Neural Multi-task Learning for Citation Function and Provenance

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 tasks using multi-task learning, we demonstrate additional performance gains.