  Text style transfer is usually performed using attributes that can take a
handful of discrete values (e.g., positive to negative reviews). In this work,
we introduce an architecture that can leverage pre-trained consistent
continuous distributed style representations and use them to transfer to an
attribute unseen during training, without requiring any re-tuning of the style
transfer model. We demonstrate the method by training an architecture to
transfer text conveying one sentiment to another sentiment, using a
fine-grained set of over 20 sentiment labels rather than the binary
positive/negative often used in style transfer. Our experiments show that this
model can then rewrite text to match a target sentiment that was unseen during
training.
