Self-Consistent Narrative Prompts on Abductive Natural Language Inference

Code

• github.com/hkust-knowcomp/alpha-pace
  OfficialIn paperpytorch★ 5

Abstract

Abduction has long been seen as crucial for narrative comprehension and reasoning about everyday situations. The abductive natural language inference (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 -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 -PACE. The performance of our method shows significant improvement against extensive competitive baselines.

Tasks

Reproductions