Legal Feature Enhanced Semantic Matching Network for Similar Case Matching
Zhilong Hong, Qifei Zhou, Rong Zhang, Weiping Li, Tong Mo
Abstract
Similar case matching (SCM) aims to determine whether legal case documents are similar or not. In fact, SCM is an extension of the semantic text matching. Various deep learning models are proposed to solve the semantic text matching problems. However, the main difference between the case documents may be subtle, and the length of documents can be quite long. Moreover, the case documents are written in structural format and contain plenty of legal terms. To address these challenges, we propose a novel model in this paper. Accordingly, the legal feature vector is introduced into the semantic text matching model, and BERT is adopted as the encoding layer to capture long-range dependencies in the case documents. We conduct several experiments to evaluate the performance of our proposed model. The results show that our model outperforms other existing methods on the public dataset CAIL2019-SCM.