# RISC: Generating Realistic Synthetic Bilingual Insurance Contract

arXiv: https://arxiv.org/abs/2304.04212 · HTML: https://mlsanity.com/paper/2304.04212/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 58 references that are in the map.
- First posted on arXiv 9 Apr 2023.

## Builds on (most cited first, 20 of 20)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Modeling Tabular data using Conditional GAN](https://mlsanity.com/paper/1907.00503/) (2019)
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- [CUAD: An Expert-Annotated NLP Dataset for Legal Contract Review](https://mlsanity.com/paper/2103.06268/) (2021)
- [BillSum: A Corpus for Automatic Summarization of US Legislation](https://mlsanity.com/paper/1910.00523/) (2019)
- [A Survey on Data Augmentation for Text Classification](https://mlsanity.com/paper/2107.03158/) (2021)
- [An Empirical Survey on Long Document Summarization: Datasets, Models and Metrics](https://mlsanity.com/paper/2207.00939/) (2022)
- [GPT Takes the Bar Exam](https://mlsanity.com/paper/2212.14402/) (2022)
- [Multi-document Summarization via Deep Learning Techniques: A Survey](https://mlsanity.com/paper/2011.04843/) (2020)
- [Meta-learning for Few-shot Natural Language Processing: A Survey](https://mlsanity.com/paper/2007.09604/) (2020)
- [EUR-Lex-Sum: A Multi- and Cross-lingual Dataset for Long-form Summarization in the Legal Domain](https://mlsanity.com/paper/2210.13448/) (2022)
- [Natural Language Processing in the Legal Domain](https://mlsanity.com/paper/2302.12039/) (2023)
- [MAUD: An Expert-Annotated Legal NLP Dataset for Merger Agreement Understanding](https://mlsanity.com/paper/2301.00876/) (2023)
- [Data Augmentation in Natural Language Processing: A Novel Text Generation Approach for Long and Short Text Classifiers](https://mlsanity.com/paper/2103.14453/) (2021)
- [Understanding Points of Correspondence between Sentences for Abstractive Summarization](https://mlsanity.com/paper/2006.05621/) (2020)
- [Synthetic Data for Social Good](https://mlsanity.com/paper/1710.08874/) (2017)
- [A Survey on Legal Question Answering Systems](https://mlsanity.com/paper/2110.07333/) (2021)
- [XCoref: Cross-document Coreference Resolution in the Wild](https://mlsanity.com/paper/2109.05252/) (2021)
- [A Survey of Methods to Leverage Monolingual Data in Low-resource Neural Machine Translation](https://mlsanity.com/paper/1910.00373/) (2019)
- [Generating Intelligible Plumitifs Descriptions: Use Case Application with Ethical Considerations](https://mlsanity.com/paper/2011.12183/) (2020)

## Built on by (most cited first, 1 of 1)

- [Benchmarking Large Language Models for Quebec Insurance: From Closed-Book to Retrieval-Augmented Generation](https://mlsanity.com/paper/2603.07825/) (2026)

## Abstract (arXiv)

This paper presents RISC, an open-source Python package data generator (https://github.com/GRAAL-Research/risc). RISC generates look-alike automobile insurance contracts based on the Quebec regulatory insurance form in French and English. Insurance contracts are 90 to 100 pages long and use complex legal and insurance-specific vocabulary for a layperson. Hence, they are a much more complex class of documents than those in traditional NLP corpora. Therefore, we introduce RISCBAC, a Realistic Insurance Synthetic Bilingual Automobile Contract dataset based on the mandatory Quebec car insurance contract. The dataset comprises 10,000 French and English unannotated insurance contracts. RISCBAC enables NLP research for unsupervised automatic summarisation, question answering, text simplification, machine translation and more. Moreover, it can be further automatically annotated as a dataset for supervised tasks such as NER