  Citation function and provenance are two cornerstone tasks in citation
analysis. Given a citation, the former task determines its rhetorical role,
while the latter locates the text in the cited paper that contains the relevant
cited information. We hypothesize that these two tasks are synergistically
related, and build a model that validates this claim. For both tasks, we show
that a single-layer convolutional neural network (CNN) outperforms existing
state-of-the-art baselines. More importantly, we show that the two tasks are
indeed synergistic: by jointly training both of the tasks in a multi-task
learning setup, we demonstrate additional performance gains. Altogether, our
models improve the current state-of-the-arts up to 2\%, with statistical
significance for both citation function and provenance prediction tasks.
