  Citations are generally analyzed using only quantitative measures while
excluding qualitative aspects such as sentiment and intent. However,
qualitative aspects provide deeper insights into the impact of a scientific
research artifact and make it possible to focus on relevant literature free
from bias associated with quantitative aspects. Therefore, it is possible to
rank and categorize papers based on their sentiment and intent. For this
purpose, larger citation sentiment datasets are required. However, from a time
and cost perspective, curating a large citation sentiment dataset is a
challenging task. Particularly, citation sentiment analysis suffers from both
data scarcity and tremendous costs for dataset annotation. To overcome the
bottleneck of data scarcity in the citation analysis domain we explore the
impact of out-domain data during training to enhance the model performance. Our
results emphasize the use of different scheduling methods based on the use
case. We empirically found that a model trained using sequential data
scheduling is more suitable for domain-specific usecases. Conversely, shuffled
data feeding achieves better performance on a cross-domain task. Based on our
findings, we propose an end-to-end trainable multi-task model that covers the
sentiment and intent analysis that utilizes out-domain datasets to overcome the
data scarcity.
