  We present an architecture that generates medical texts while learning an
informative, continuous representation with discriminative features. During
training the input to the system is a dataset of captions for medical X-Rays.
The acquired continuous representations are of particular interest for use in
many machine learning techniques where the discrete and high-dimensional nature
of textual input is an obstacle. We use an Adversarially Regularized
Autoencoder to create realistic text in both an unconditional and conditional
setting. We show that this technique is applicable to medical texts which often
contain syntactic and domain-specific shorthands. A quantitative evaluation
shows that we achieve a lower model perplexity than a traditional LSTM
generator.
