论文标题
门：用于跨语言关系和事件提取的图形注意变压器编码器
GATE: Graph Attention Transformer Encoder for Cross-lingual Relation and Event Extraction
论文作者
论文摘要
跨语性关系和事件提取使用图卷积网络（GCN）的最新进展具有通用依赖性解释，以学习语言 - 敏捷的句子表示形式，以便将接受一种语言训练的模型应用于其他语言。但是，GCN难以建模具有远距离依赖性的单词或在依赖树中没有直接连接。为了应对这些挑战，我们建议利用自我发挥的机制，在这些机制中，我们明确融合结构信息，以了解具有不同句法距离的单词之间的依赖性。我们介绍GATE，A {\ bf G} Raph {\ bf a} ttention {\ bf t} ransformer {\ bf e} ncoder，并在关系和事件提取任务上测试其跨语言可传递性。我们在ACE05数据集上执行实验，其中包括三种类型上不同的语言：英语，中文和阿拉伯语。评估结果表明，Gate的表现胜过最近提出的三种方法。我们的详细分析表明，由于依赖句法依赖性，门会产生促进跨语言转移的强大表示。
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. 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.
