面向多语言和多领域的虚假评论检测数据增强
Data Augmentation for Fake Reviews Detection in Multiple Languages and Multiple Domains
AI总结：
本文利用大型语言模型生成多领域、多语言虚假评论数据，通过数据增强有效提升了虚假评论检测模型在多个基准数据集上的准确率。
摘要：
随着互联网的发展，购买习惯已经改变，顾客越来越依赖其他顾客的在线意见来指导自己的购买行为。因此，识别虚假评论成为自然语言处理（NLP）研究的一个重要领域。然而，高性能NLP模型的开发依赖于大量训练数据的可用性，而这些数据对于低资源语言或领域往往并不具备。在本研究中，我们使用大型语言模型生成数据集来训练虚假评论检测器。我们的方法被用于生成不同领域（书评、餐厅评论和酒店评论）和不同语言（英语和中文）的虚假评论。我们的结果表明，我们的数据增强技术在所有领域和语言上都提升了虚假评论检测的性能。使用增强后的数据集，我们的虚假评论检测模型在DeRev TEST上的准确率可提高0.3个百分点，在Amazon TEST上提高10.9个百分点，在Yelp TEST上提高8.3个百分点，在DianPing TEST上提高7.2个百分点。
英文摘要：
With the growth of the Internet, buying habits have changed, and customers have become more dependent on the online opinions of other customers to guide their purchases. Identifying fake reviews thus became an important area for Natural Language Processing (NLP) research. However, developing high-performance NLP models depends on the availability of large amounts of training data, which are often not available for low-resource languages or domains. In this research, we used large language models to generate datasets to train fake review detectors. Our approach was used to generate fake reviews in different domains (book reviews, restaurant reviews, and hotel reviews) and different languages (English and Chinese). Our results demonstrate that our data augmentation techniques result in improved performance at fake review detection for all domains and languages. The accuracy of our fake review detection model can be improved by 0.3 percentage points on DeRev TEST, 10.9 percentage points on Amazon TEST, 8.3 percentage points on Yelp TEST and 7.2 percentage points on DianPing TEST using the augmented datasets.
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。
