MOHGCAA, a multi-order hyperbolic graph convolution with aggregated attention, is reported to outperform prior Euclidean and hyperbolic baselines on four datasets in supervised and unsupervised settings.
GTN-ED: Event Detection Using Graph Transformer Networks
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Recent works show that the graph structure of sentences, generated from dependency parsers, has potential for improving event detection. However, they often only leverage the edges (dependencies) between words, and discard the dependency labels (e.g., nominal-subject), treating the underlying graph edges as homogeneous. In this work, we propose a novel framework for incorporating both dependencies and their labels using a recently proposed technique called Graph Transformer Networks (GTN). We integrate GTNs to leverage dependency relations on two existing homogeneous-graph-based models, and demonstrate an improvement in the F1 score on the ACE dataset.
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Multi-Order Hyperbolic Graph Convolution and Aggregated Attention for Social Event Detection
MOHGCAA, a multi-order hyperbolic graph convolution with aggregated attention, is reported to outperform prior Euclidean and hyperbolic baselines on four datasets in supervised and unsupervised settings.