Modified Hierarchical-Attention Network model for legal judgment predictions

The impact of Artificial Intelligence in Legal Research has reached a high level in simulating human thought processes. Case Pendency is a long-lasting problem in many countries. The judicial system has to be more competent and reliable to provide justice on time for any developing country. Litigants and attorneys devote more time and effort to trial case preparation in the courtroom. The task of decision prediction is to automatically forecast the type of charge, law article, and term of punishment. Most of the earlier works for verdict prediction focused to work on civil law jurisdictions. Some of the challenges in the task are case facts are highly unstructured lengthy documents with a lack of annotations and mainly used machine learning techniques. While most research works ignore the information loss at the encoding stage, our proposed MHAN overcomes the above issue and long-range dependency problem using the attention model over hierarchical encoders with three tiers namely Sentence encoder, word encoder, and character encoder. To avoid information loss, a brand-new judgment prediction framework called MHAN is developed in this study effort. It is built on a modified Hierarchical-Attention network and a specially designed domain-specific word embedding model. Additionally, it emphasizes the feature extraction phase by joining features obtained using MHAN with an improved cosine similarity feature. Finally, a hybrid Self Improved RNN is employed to provide the projected results. Furthermore, the proposed model is trained on 10 types of real-time criminal cases from the Madras High Court of India and Supreme Court of India. It has outperformed prior methods in terms of verdict prediction. By applying different variations of the deep learning model and ablation tests, the proposed model achieves consistent results over baseline models.