2 research outputs found
Multitask Learning for Citation Purpose Classification
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.Comment: Second Workshop on Scholarly Document Processin