Detecting Relevant Differences Between Similar Legal Texts

Abstract

Given two similar legal texts, is it useful to be able to focus only on the parts that contain relevant differences. However, because of variation in linguistic structure and terminology, it is not easy to identify true semantic differences. An accurate difference detection model between similar legal texts is therefore in demand, in order to increase the efficiency of legal research and document analysis. In this paper, we automatically label a training dataset of sentence pairs using an existing legal resource of international investment treaties that were already manually annotated with metadata. Then we propose models based on state-of-the-art deep learning techniques for the novel task of detecting relevant differences. In addition to providing solutions for this task, we include models for automatically producing metadata for the treaties that do not have it.