  Most current methods for detecting anomalies in text concentrate on
constructing models solely relying on unlabeled data. These models operate on
the presumption that no labeled anomalous examples are available, which
prevents them from utilizing prior knowledge of anomalies that are typically
present in small numbers in many real-world applications. Furthermore, these
models prioritize learning feature embeddings rather than optimizing anomaly
scores directly, which could lead to suboptimal anomaly scoring and inefficient
use of data during the learning process. In this paper, we introduce FATE, a
deep few-shot learning-based framework that leverages limited anomaly examples
and learns anomaly scores explicitly in an end-to-end method using deviation
learning. In this approach, the anomaly scores of normal examples are adjusted
to closely resemble reference scores obtained from a prior distribution.
Conversely, anomaly samples are forced to have anomalous scores that
considerably deviate from the reference score in the upper tail of the prior.
Additionally, our model is optimized to learn the distinct behavior of
anomalies by utilizing a multi-head self-attention layer and multiple instance
learning approaches. Comprehensive experiments on several benchmark datasets
demonstrate that our proposed approach attains a new level of state-of-the-art
performance.
