Abstract
The Chinese-Vietnamese cross-lingual event causality identification aims to identify the cause and effect events from the news text describing the event information and present them in a structured form. The existing event causality extraction model faces the following two challenges: 1) The research work related to event causality extraction is mainly focused on resource-rich monolingual scenarios, and the performance of resource-scarce languages needs to be further improved; 2) Existing event causality identification methods are not good at capturing implicit causal semantic relations. Therefore, we propose a novel Chinese-Vietnamese Cross-lingual Event Causality Identification Based on Syntactic Graph Convolution. Firstly, the Chinese-Vietnamese word vectors are mapped into the same semantic space through pre-trained cross-lingual word embeddings. Then the syntactic graph convolutional neural network is used to capture the deep semantic information of the event sentence. Finally, the in-depth semantic features of event sentences in different languages are obtained by combining the cross-attention mechanism of event types. Experiment results on a self-built dataset that the proposed method outperforms the state-of-the-art models.
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Acknowledgement
This work was supported by the National Natural Science Foundation of China(U21B2027, 61972186, 62266027, 62266028); Yunnan provincial major science and technology special plan projects(202302AD080003, 202202AD080003); Yunnan Fundamental Research Projects(202301AT070393, 202301AT070471); Kunming University of Science and Technology’s"Double First-rate" construction joint project(202201BE070001-021).
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Zhu, E., Yu, Z., Huang, Y., Xian, Y., Xiang, Y., Zhou, S. (2024). Chinese-Vietnamese Cross-Lingual Event Causality Identification Based on Syntactic Graph Convolution. In: Liu, Q., et al. Pattern Recognition and Computer Vision. PRCV 2023. Lecture Notes in Computer Science, vol 14431. Springer, Singapore. https://doi.org/10.1007/978-981-99-8540-1_7
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