NLP-ADBench: NLP Anomaly Detection Benchmark

Understand

Anomaly detection (AD) is an important machine learning task with applications in fraud detection, content moderation, and user behavior analysis.

• However, AD is relatively understudied in a natural language processing (NLP) context, limiting its effectiveness in detecting harmful content, phishing attempts, and spam reviews.
• We introduce NLP-ADBench, the most comprehensive NLP anomaly detection (NLP-AD) benchmark to date, which includes eight curated datasets and 19 state-of-the-art algorithms.
• These span 3 end-to-end methods and 16 two-step approaches that adapt classical, non-AD methods to language embeddings from BERT and OpenAI.