  Deep learning methods are often difficult to apply in the legal domain due to
the large amount of labeled data required by deep learning methods. A recent
new trend in the deep learning community is the application of multi-task
models that enable single deep neural networks to perform more than one task at
the same time, for example classification and translation tasks. These powerful
novel models are capable of transferring knowledge among different tasks or
training sets and therefore could open up the legal domain for many deep
learning applications. In this paper, we investigate the transfer learning
capabilities of such a multi-task model on a classification task on the
publicly available Kaggle toxic comment dataset for classifying illegal
comments and we can report promising results.
