arXiv 2208.124822022-08-26cs.CLcs.AI

Cross-lingual Transfer Learning for Fake News Detector in a Low-Resource Language

Sangdo Han

备注：: I've withdraw this paper from a journal during revision. There were two reasons. First, data was not verified enough. Data verification steps are required. Second, even the average accuracy was higher than baseline, but it was not stable enough. However, I think the news embedding truely represents credibility of the speakers. I hope that the knowledge I've got would help for other researchers

英文摘要：

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%.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。