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- W4386841414 abstract "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." @default.
- W4386841414 created "2023-09-19" @default.
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- W4386841414 date "2023-09-15" @default.
- W4386841414 modified "2023-09-27" @default.
- W4386841414 title "Self-Consistent Narrative Prompts on Abductive Natural Language Inference" @default.
- W4386841414 doi "https://doi.org/10.48550/arxiv.2309.08303" @default.
- W4386841414 hasPublicationYear "2023" @default.
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