Dependency-Gated Cascade Biaffine Network for Chinese Semantic Dependency Graph Parsing
Lecture Notes in Artificial Intelligence(2019)
摘要
The Chinese Semantic Dependency Graph (CSDG) parsing breaks the limitation of the syntactic or semantic tree structure dependency system with a richer representation ability to express more complex language phenomena and semantic relationships. Most of the existing CSDG parsing systems used transition-based approach. It needs to define a complex transition system and its performance depends heavily on whether the model can properly represent the transition state. In this paper, we adopt neural graph-based approach which using Biaffine network to solve the CSDG parsing task. Furthermore, considering that dependency edge and label have the strong relationship, we design an effective dependency-gated cascade mechanism to improve the accuracy of dependency label prediction. We test our system on the SemEval-2016 Task 9 dataset. Experiment result shows that our model achieves state-of-the-art performance with 7.48% and 6.36% labeled F1-score improvement compared to the previous best model in TEXTBOOKS and NEWS domain respectively.
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关键词
Chinese semantic dependency graph paring,Dependency-gated cascade mechanism,Biaffine network
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