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Found 9 papers, 7 papers with code
Quebec Automobile Insurance Question-Answering With Retrieval-Augmented Generation
Large Language Models (LLMs) perform outstandingly in various downstream tasks, and the use of the Retrieval-Augmented Generation (RAG) architecture has been shown to improve performance for legal question answering (Nuruzzaman and Hussain, 2020; Louis et al., 2024).
Deepparse : An Extendable, and Fine-Tunable State-Of-The-Art Library for Parsing Multinational Street Addresses
Segmenting an address into meaningful components, also known as address parsing, is an essential step in many applications from record linkage to geocoding and package delivery.
RISC: Generating Realistic Synthetic Bilingual Insurance Contract
RISC generates look-alike automobile insurance contracts based on the Quebec regulatory insurance form in French and English.
Quantifying French Document Complexity
Measuring a document's complexity level is an open challenge, particularly when one is working on a diverse corpus of documents rather than comparing several documents on a similar topic or working on a language other than English.
"FIJO": a French Insurance Soft Skill Detection Dataset
Understanding the evolution of job requirements is becoming more important for workers, companies and public organizations to follow the fast transformation of the employment market.
Multinational Address Parsing: A Zero-Shot Evaluation
While these models yield notable results, previous work on neural networks has only focused on parsing addresses from a single source country.
Generating Intelligible Plumitifs Descriptions: Use Case Application with Ethical Considerations
Plumitifs (dockets) were initially a tool for law clerks.
Leveraging Subword Embeddings for Multinational Address Parsing
We propose an approach in which we employ subword embeddings and a Recurrent Neural Network architecture to build a single model capable of learning to parse addresses from multiple countries at the same time while taking into account the difference in languages and address formatting systems.
A Robust Self-Learning Method for Fully Unsupervised Cross-Lingual Mappings of Word Embeddings: Making the Method Robustly Reproducible as Well
In this paper, we reproduce the experiments of Artetxe et al. (2018b) regarding the robust self-learning method for fully unsupervised cross-lingual mappings of word embeddings.
