【摘 要】
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Sentence compression is a task of compressing sentences containing redundant information into short semantic expressions,simplifying the text struc-ture and retaining important meanings and informatio
【机 构】
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Beijing University of Posts and Telecommunications,Beijing,China
【出 处】
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第十八届中国计算语言学大会暨中国中文信息学会2019学术年会
论文部分内容阅读
Sentence compression is a task of compressing sentences containing redundant information into short semantic expressions,simplifying the text struc-ture and retaining important meanings and information.Neural network-based models are limited by the size of the window and do not perform well when using long-distance dependent information.To solve this problem,we introduce a ver-sion of the graph convolutional network(GCNs)to utilize the syntactic depend-ency relations,and explore a new way to combine GCNs with the Sequence-to-Sequence model(Seq2Seq)to complete the task.The model combines the ad-vantages of both and achieves complementary effects.In addition,in order to reduce the error propagation of the parse tree,we dynamically adjust the depend-ency arc to optimize the construction process of GCNs.Experiments show that the model combined with the graph convolution network is better than the origi-nal model,and the performance in the Google sentence compression dataset has been effectively improved.
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