GATE: Graph Attention Transformer Encoder for Cross-lingual Relation and Event Extraction

• Main Authors: Ahmad, Wasi Uddin, Peng, Nanyun, Chang, Kai-Wei
• Format: Conference Proceeding
• Language: English
• ISSN: 2159-5399; 2374-3468
• DOI: 10.1609/aaai.v35i14.17478

Recent progress in cross-lingual relation and event extraction use graph convolutional networks (GCNs) with universal dependency parses to learn language-agnostic sentence representations such that models trained on one language can be applied to other languages. However, GCNs struggle to model words with long-range dependencies or are not directly connected in the dependency tree. To address these challenges, we propose to utilize the self-attention mechanism where we explicitly fuse structural information to learn the dependencies between words with different syntactic distances. We introduce GATE, a Graph Attention Transformer Encoder, and test its cross-lingual transferability on relation and event extraction tasks. We perform experiments on the ACE05 dataset that includes three typologically different languages: English, Chinese, and Arabic. The evaluation results show that GATE outperforms three recently proposed methods by a large margin. Our detailed analysis reveals that due to the reliance on syntactic dependencies, GATE produces robust representations that facilitate transfer across languages.