Graph Transformer Networks with Syntactic and Semantic Structures for Event Argument Extraction

Understand

The goal of Event Argument Extraction (EAE) is to find the role of each entity mention for a given event trigger word.

• It has been shown in the previous works that the syntactic structures of the sentences are helpful for the deep learning models for EAE.
• However, a major problem in such prior works is that they fail to exploit the semantic structures of the sentences to induce effective representations for EAE.
• Consequently, in this work, we propose a novel model for EAE that exploits both syntactic and semantic structures of the sentences with the Graph Transformer Networks (GTNs) to learn more effective sentence structures for EAE.