  Text autoencoders are often used for unsupervised conditional text generation
by applying mappings in the latent space to change attributes to the desired
values. Recently, Mai et al. (2020) proposed Emb2Emb, a method to learn these
mappings in the embedding space of an autoencoder. However, their method is
restricted to autoencoders with a single-vector embedding, which limits how
much information can be retained. We address this issue by extending their
method to Bag-of-Vectors Autoencoders (BoV-AEs), which encode the text into a
variable-size bag of vectors that grows with the size of the text, as in
attention-based models. This allows to encode and reconstruct much longer texts
than standard autoencoders. Analogous to conventional autoencoders, we propose
regularization techniques that facilitate learning meaningful operations in the
latent space. Finally, we adapt Emb2Emb for a training scheme that learns to
map an input bag to an output bag, including a novel loss function and neural
architecture. Our empirical evaluations on unsupervised sentiment transfer show
that our method performs substantially better than a standard autoencoder.
