Synthetic Data Generation with Python

RISC: Generating Realistic Synthetic Bilingual Insurance Contract

“…However, creating a synthetic dataset is not a new challenge. Research in many areas, such as finance, healthcare and computer vision, use synthetic datasets [21]. Synthetic data generation is usually categorized into two distinct categories: process-driven methods and data-driven methods.…”

“…Process-driven methods generate synthetic data from mathematical models of an underlying physical process; for example, numerical simulations using Monte Carlo. Data-driven methods generate synthetic data from generative models that have been trained on real data [21]. Most recent approaches are data-driven and rely on generative methods using generative adversarial networks (GAN) [21].…”

“…Data-driven methods generate synthetic data from generative models that have been trained on real data [21]. Most recent approaches are data-driven and rely on generative methods using generative adversarial networks (GAN) [21]. GANs are deep neural networks that produce two jointly-trained networks; one generates synthetic data intended to be as similar as possible to the training data, and one tries to discriminate the synthetic data from true training data.…”

“…GANs are deep neural networks that produce two jointly-trained networks; one generates synthetic data intended to be as similar as possible to the training data, and one tries to discriminate the synthetic data from true training data. They have proven to be very good at learning high-dimensional, continuous data such as images [21]. However, GAN data generators (or any data-driven approach) usually generate images, numerical values and short texts (i.e.…”