  Continuous path keyboard input has higher inherent ambiguity than standard
tapping, because the path trace may exhibit not only local
overshoots/undershoots (as in tapping) but also, depending on the user,
substantial mid-path excursions. Deploying a robust solution thus requires a
large amount of high-quality training data, which is difficult to
collect/annotate. In this work, we address this challenge by using GANs to
augment our training corpus with user-realistic synthetic data. Experiments
show that, even though GAN-generated data does not capture all the
characteristics of real user data, it still provides a substantial boost in
accuracy at a 5:1 GAN-to-real ratio. GANs therefore inject more robustness in
the model through greatly increased word coverage and path diversity.
