Modeling the Graphotactics of Low-Resource Languages Using Sequential GANs

TL;DR

This paper presents a GAN-based approach to generate graphotactically valid strings for low-resource languages, aiding morphological inflection modeling with minimal data.

Contribution

It introduces a novel sequential GAN model specifically designed to produce linguistically plausible data from very limited examples.

Findings

Successfully generated graphotactically compliant strings

Enhanced morphological inflection modeling for low-resource languages

Demonstrated effectiveness with only 100 example strings

Abstract

Generative Adversarial Networks (GANs) have been shown to aid in the creation of artificial data in situations where large amounts of real data are difficult to come by. This issue is especially salient in the computational linguistics space, where researchers are often tasked with modeling the complex morphologic and grammatical processes of low-resource languages. This paper will discuss the implementation and testing of a GAN that attempts to model and reproduce the graphotactics of a language using only 100 example strings. These artificial, yet graphotactically compliant, strings are meant to aid in modeling the morphological inflection of low-resource languages.