  Unsupervised domain adaptation (UDA) is the task of modifying a statistical
model trained on labeled data from a source domain to achieve better
performance on data from a target domain, with access to only unlabeled data in
the target domain. Existing state-of-the-art UDA approaches use neural networks
to learn representations that can predict the values of subset of important
features called "pivot features." In this work, we show that it is possible to
improve on these methods by jointly training the representation learner with
the task learner, and examine the importance of existing pivot selection
methods.
