To address the problems in the cross-language news event causality identification task for Chinese-Vietnamese,such as the difficulty in unifying the text semantic space across Chinese-Vietnamese and capturing the causal correlation fea-tures between news,we propose a Chinese-Vietnamese cross-lingual news event causality identification method based on type matrix transfer.First,the text semantic space across Chinese and Vietnamese languages is unified through cross-lingual pre-training.Second,a tree-shaped long-short-term memory recurrent neural network is used to extract syntactically struc-tured features in Chinese-Vietnamese texts.Finally,the event causality between Chinese-Vietnamese temporal sentence pairs is identified by incorporating Chinese-Vietnamese syntactic features and combining the attention mechanism based on temporal type transfer.Experimental results demonstrate that our method improves the accuracy of identifying causal rela-tionships between Chinese and Vietnamese cross-language news events when compared to the best baseline model.
维普
万方数据