  Recent studies show that auto-encoder based approaches successfully perform
language generation, smooth sentence interpolation, and style transfer over
unseen attributes using unlabelled datasets in a zero-shot manner. The latent
space geometry of such models is organised well enough to perform on datasets
where the style is "coarse-grained" i.e. a small fraction of words alone in a
sentence are enough to determine the overall style label. A recent study uses a
discrete token-based perturbation approach to map "similar" sentences
("similar" defined by low Levenshtein distance/ high word overlap) close by in
latent space. This definition of "similarity" does not look into the underlying
nuances of the constituent words while mapping latent space neighbourhoods and
therefore fails to recognise sentences with different style-based semantics
while mapping latent neighbourhoods. We introduce EPAAEs (Embedding Perturbed
Adversarial AutoEncoders) which completes this perturbation model, by adding a
finely adjustable noise component on the continuous embeddings space. We
empirically show that this (a) produces a better organised latent space that
clusters stylistically similar sentences together, (b) performs best on a
diverse set of text style transfer tasks than similar denoising-inspired
baselines, and (c) is capable of fine-grained control of Style Transfer
strength. We also extend the text style transfer tasks to NLI datasets and show
that these more complex definitions of style are learned best by EPAAE. To the
best of our knowledge, extending style transfer to NLI tasks has not been
explored before.
