consistency and inter-sentential coherence into consideration. Besides, we propose a general self-consistent framework that considers various narrative sequences (e.g., linear narrative and reverse chronology) for guiding the pre-trained language model in understanding the narrative context of input. We conduct extensive experiments and thorough ablation studies to illustrate the necessity and effectiveness of $\alpha$-PACE. The performance of our method shows significant improvement against extensive competitive baselines.

翻译：溯因推理长期以来被视为日常情境叙事理解与推理的关键。溯因自然语言推理任务（$\alpha$NLI）已被提出，这一基于叙事文本的任务旨在从给定两个观察结果的候选假设中推断出最合理的解释。然而，现有研究尚未充分挖掘句间连贯性与模型一致性。本文提出了一种提示调优模型$\alpha$-PACE，该模型兼顾了自洽性与句间连贯性。此外，我们提出了一种通用的自洽框架，通过考虑多种叙事序列（如线性叙事与倒叙）来引导预训练语言模型理解输入的叙事上下文。我们进行了大量实验和全面的消融研究，以证明$\alpha$-PACE的必要性与有效性。与广泛使用的强基线方法相比，我们的方法性能提升显著。