Convolutional Neural Networks can achieve binary bail judgement classification (2023)

Abstract: There is an evident lack of implementation of Machine Learning (ML) in the legal domain in India, and any research that does take place in this domain is usually based on data from the higher courts of law and works with English data. The lower courts and data from the different regional languages of India are often overlooked. In this paper, we deploy a Convolutional Neural Network (CNN) architecture on a corpus of Hindi legal documents. We perform a bail Prediction task with the help of a CNN model and achieve an overall accuracy of 93% which is an improvement on the benchmark accuracy, set by Kapoor et al. (2022), albeit in data from 20 districts of the Indian state of Uttar Pradesh.

## data the hldc corpus was created by collecting data from the e-courts website @xcite . this corpus contains judgments from district courts of 71 districts in the state of uttar pradesh for a duration of 2 years from may 01, 2019, to may 01, 2021. we have used this corpus for training and testing our cnn. an obvious issue that arose post-pre-processing was the imbalance in several "granted" and "dismissed" cases for each district. figure 1 shows the district-wise case distribution for the 20 selected districts. we can see that in nearly all the districts (excepting bhadohi, shravasti, basti, and hathras) the number of "granted" case data exceeds the number of "dismissed" case data and often by quite a lot. it is only for deoria and mirzapur that we see a nearly equal distribution of case data in terms of decision. ## experiments and results it has been demonstrated that convolution neural networks (cnns) are effective tools for image analytics, particularly when used in conjunction with transfer learning to extract features @xcite @xcite . in the past few years, cnn has been modified for text classification and has shown effectiveness in tasks like classification @xcite where we anticipate finding strong local cues about class membership like a few salient lines or phrases @xcite . convolution layers in a cnn for text classification use max pooling to condense or summarize the features that are extracted from the convolution and one-dimensional convolution with a small size kernel to extract features. ultimately, the fully connected layer creates predictions by fitting the characteristics obtained from activations to the training set. we have also used this architecture in our paper for the purpose of our mission. figure 2 displays the comprehensive architecture. word representations are the neural network's inputs, regardless of the neural network architecture that is selected. word embedding is frequently used in neural networks for text classification to carry out the task. the process of word embedding involves treating every word as a vector in a low-dimensional space. any text classification task involving deep learning must include tokenization; for our research, we have utilized hugging-face's "bert-basemultilingual-uncased" as our tokenizer @xcite . which contains 110,000 words in its vocabulary. the embedding layer receives these tokens that were produced during the tokenization process. ## conclusion in india, most of the research in the legal domain involving machine learning methods is performed on english language data from the higher courts of law @xcite @xcite . we have shown that ml can also be applied to data from the lower courts, and the district courts, in the regional languages to yield successful results. in this paper, we report on the use of convolutional layers to achieve a binary classification of legal data in hindi. we apply the cnn architecture to the dataset that we extract from the hindi legal documents corpus (hldc) via pre-processing. we then use this model to perform the binary classification task of bail prediction. using the cnn model, we improve on the prediction task's benchmark accuracy set by @xcite .