Multitask Learning for Citation Purpose Classification
This work addresses a domain-specific problem for scientific literature analysis, but it appears incremental as it builds on existing methods for a competition task.
The authors tackled citation purpose classification in the 2021 3C Shared Task by developing a multi-task model combining linguistic features, TF-IDF, and an LSTM-with-attention approach, but no concrete performance numbers are provided in the abstract.
We present our entry into the 2021 3C Shared Task Citation Context Classification based on Purpose competition. The goal of the competition is to classify a citation in a scientific article based on its purpose. This task is important because it could potentially lead to more comprehensive ways of summarizing the purpose and uses of scientific articles, but it is also difficult, mainly due to the limited amount of available training data in which the purposes of each citation have been hand-labeled, along with the subjectivity of these labels. Our entry in the competition is a multi-task model that combines multiple modules designed to handle the problem from different perspectives, including hand-generated linguistic features, TF-IDF features, and an LSTM-with-attention model. We also provide an ablation study and feature analysis whose insights could lead to future work.