  Over the past several years, legal applications of deep learning have been on
the rise. However, as with other high-stakes decision making areas, the
requirement for interpretability is of crucial importance. Current models
utilized by legal practitioners are more of the conventional machine learning
type, wherein they are inherently interpretable, yet unable to harness the
performance capabilities of data-driven deep learning models. In this work, we
utilize deep learning models in the area of trademark law to shed light on the
issue of likelihood of confusion between trademarks. Specifically, we introduce
a model-agnostic interpretable intermediate layer, a technique which proves to
be effective for legal documents. Furthermore, we utilize weakly supervised
learning by means of a curriculum learning strategy, effectively demonstrating
the improved performance of a deep learning model. This is in contrast to the
conventional models which are only able to utilize the limited number of
expensive manually-annotated samples by legal experts. Although the methods
presented in this work tackles the task of risk of confusion for trademarks, it
is straightforward to extend them to other fields of law, or more generally, to
other similar high-stakes application scenarios.
