Exploring How Generative Adversarial Networks Learn Phonological
  Representations
Jingyi Chen Micha Elsner
This paper explores how Generative Adversarial Networks (GANs) learn
representations of phonological phenomena. We analyze how GANs encode
contrastive and non-contrastive nasality in French and English vowels by
applying the ciwGAN architecture (Begus 2021a). Begus claims that ciwGAN
encodes linguistically meaningful representations with categorical variables in
its latent space and manipulating the latent variables shows an almost one to
one corresponding control of the phonological features in ciwGAN's generated
outputs. However, our results show an interactive effect of latent variables on
the features in the generated outputs, which suggests the learned
representations in neural networks are different from the phonological
representations proposed by linguists. On the other hand, ciwGAN is able to
distinguish contrastive and noncontrastive features in English and French by
encoding them differently. Comparing the performance of GANs learning from
different languages results in a better understanding of what language specific
features contribute to developing language specific phonological
representations. We also discuss the role of training data frequencies in
phonological feature learning.