Explainable Graphs for Legal Judgement Prediction


  1. Ambreesh Parthasarathy (Post Baccalaureate Fellow)
  2. Bavish Kulur (Post Baccalaureate Fellow)
  3. Shubham Kashyapi (Post Baccalaureate Fellow)
  4. Yogesh Tripathi (Post Baccalaureate Fellow)
  5. Dr. Gokul Krishnan (Research Scientist)
  6. Dr. Balaraman Ravindran (Principal Investigator)


Problem Statement- Given the facts / description of a case, predict the legal articles violated in the given case. [Dataset- ECtHR (European Court of Human Rights)]

Ongoing Work:

  1. Heterogeneous Graphs Existing LJP techniques use a single embedding for large case documents [Chalkidis et al. (2019)]. Compute fine-grained embeddings with different kinds of nodes- facts, cases, articles

  2. Summarization Query LLMs to summarize an entire case or parts of it. Could learn better case embeddings and improve the performance of the GNN models.


  1. Cui, J., Shen, X., & Wen, S. (2023). A survey on legal judgment prediction: Datasets, metrics, models and challenges. IEEE Access.
  2. Chalkidis, I., Fergadiotis, M., Malakasiotis, P., Aletras, N., & Androutsopoulos, I. (2020). LEGAL-BERT: The muppets straight out of law school. arXiv preprint arXiv:2010.02559.
  3. Veličković, P., Cucurull, G., Casanova, A., Romero, A., Lio, P., & Bengio, Y. (2017). Graph attention networks. arXiv preprint arXiv:1710.10903.
  4. Chalkidis, I., Androutsopoulos, I., & Aletras, N. (2019). Neural legal judgment prediction in English. arXiv preprint arXiv:1906.02059.
  5. Lewis, P., Perez, E., Piktus, A., Petroni, F., Karpukhin, V., Goyal, N., ... & Kiela, D. (2020). Retrieval-augmented generation for knowledge-intensive nlp tasks. Advances in Neural Information Processing Systems, 33, 9459-9474.
  6. Chalkidis, I., Fergadiotis, M., Tsarapatsanis, D., Aletras, N., Androutsopoulos, I., & Malakasiotis, P. (2021). Paragraph-level rationale extraction through regularization: A case study on European court of human rights cases. arXiv preprint arXiv:2103.13084.