  Prior studies in privacy policies frame the question answering (QA) task as
identifying the most relevant text segment or a list of sentences from a policy
document given a user query. Existing labeled datasets are heavily imbalanced
(only a few relevant segments), limiting the QA performance in this domain. In
this paper, we develop a data augmentation framework based on ensembling
retriever models that captures the relevant text segments from unlabeled policy
documents and expand the positive examples in the training set. In addition, to
improve the diversity and quality of the augmented data, we leverage multiple
pre-trained language models (LMs) and cascade them with noise reduction filter
models. Using our augmented data on the PrivacyQA benchmark, we elevate the
existing baseline by a large margin (10\% F1) and achieve a new
state-of-the-art F1 score of 50\%. Our ablation studies provide further
insights into the effectiveness of our approach.
