  We study textual autocomplete---the task of predicting a full sentence from a
partial sentence---as a human-machine communication game. Specifically, we
consider three competing goals for effective communication: use as few tokens
as possible (efficiency), transmit sentences faithfully (accuracy), and be
learnable to humans (interpretability). We propose an unsupervised approach
which tackles all three desiderata by constraining the communication scheme to
keywords extracted from a source sentence for interpretability and optimizing
the efficiency-accuracy tradeoff. Our experiments show that this approach
results in an autocomplete system that is 52% more accurate at a given
efficiency level compared to baselines, is robust to user variations, and saves
time by nearly 50% compared to typing full sentences.
