Leveraging GPT-2 for Classifying Spam Reviews with Limited Labeled Data
  via Adversarial Training
  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.
