mlsanityData to 7 Oct 2026

Modeling the Graphotactics of Low-Resource Languages Using Sequential GANs

arXiv 2210.14409 ↗First posted 26 Oct 2022cs.CL cs.LGcited by 0

Not on the map. Why?

Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 12 of its 16 references that are in the map. First posted on arXiv 26 Oct 2022.

Builds on

12 of 16 references.

References · 12

  1. 2018A Style-Based Generator Architecture for Generative Adversarial Networkscited by 5,986
  2. 2016Image-to-Image Translation with Conditional Adversarial Networkscited by 4,510
  3. 2017Wasserstein GANcited by 635
  4. 2016SeqGAN: Sequence Generative Adversarial Nets with Policy Gradientcited by 375
  5. 2017Real-valued (Medical) Time Series Generation with Recurrent Conditional GANscited by 329
  6. 2017Learning to Discover Cross-Domain Relations with Generative Adversarial Networkscited by 278
  7. 2017Data Augmentation Generative Adversarial Networkscited by 278
  8. 2018Medical Image Synthesis for Data Augmentation and Anonymization using Generative Adversarial Networkscited by 146
  9. 2019The SIGMORPHON 2019 Shared Task: Morphological Analysis in Context and Cross-Lingual Transfer for Inflectioncited by 43
  10. 2017Automatic Brain Tumor Segmentation using Cascaded Anisotropic Convolutional Neural Networkscited by 33
  11. 2019Pushing the Limits of Low-Resource Morphological Inflectioncited by 29
  12. 2019Data augmentation for low resource sentiment analysis using generative adversarial networkscited by 7

Built on by

Not cited yet.