Graph-Enhanced Document Representation for Court Case Retrieval

To reach informed decisions, legal domain experts in Civil Law systems need to have knowledge not only about legal paragraphs, but also about related court cases. However, court case retrieval is challenging due to the domain-specific language and large document sizes. While modern transformer models such as BERT create dense text representations suitable for efficient retrieval in many domains, without domain specific adaptions they are outperformed by established lexical retrieval models in the legal domain. Although citations of court cases and codified law play an important role in the domain, there has been little research on utilizing a combination of text representations and citation graph data for court case retrieval. In other domains, attempts have been made to combine these two with methods such as concatenating graph embeddings to text embeddings. In the PhD research project, domain-specific challenges of legal retrieval systems will be tackled. To help with this task, a dataset of Austrian court cases, their document labels as well as their citations of other court cases and codified law on a document and paragraph level will be created and made public. Experiments in this project will include various ways of enhancing transformer-based text representations methods with citation graph data, such as graph based transformer re-training or graph embeddings.