Stop Illegal Comments: A Multi-Task Deep Learning Approach

Ahmed Elnaggar, Bernhard Waltl, Ingo Glaser, Jörg Landthaler, Elena Scepankova, F. Matthes
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引用次数: 14

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.
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停止非法评论:一种多任务深度学习方法
由于深度学习方法需要大量的标记数据,因此深度学习方法往往难以应用于法律领域。深度学习社区最近的一个新趋势是多任务模型的应用,它使单个深度神经网络能够同时执行多个任务,例如分类和翻译任务。这些强大的新模型能够在不同的任务或训练集之间转移知识,因此可以为许多深度学习应用开辟法律领域。在本文中,我们研究了这种多任务模型在公开可用的Kaggle有毒评论数据集上的分类任务上的迁移学习能力,用于对非法评论进行分类,我们可以报告有希望的结果。
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