  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.
