考虑提示学习的洪涝灾害应急决策自动问答模型研究

Research on automatic question answering model of flood disaster emergency decision-making considering Prompt-learning

为提高洪涝灾害应急处置时效性和科学性,构建洪涝灾害应急决策自动问答系统模型,以提高应急指挥团队的决策效率,在分析洪涝灾害应急决策逻辑基础上,以摘要式问答为任务框架,收集整理包含洪涝灾害应急情景和应急决策的摘要式问答对数据集,建立可用于问答生成的GPT2预训练语言模型,并引入提示学习(Prompt-learning),通过自动创建连续型前缀提示(Prompt),优化少量连续参数,缓解问答对数据较少带来的过拟合风险,利用人工评估和自动评估2种方法验证模型的有效性。研究结果表明:通过GPT2与提示学习相结合建立的自动问答模型,可根据洪涝灾害情景生成语言质量良好及决策信息丰富的答案,有利于提高洪涝灾害应急处置中的科学决策能力。

To improve the timeliness and scientificity of emergency response to the flood disasters, an automatic question answering system model for the emergency decision-making of flood disasters was constructed to improve the decision-making efficiency of emergency command team.Based on the analysis on the emergency decision-making logic of flood disasters, the abstractive question answering was taken as the task framework, and the data sets of abstractive question answering containing the emergency scenarios and emergency decision-making of flood disasters were collected and sorted out.Then a GPT2 pre-training language model that could be used for the question answering generation was established, and the Prompt-learning was introduced.By automatically creating the continuous prefix Prompt, a small number of continuous parameters were optimized to alleviate the risk of over-fitting caused by less data in question answering.Two methods of human evaluation and automatic evaluation were used to verify the effectiveness of the model.The results showed that the automatic question answering model established by the GPT2 and Prompt-learning could generate the answer with good language quality and rich decision-making information according to the flood disaster scenarios.It is beneficial to improve the scientific decision-making ability in the emergency response of flood disasters.