DeepParliament: A Legal domain Benchmark & Dataset for Parliament Bills Prediction

TL;DR

DeepParliament introduces a comprehensive legal dataset and benchmark for parliament bill classification, enabling improved prediction models to assist legislative decision-making and streamline parliamentary processes.

Contribution

This work presents the first parliament bill prediction benchmark dataset and evaluates various models, including pretrained ones, for bill status classification tasks.

Findings

Pretrained models outperform traditional RNNs in bill classification.

The dataset covers bills from 1986 to present with rich metadata.

Two new benchmarks: Binary and Multi-Class Bill Status classification.

Abstract

This paper introduces DeepParliament, a legal domain Benchmark Dataset that gathers bill documents and metadata and performs various bill status classification tasks. The proposed dataset text covers a broad range of bills from 1986 to the present and contains richer information on parliament bill content. Data collection, detailed statistics and analyses are provided in the paper. Moreover, we experimented with different types of models ranging from RNN to pretrained and reported the results. We are proposing two new benchmarks: Binary and Multi-Class Bill Status classification. Models developed for bill documents and relevant supportive tasks may assist Members of Parliament (MPs), presidents, and other legal practitioners. It will help review or prioritise bills, thus speeding up the billing process, improving the quality of decisions and reducing the time consumption in both houses. Considering that the foundation of the country's democracy is Parliament and state legislatures, we anticipate that our research will be an essential addition to the Legal NLP community. This work will be the first to present a Parliament bill prediction task. In order to improve the accessibility of legal AI resources and promote reproducibility, we have made our code and dataset publicly accessible at github.com/monk1337/DeepParliament