  A privacy policy is a document that states how a company intends to handle
and manage their customers' personal data. One of the problems that arises with
these privacy policies is that their content might violate data privacy
regulations. Because of the enormous number of privacy policies that exist, the
only realistic way to check for legal inconsistencies in all of them is through
an automated method. In this work, we use Natural Language Inference (NLI)
techniques to compare privacy regulations against sections of privacy policies
from a selection of large companies. Our NLI model uses pre-trained embeddings,
along with BiLSTM in its attention mechanism. We tried two versions of our
model: one that was trained on the Stanford Natural Language Inference (SNLI)
and the second on the Multi-Genre Natural Language Inference (MNLI) dataset. We
found that our test accuracy was higher on our model trained on the SNLI, but
when actually doing NLI tasks on real world privacy policies, the model trained
on MNLI generalized and performed much better.
