CitePrompt: Using Prompts to Identify Citation Intent in Scientific Papers
This addresses the problem of limited labeled data for citation intent classification, offering a novel approach for researchers and scholars, though it is incremental in applying prompt-based learning to this specific task.
The paper tackles citation intent classification in scientific papers by introducing CitePrompt, a prompt-based learning framework that achieves state-of-the-art results on the ACL-ARC dataset and significant improvements on SciCite, with zero-shot F1 scores of 53.86% improving to 66.99% in few-shot settings.
Citations in scientific papers not only help us trace the intellectual lineage but also are a useful indicator of the scientific significance of the work. Citation intents prove beneficial as they specify the role of the citation in a given context. In this paper, we present CitePrompt, a framework which uses the hitherto unexplored approach of prompt-based learning for citation intent classification. We argue that with the proper choice of the pretrained language model, the prompt template, and the prompt verbalizer, we can not only get results that are better than or comparable to those obtained with the state-of-the-art methods but also do it with much less exterior information about the scientific document. We report state-of-the-art results on the ACL-ARC dataset, and also show significant improvement on the SciCite dataset over all baseline models except one. As suitably large labelled datasets for citation intent classification can be quite hard to find, in a first, we propose the conversion of this task to the few-shot and zero-shot settings. For the ACL-ARC dataset, we report a 53.86% F1 score for the zero-shot setting, which improves to 63.61% and 66.99% for the 5-shot and 10-shot settings, respectively.