Recently, many studies incorporate external knowledge into character-level feature based models to improve the performance of Chinese relation extraction. However, these methods tend to ignore the internal information of the Chinese character and cannot filter out the noisy information of external knowledge. To address these issues, we propose a mixture-of-view-experts framework (MoVE) to dynamically learn multi-view features for Chinese relation extraction. With both the internal and external knowledge of Chinese characters, our framework can better capture the semantic information of Chinese characters. To demonstrate the effectiveness of the proposed framework, we conduct extensive experiments on three real-world datasets in distinct domains. Experimental results show consistent and significant superiority and robustness of our proposed framework. Our code and dataset will be released at: https://gitee.com/tmg-nudt/multi-view-of-expert-for-chineserelation-extraction

翻译：近年来，许多研究将外部知识融入基于字符级特征的模型，以提升中文关系提取的性能。然而，这些方法往往忽略汉字的内在信息，且无法有效过滤外部知识中的噪声信息。为解决这些问题，我们提出了一种多视角专家混合框架（MoVE），用于动态学习中文关系提取的多视角特征。通过结合汉字的内部与外部知识，该框架能够更有效地捕捉汉字的语义信息。为验证所提框架的有效性，我们在三个不同领域的真实数据集上进行了广泛实验。实验结果表明，我们提出的框架在一致性和鲁棒性方面均具有显著优势。相关代码与数据集将在以下链接公开：https://gitee.com/tmg-nudt/multi-view-of-expert-for-chineserelation-extraction