论文标题
部分可观测时空混沌系统的无模型预测
Cross-lingual Transfer Learning for Fake News Detector in a Low-Resource Language
论文作者
论文摘要
缺乏培训数据阻碍了低资源语言检测假新闻（FN）的方法。在这项研究中，我们仅使用来自高资源语言的培训数据来解决问题。我们的FN检测系统通过应用对抗性学习来通过语言传输检测知识，从而允许这种策略。为了协助知识转移，我们的系统通过利用源信息来评判文章的可靠性，这是代表说话者信誉的跨语性功能。在实验中，与使用机器翻译培训数据集的系统相比，我们的系统的精度高3.71％。此外，我们建议使用假新闻检测的跨语性功能剥削提高了3.03％的精度。
Development of methods to detect fake news (FN) in low-resource languages has been impeded by a lack of training data. In this study, we solve the problem by using only training data from a high-resource language. Our FN-detection system permitted this strategy by applying adversarial learning that transfers the detection knowledge through languages. To assist the knowledge transfer, our system judges the reliability of articles by exploiting source information, which is a cross-lingual feature that represents the credibility of the speaker. In experiments, our system got 3.71% higher accuracy than a system that uses a machine-translated training dataset. In addition, our suggested cross-lingual feature exploitation for fake news detection improved accuracy by 3.03%.
