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Automatic Generation of Artificial Space Weather Forecast Product Based on Sequence-to-sequence Model
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作者 罗冠霆 ZOU Yenan CAI Yanxia 《空间科学学报》 CAS CSCD 北大核心 2024年第1期80-94,共15页
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 languag... 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 language generation methods based on the sequence-to-sequence model,space weather forecast texts can be automatically 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 network sub-models(one Bidirectional Auto-Regressive Transformers pre-trained model and two Transformer models).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 generating 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 forecasters in generating high-quality space weather forecast products,despite the data being starved. 展开更多
关键词 Space weather Deep learning data-to-text Natural language generation
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Recent advances of neural text generation:Core tasks,datasets,models and challenges 被引量:2
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作者 JIN HanQi CAO Yue +2 位作者 WANG TianMing XING XinYu WAN XiaoJun 《Science China(Technological Sciences)》 SCIE EI CAS CSCD 2020年第10期1990-2010,共21页
In recent years,deep neural network has achieved great success in solving many natural language processing tasks.Particularly,substantial progress has been made on neural text generation,which takes the linguistic and... In recent years,deep neural network has achieved great success in solving many natural language processing tasks.Particularly,substantial progress has been made on neural text generation,which takes the linguistic and non-linguistic input,and generates natural language text.This survey aims to provide an up-to-date synthesis of core tasks in neural text generation and the architectures adopted to handle these tasks,and draw attention to the challenges in neural text generation.We first outline the mainstream neural text generation frameworks,and then introduce datasets,advanced models and challenges of four core text generation tasks in detail,including AMR-to-text generation,data-to-text generation,and two text-to-text generation tasks(i.e.,text summarization and paraphrase generation).Finally,we present future research directions for neural text generation.This survey can be used as a guide and reference for researchers and practitioners in this area. 展开更多
关键词 natural language generation neural text generation AMR-to-text data-to-text text summarization paraphrase generation
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