Stop Illegal Comments: A Multi-Task Deep Learning Approach
This work addresses the challenge of limited labeled data in the legal domain for deep learning applications, though it appears incremental in its approach.
The paper tackled the problem of applying deep learning to legal domain tasks by investigating a multi-task model's transfer learning capabilities on classifying illegal comments using the Kaggle toxic comment dataset, reporting promising results.
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.