论文标题
事件检测：门的多样性和句法重要性得分对图形卷积神经网络
Event Detection: Gate Diversity and Syntactic Importance Scoresfor Graph Convolution Neural Networks
论文作者
论文摘要
关于事件检测（ED）的最新研究，表明句法依赖图可以在图形卷积神经网络（GCN）中使用以实现最新的每一范围。但是，在此类基于图的模型中，对触发候选单词的iSagnostic的被命名载体的计算给出了事件预测的候选候选者的无关信息。另外，当前的ED模型无法利用该单词的整体上下文重要性得分，可以通过脱键树来提高性能。在本研究中，我们根据触发候选者的信息，在GCN模型的隐藏vec-tors中提出了一种新颖的门控机制，以滤波噪声信息。 Wealso引入了新的机制，以实现大门的上下文多样性，以及ED中图形模型的象征分数的一致性。实验表明，所提出的模型在两个ED数据集上实现了最先进的表现
Recent studies on event detection (ED) haveshown that the syntactic dependency graph canbe employed in graph convolution neural net-works (GCN) to achieve state-of-the-art per-formance. However, the computation of thehidden vectors in such graph-based models isagnostic to the trigger candidate words, po-tentially leaving irrelevant information for thetrigger candidate for event prediction. In addi-tion, the current models for ED fail to exploitthe overall contextual importance scores of thewords, which can be obtained via the depen-dency tree, to boost the performance. In thisstudy, we propose a novel gating mechanismto filter noisy information in the hidden vec-tors of the GCN models for ED based on theinformation from the trigger candidate. Wealso introduce novel mechanisms to achievethe contextual diversity for the gates and theimportance score consistency for the graphsand models in ED. The experiments show thatthe proposed model achieves state-of-the-artperformance on two ED datasets
