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

arXiv: https://arxiv.org/abs/2211.15424 · HTML: https://mlsanity.com/paper/2211.15424/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 26 references that are in the map.
- First posted on arXiv 15 Nov 2022.

## Builds on (most cited first, 16 of 16)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Longformer: The Long-Document Transformer](https://mlsanity.com/paper/2004.05150/) (2020)
- [ALBERT: A Lite BERT for Self-supervised Learning of Language Representations](https://mlsanity.com/paper/1909.11942/) (2019)
- [Big Bird: Transformers for Longer Sequences](https://mlsanity.com/paper/2007.14062/) (2020)
- [Reformer: The Efficient Transformer](https://mlsanity.com/paper/2001.04451/) (2020)
- [Convolutional Neural Networks for Sentence Classification](https://mlsanity.com/paper/1408.5882/) (2014)
- [Datasets: A Community Library for Natural Language Processing](https://mlsanity.com/paper/2109.02846/) (2021)
- [CUAD: An Expert-Annotated NLP Dataset for Legal Contract Review](https://mlsanity.com/paper/2103.06268/) (2021)
- [Neural Legal Judgment Prediction in English](https://mlsanity.com/paper/1906.02059/) (2019)
- [When Does Pretraining Help? Assessing Self-Supervised Learning for Law and the CaseHOLD Dataset](https://mlsanity.com/paper/2104.08671/) (2021)
- [CAIL2018: A Large-Scale Legal Dataset for Judgment Prediction](https://mlsanity.com/paper/1807.02478/) (2018)
- [Interpretable Charge Predictions for Criminal Cases: Learning to Generate Court Views from Fact Descriptions](https://mlsanity.com/paper/1802.08504/) (2018)
- [A Dataset of German Legal Documents for Named Entity Recognition](https://mlsanity.com/paper/2003.13016/) (2020)
- [Obligation and Prohibition Extraction Using Hierarchical RNNs](https://mlsanity.com/paper/1805.03871/) (2018)

## Built on by (most cited first, 2 of 2)

- [Framework of Voting Prediction of Parliament Members](https://mlsanity.com/paper/2505.12535/) (2025)
- [RTI-Bench: A Structured Dataset for Indian Right-to-Information Decision Analysis](https://mlsanity.com/paper/2605.16843/) (2026)

## Abstract (arXiv)

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