论文标题
利用GPT-2通过对抗培训进行有限标记的数据分类垃圾邮件评论
Leveraging GPT-2 for Classifying Spam Reviews with Limited Labeled Data via Adversarial Training
论文作者
论文摘要
购买服务或产品时，在线评论是至关重要的信息来源。意见垃圾邮件发送者操纵这些评论，故意改变了对服务的整体看法。尽管存在大量的在线评论，但只有少数被标记为垃圾邮件或非垃圾邮件，因此很难训练垃圾邮件检测模型。我们提出了一种利用生成预训练2（GPT-2）功能的对抗训练机制，用于将意见垃圾邮件分类为有限的标记数据和大量未标记的数据。 TripAdvisor和Yelpzip数据集的实验表明，当标记的数据受到限制时，所提出的模型在准确性方面至少优于最先进的技术。所提出的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，从而在培训期间提供了其他标记的数据。
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.
