Detection of the citation-worthiness using BERT and its error analysis

Abstract

Due to the explosive increase in academic papers and the need to cite appropriate references in writing papers, research on paper writing support has been conducted. In this paper, we focus on the citation-worthiness task of detecting which sentences need a citation. First, we developed a detection model based on transfer learning of the large-scale language model BERT that uses the existing Citation Worthiness dataset, and we obtained a significant performance improvement over the conventional method using convolutional neural networks. Next, we developed a detection model for each citation function using the Citation Function dataset. The evaluation results showed that the detection performance of citation-worthiness varies by citation functions. The citation functions like ``Background,'' expressed in various expressions, tended to lower performance than those like ``Compare & Contrast,'' expressed in limited surface forms. The error analysis indicated the necessity of a detection model that allows for the citation context.