Prompt2Gaussia: Uncertain Prompt-learning for Script Event Prediction

• URL: http://arxiv.org/abs/2308.02103v1
• Date: Fri, 4 Aug 2023 01:34:46 GMT
• Title: Prompt2Gaussia: Uncertain Prompt-learning for Script Event Prediction
• Authors: Shiyao Cui, Xin Cong, Jiawei Sheng, Xuebin Wang, Tingwen Liu, Jinqiao Shi
• Abstract summary: Script Event Prediction (SEP) aims to predict the subsequent event for a given event chain from a candidate list. We consider public pre-trained language models as knowledge bases and automatically mine the script-related knowledge via prompt-learning. Our method, which benefits from knowledge evoked from pre-trained language models, outperforms prior baselines by 1.46% and 1.05% on two benchmarks.
• Score: 11.54608099442562
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Script Event Prediction (SEP) aims to predict the subsequent event for a given event chain from a candidate list. Prior research has achieved great success by integrating external knowledge to enhance the semantics, but it is laborious to acquisite the appropriate knowledge resources and retrieve the script-related knowledge. In this paper, we regard public pre-trained language models as knowledge bases and automatically mine the script-related knowledge via prompt-learning. Still, the scenario-diversity and label-ambiguity in scripts make it uncertain to construct the most functional prompt and label token in prompt learning, i.e., prompt-uncertainty and verbalizer-uncertainty. Considering the innate ability of Gaussian distribution to express uncertainty, we deploy the prompt tokens and label tokens as random variables following Gaussian distributions, where a prompt estimator and a verbalizer estimator are proposed to estimate their probabilistic representations instead of deterministic representations. We take the lead to explore prompt-learning in SEP and provide a fresh perspective to enrich the script semantics. Our method is evaluated on the most widely used benchmark and a newly proposed large-scale one. Experiments show that our method, which benefits from knowledge evoked from pre-trained language models, outperforms prior baselines by 1.46\% and 1.05\% on two benchmarks, respectively.

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