  Generative adversarial nets (GANs) have been successfully applied to the
artificial generation of image data. In terms of text data, much has been done
on the artificial generation of natural language from a single corpus. We
consider multiple text corpora as the input data, for which there can be two
applications of GANs: (1) the creation of consistent cross-corpus word
embeddings given different word embeddings per corpus; (2) the generation of
robust bag-of-words document embeddings for each corpora. We demonstrate our
GAN models on real-world text data sets from different corpora, and show that
embeddings from both models lead to improvements in supervised learning
problems.
