  Protecting privileged communications and data from inadvertent disclosure is
a paramount task in the US legal practice. Traditionally counsels rely on
keyword searching and manual review to identify privileged documents in cases.
As data volumes increase, this approach becomes less and less defensible in
costs. Machine learning methods have been used in identifying privilege
documents. Given the generalizable nature of privilege in legal cases, we
hypothesize that transfer learning can capitalize knowledge learned from
existing labeled data to identify privilege documents without requiring
labeling new training data. In this paper, we study both traditional machine
learning models and deep learning models based on BERT for privilege document
classification tasks in legal document review, and we examine the effectiveness
of transfer learning in privilege model on three real world datasets with
privilege labels. Our results show that BERT model outperforms the industry
standard logistic regression algorithm and transfer learning models can achieve
decent performance on datasets in same or close domains.
