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

Understand

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 {\bf G}raph {\bf A}ttention {\bf T}ransformer {\bf E}ncoder, and test its cross-lingual transferability on relation and event extraction tasks.