THESIS
2023
1 online resource (x, 50 pages) : color illustrations
Abstract
Abduction has long been seen as crucial for narrative comprehension and reasoning about
everyday situations. The abductive natural language inference (αNLI) 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 yet to be well exploited in the previous works on this task. In
this study, we propose a prompt tuning model α-PACE, which takes self-consistency
and inter-sentential coherence into consideration. Besides, we propose a general self-consistency
framework that considers various narrative sequences (e.g., linear narrative
and reverse chronology) for guiding the pre-trained language model in understanding
the narrative con...[
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Abduction has long been seen as crucial for narrative comprehension and reasoning about
everyday situations. The abductive natural language inference (αNLI) 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 yet to be well exploited in the previous works on this task. In
this study, we propose a prompt tuning model α-PACE, which takes self-consistency
and inter-sentential coherence into consideration. Besides, we propose a general self-consistency
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 in
the full data and few-shot settings. Finally, we validate the interpretability of neuralized
continuous prompts by providing qualitative and quantitative analysis.
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