首页|Automatic Generation of Artificial Space Weather Forecast Product Based on Sequence-to-sequence Model
Automatic Generation of Artificial Space Weather Forecast Product Based on Sequence-to-sequence Model
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原文链接
国家科技期刊平台
NETL
NSTL
万方数据
Both analyzing a large amount of space weather observed data and alleviating personal experience bias are significant challenges in generating artificial space weather forecast products.With the use of natural lan-guage generation methods based on the sequence-to-sequence model,space weather forecast texts can be automati-cally generated.To conduct our generation tasks at a fine-grained level,a taxonomy of space weather phenomena based on descriptions is presented.Then,our MDH(Multi-Domain Hybrid)model is proposed for generating space weather summaries in two stages.This model is composed of three sequence-to-sequence-based deep neural net-work sub-models(one Bidirectional Auto-Regressive Transformers pre-trained model and two Transformer mo-dels).Then,to evaluate how well MDH performs,quality evaluation metrics based on two prevalent automatic metrics and our innovative human metric are presented.The comprehensive scores of the three summaries generat-ing tasks on testing datasets are 70.87,93.50,and 92.69,respectively.The results suggest that MDH can generate space weather summaries with high accuracy and coherence,as well as suitable length,which can assist forecast-ers in generating high-quality space weather forecast products,despite the data being starved.
Space weatherDeep learningData-to-textNatural language generation
罗冠霆、ZOU Yenan、CAI Yanxia
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中国科学院大学国家空间科学中心
State Key Laboratory of Space Weather,National Space Science Center,Chinese Academy of Sciences,Beijing 100190
Key Laboratory of Science and Technology on Environment Space Situation Awareness,Chinese Academy of Sciences,Beijing 100190
Key Research Program of the Chinese Academy of Sciences
国家科技期刊平台
NSTL
万方数据
