Detecting covariate drift in text data using document embeddings and
  dimensionality reduction
Vinayak Sodar Ankit Sekseria
Detecting covariate drift in text data is essential for maintaining the
reliability and performance of text analysis models. In this research, we
investigate the effectiveness of different document embeddings, dimensionality
reduction techniques, and drift detection methods for identifying covariate
drift in text data. We explore three popular document embeddings: term
frequency-inverse document frequency (TF-IDF) using Latent semantic
analysis(LSA) for dimentionality reduction and Doc2Vec, and BERT embeddings,
with and without using principal component analysis (PCA) for dimensionality
reduction. To quantify the divergence between training and test data
distributions, we employ the Kolmogorov-Smirnov (KS) statistic and the Maximum
Mean Discrepancy (MMD) test as drift detection methods. Experimental results
demonstrate that certain combinations of embeddings, dimensionality reduction
techniques, and drift detection methods outperform others in detecting
covariate drift. Our findings contribute to the advancement of reliable text
analysis models by providing insights into effective approaches for addressing
covariate drift in text data.