Stop Illegal Comments: A Multi-Task Deep Learning Approach

TL;DR

This paper explores the use of multi-task deep learning models to improve illegal comment detection in the legal domain, demonstrating transfer learning benefits on a toxic comment dataset.

Contribution

It introduces a multi-task deep learning approach for illegal comment classification and evaluates its transfer learning capabilities in the legal context.

Findings

Promising results in classifying illegal comments

Effective transfer learning observed on Kaggle dataset

Multi-task models enhance legal comment analysis

Abstract

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.