Sanghamitra Dutta


2021

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GTN-ED: Event Detection Using Graph Transformer Networks
Sanghamitra Dutta | Liang Ma | Tanay Kumar Saha | Di Liu | Joel Tetreault | Alejandro Jaimes
Proceedings of the Fifteenth Workshop on Graph-Based Methods for Natural Language Processing (TextGraphs-15)

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 Network (GTN). We integrate GTN to leverage dependency relations on two existing homogeneous-graph-based models and demonstrate an improvement in the F1 score on the ACE dataset.