Self-Consistent Narrative Prompts on Abductive Natural Language Inference
This work addresses narrative comprehension for AI systems, but it is incremental as it builds on existing methods for a specific task.
The paper tackles the abductive natural language inference task by proposing a prompt tuning model that incorporates self-consistency and inter-sentential coherence, resulting in significant performance improvements over competitive baselines.
Abduction has long been seen as crucial for narrative comprehension and reasoning about everyday situations. The abductive natural language inference ($α$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 $α$-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 $α$-PACE. The performance of our method shows significant improvement against extensive competitive baselines.