Few-Shot Learning for Chinese Legal Controversial Issues Classification

Abstract

Chinese courts organize debates surrounding controversial issues along with the gradual formation of the new procedural system. With the progress of China's judicial reform, more than 80 million judgement documents have been made public online. Similar controversial issues identified in and among the massive public judgment documents are of significant value for judges in their trial work. Hence, homogeneous controversial issues classification becomes the basis for similar cases retrieval. However, controversial issues follow the power-law distribution, not all of them are within the labels provided by manual annotation and their categories cannot be exhausted. In order to generalize those unfamiliar categories without necessitating extensive retraining, we propose a controversial issues classification algorithm based on few-shot learning. Two few-shot learning algorithms are proposed for our controversial issues problem, Relation Network and Induction Network, respectively. With only a handful of given instances, both of them have shown excellent results on the two datasets, which proves their effectiveness in adapting to accommodating new categories not seen in training. The proposed method provides trial assistance for judges, promotes the dissemination of experience and improves fairness of adjudication.

Keywords

• Controversial issues
• few-shot learning
• text classification
• power-law
• BERT