  The digitalization of the legal domain has been ongoing for a couple of
years. In that process, the application of different machine learning (ML)
techniques is crucial. Tasks such as the classification of legal documents or
contract clauses as well as the translation of those are highly relevant. On
the other side, digitized documents are barely accessible in this field,
particularly in Germany. Today, deep learning (DL) is one of the hot topics
with many publications and various applications. Sometimes it provides results
outperforming the human level. Hence this technique may be feasible for the
legal domain as well. However, DL requires thousands of samples to provide
decent results. A potential solution to this problem is multi-task DL to enable
transfer learning. This approach may be able to overcome the data scarcity
problem in the legal domain, specifically for the German language. We applied
the state of the art multi-task model on three tasks: translation,
summarization, and multi-label classification. The experiments were conducted
on legal document corpora utilizing several task combinations as well as
various model parameters. The goal was to find the optimal configuration for
the tasks at hand within the legal domain. The multi-task DL approach
outperformed the state of the art results in all three tasks. This opens a new
direction to integrate DL technology more efficiently in the legal domain.
