IACN: Interactive attention capsule network for similar case matching

Abstract

The similar case matching task aims to detect which two cases are more similar for a given triplet. It plays a significant role in the legal industry and thus has gained much attention. Due to the rapid development of natural language processing technology, various deep learning techniques have been applied to similar case matching task and obtained attractive performance. Most existing researches usually focus on encoding legal documents into a continuous vector. However, a unified vector is difficult to model multiple elements of the case. In the real world, cases contain numerous elements, which are the basis for legal practitioners to judge the similarity among cases. Legal experts usually focus on whether the two cases have similar legal elements. It makes this task especially challenging. In this paper, we propose a novel model, namely Interactive Attention Capsule Network (dubbed as IACN). It attempts to simulate the process of judgment by legal experts, which captures fine-grained elements similarity to make an interpretable judgment. In other words, the IACN judges the similarity of the case pairs based on the legal elements. The more similar legal elements of a case pair, the higher the degree of similarity of the case pair. In addition, we devise an interactive dynamic routing mechanism, which can better learn the interactive representation of legal elements among cases than the vanilla dynamic routing. We conduct extensive experiments based on a real-world dataset. The experimental results consistently demonstrate the superiorities and competitiveness of our proposed model.