Self-Consistent Narrative Prompts on Abductive Natural Language
  Inference
Chunkit Chan Xin Liu Tsz Ho Chan Jiayang Cheng Yangqiu Song Ginny Wong Simon See
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.