  Citation field learning is to segment a citation string into fields of
interest such as author, title, and venue. Extracting such fields from
citations is crucial for citation indexing, researcher profile analysis, etc.
User-generated resources like academic homepages and Curriculum Vitae, provide
rich citation field information. However, extracting fields from these
resources is challenging due to inconsistent citation styles, incomplete
sentence syntax, and insufficient training data. To address these challenges,
we propose a novel algorithm, CIFAL (citation field learning by anchor
learning), to boost the citation field learning performance. CIFAL leverages
the anchor learning, which is model-agnostic for any Pre-trained Language
Model, to help capture citation patterns from the data of different citation
styles. The experiments demonstrate that CIFAL outperforms state-of-the-art
methods in citation field learning, achieving a 2.68% improvement in
field-level F1-scores. Extensive analysis of the results further confirms the
effectiveness of CIFAL quantitatively and qualitatively.
