Text Attribute Control via Closed-Loop Disentanglement
Lei Sha Thomas Lukasiewicz
Changing an attribute of a text without changing the content usually requires
to first disentangle the text into irrelevant attributes and content
representations. After that, in the inference phase, the representation of one
attribute is tuned to a different value, expecting that the corresponding
attribute of the text can also be changed accordingly. The usual way of
disentanglement is to add some constraints on the latent space of an
encoder-decoder architecture, including adversarial-based constraints and
mutual-information-based constraints. However, the previous semi-supervised
processes of attribute change are usually not enough to guarantee the success
of attribute change and content preservation. In this paper, we propose a novel
approach to achieve a robust control of attributes while enhancing content
preservation. In this approach, we use a semi-supervised contrastive learning
method to encourage the disentanglement of attributes in latent spaces.
Differently from previous works, we re-disentangle the reconstructed sentence
and compare the re-disentangled latent space with the original latent space,
which makes a closed-loop disentanglement process. This also helps content
preservation. In addition, the contrastive learning method is also able to
replace the role of minimizing mutual information and adversarial training in
the disentanglement process, which alleviates the computation cost. We
conducted experiments on three text datasets, including the Yelp Service review
dataset, the Amazon Product review dataset, and the GoEmotions dataset. The
experimental results show the effectiveness of our model.