  Disentangled latent spaces usually have better semantic separability and
geometrical properties, which leads to better interpretability and more
controllable data generation. While this has been well investigated in Computer
Vision, in tasks such as image disentanglement, in the NLP domain sentence
disentanglement is still comparatively under-investigated. Most previous work
have concentrated on disentangling task-specific generative factors, such as
sentiment, within the context of style transfer. In this work, we focus on a
more general form of sentence disentanglement, targeting the localised
modification and control of more general sentence semantic features. To achieve
this, we contribute to a novel notion of sentence semantic disentanglement and
introduce a flow-based invertible neural network (INN) mechanism integrated
with a transformer-based language Autoencoder (AE) in order to deliver latent
spaces with better separability properties. Experimental results demonstrate
that the model can conform the distributed latent space into a better
semantically disentangled sentence space, leading to improved language
interpretability and controlled generation when compared to the recent
state-of-the-art language VAE models.
