Neural Multi-Task Learning for Citation Function and Provenance
This work addresses citation analysis for researchers and scholars, but it is incremental as it builds on existing methods with modest improvements.
The paper tackled the tasks of citation function and provenance prediction by building a neural multi-task learning model, showing that joint training yields synergistic gains and improves state-of-the-art performance by up to 2% with statistical significance.
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.