  Bill writing is a critical element of representative democracy. However, it
is often overlooked that most legislative bills are derived, or even directly
copied, from other bills. Despite the significance of bill-to-bill linkages for
understanding the legislative process, existing approaches fail to address
semantic similarities across bills, let alone reordering or paraphrasing which
are prevalent in legal document writing. In this paper, we overcome these
limitations by proposing a 5-class classification task that closely reflects
the nature of the bill generation process. In doing so, we construct a
human-labeled dataset of 4,721 bill-to-bill relationships at the
subsection-level and release this annotated dataset to the research community.
To augment the dataset, we generate synthetic data with varying degrees of
similarity, mimicking the complex bill writing process. We use BERT variants
and apply multi-stage training, sequentially fine-tuning our models with
synthetic and human-labeled datasets. We find that the predictive performance
significantly improves when training with both human-labeled and synthetic
data. Finally, we apply our trained model to infer section- and bill-level
similarities. Our analysis shows that the proposed methodology successfully
captures the similarities across legal documents at various levels of
aggregation.
