  Text autoencoders are commonly used for conditional generation tasks such as
style transfer. We propose methods which are plug and play, where any
pretrained autoencoder can be used, and only require learning a mapping within
the autoencoder's embedding space, training embedding-to-embedding (Emb2Emb).
This reduces the need for labeled training data for the task and makes the
training procedure more efficient. Crucial to the success of this method is a
loss term for keeping the mapped embedding on the manifold of the autoencoder
and a mapping which is trained to navigate the manifold by learning offset
vectors. Evaluations on style transfer tasks both with and without
sequence-to-sequence supervision show that our method performs better than or
comparable to strong baselines while being up to four times faster.

Computation and Language;Artificial Intelligence