  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
