利用GPT-2通过对抗训练对具有有限标签数据的垃圾评论进行分类
Leveraging GPT-2 for Classifying Spam Reviews with Limited Labeled Data via Adversarial Training
摘要：
在线评论是购买服务或产品时的重要信息来源。意见垃圾邮件制造者操纵这些评论，故意改变服务的整体看法。虽然存在在线评论的语料库，但只有少数被标记为垃圾邮件或非垃圾邮件，这使得训练垃圾邮件检测模型变得困难。我们提出了一种对抗性训练机制，利用生成预训练2（GPT-2）的功能，对具有有限标记数据和大量未标记数据的意见垃圾邮件进行分类。在TripAdvisor和YelpZip数据集上的实验表明，当标记数据有限时，该模型在准确性方面至少比最先进的技术高出7%。该模型还可以生成具有合理困惑的合成垃圾邮件/非垃圾邮件评论，从而在训练期间提供额外的标记数据。
英文摘要：
Online reviews are a vital source of information when purchasing a service or a product. Opinion spammers manipulate these reviews, deliberately altering the overall perception of the service. Though there exists a corpus of online reviews, only a few have been labeled as spam or non-spam, making it difficult to train spam detection models. We propose an adversarial training mechanism leveraging the capabilities of Generative Pre-Training 2 (GPT-2) for classifying opinion spam with limited labeled data and a large set of unlabeled data. Experiments on TripAdvisor and YelpZip datasets show that the proposed model outperforms state-of-the-art techniques by at least 7% in terms of accuracy when labeled data is limited. The proposed model can also generate synthetic spam/non-spam reviews with reasonable perplexity, thereby, providing additional labeled data during training.
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。
