论文标题
与变压器的跨语性关系提取
Cross-Lingual Relation Extraction with Transformers
论文作者
论文摘要
关系提取（RE）是信息提取中最重要的任务之一，因为它为许多NLP应用程序提供了必不可少的信息。在本文中，我们提出了一种跨语性的方法，该方法不需要目标语言或任何跨语性资源的任何人类注释。在无监督的跨语言表示学习框架的基础上，我们使用一种新型的编码方案开发了几个基于深层变压器的RE模型，该方案可以有效地编码实体位置和实体类型信息。我们的RE模型在接受英语数据培训时，优于几个基于神经网络的英语RE模型。更重要的是，我们的模型可以应用于执行零射击的跨语性重新元素，从而在两个数据集上实现了最先进的跨语义性能（68-89％的监督目标语言RE模型的准确性的68-89％）。高的跨语性转移效率不需要额外的培训数据或跨语性资源表明，我们的RE模型对于低资源语言特别有用。
Relation extraction (RE) is one of the most important tasks in information extraction, as it provides essential information for many NLP applications. In this paper, we propose a cross-lingual RE approach that does not require any human annotation in a target language or any cross-lingual resources. Building upon unsupervised cross-lingual representation learning frameworks, we develop several deep Transformer based RE models with a novel encoding scheme that can effectively encode both entity location and entity type information. Our RE models, when trained with English data, outperform several deep neural network based English RE models. More importantly, our models can be applied to perform zero-shot cross-lingual RE, achieving the state-of-the-art cross-lingual RE performance on two datasets (68-89% of the accuracy of the supervised target-language RE model). The high cross-lingual transfer efficiency without requiring additional training data or cross-lingual resources shows that our RE models are especially useful for low-resource languages.
